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Banana fusarium wilt has a significant adverse effect on the banana industry. It is worth noting that fusarium oxysporum f.sp. cubense 4(Foc4) is one of the most devastating and widespread pathogens of banana fusarium wilt in the world. It is a catastrophic threat to the global banana industry. At present, there is still no technology that can cure bananas infected with banana fusarium wilt. Therefore, it is extremely important for banana planting industry to detect banana fusarium wilt in time and do a good job in prevention and control. In order to realize the early accurate detection of banana fusarium wilt race 4, this study proposed a detection method of double probe paper-based sensor based on colloidal gold, which increased the use of signal enhancement probes to increase the binding amount of colloidal gold, thereby reducing the detection limit. Two kinds of colloidal gold particles with different particle sizes were combined with the corresponding detection probes and signal enhancement probes to form a double gold label probe system. The large particle size colloidal gold was used with the detection probe, while the small particle size colloidal gold was paired with the signal enhancement probe. The sample being tested was mixed with this double gold-labeled probe system. The Foc4 target sequence in the sample would be paired with the double gold-labeled probe to form a ' gold-labeled probe-target sequence-T-line probe ' complex, which continued to migrate to the water-absorbing pad. The complex was captured on the test area of the sensor. Within 10 minutes, the test paper would generate a visible target product that could be observed by the naked eye at both the test line and the quality control line areas, thus achieving qualitative detection. For quantitative detection of Foc4, the light intensity of the test strip would be further analyzed using software. The light intensity peak area was obtained and substituted into the standard curve to obtain the concentration of Foc4 detected and realize quantitative detection. Experimental results demonstrate that the detection limit of the dual-probe paper-based sensor is 0.001 nmol/L, which is 100 times of that of the traditional paper-based sensor, and the detection sensitivity is improved. The sensitivity test results demonstrated a strong linear relationship between the concentration of Foc4 and the peak area of the test line light intensity within the concentration range of 0.001-1 000.000 nmol/L. This finding suggests that the sensor can be effectively employed for quantitative detection. To assess specificity, a high concentration of non-complementary probe was utilized as the target sequence for detection and compared with the detection results of samples containing Foc4. The experiment revealed the absence of a red band at the test line when the test paper was detected by the noncomplementary sequence. Importantly, this observation indicated that the presence of the non-complementary sequence had minimal impact on the detection effect, thus affirming the sensor's excellent specificity. Finally, the paper-based sensor was used to detect Foc4 in banana leaves. The average recovery rate was 77.6%-102.3%, and the relative standard deviation was 7.4%-7.7%, indicating that it can be used for on-site detection of Foc4. The detection technology possesses the characteristics of being low-cost and easy to operate. In comparison to traditional detection methods, it enables timely, rapid and accurately determination of the presence of Foc4. Compared with the existing molecular detection technology, the detection can be completed without the need of expensive equipment or specialized personnel. This feature makes it highly suitable for widespread use. The dual-probe paper-based sensor realizes the combination of nucleic acid lateral chromatography detection technology and banana fusarium wilt detection. Its potential for widespread adoption in the early disease warning system of the banana industry is substantial. Furthermore, this technology can be combined with traditional polymerase chain reaction (PCR) and other techniques to facilitate on-site detection of Foc4. [ABSTRACT FROM AUTHOR]

A high-precision mechanized seeding can greatly contribute to the vegetable production at present. In this study, a spotting glue-paper tape precision seeder was designed for the small seed vegetables, particularly combining the seed rope and printing seeding. Three components were also divided in the seeder, including the peristaltic pump glue, ratchet spotting glue, and seed metering device. Among them, the glue was a mixture of pregelatinized starch and water. Specifically, the glue volume and viscosity were the main factors affecting the number of adhered seeds. The glue point volume depended mainly on the rotation angle of the peristaltic pump during the movement. A pretest of seed adhesion was carried out to preliminarily determine the mass ratio of pregelatinized starch to water (less than 1:5), representing the glue viscosity. Furthermore, the frequency of pump and dispensing were analyzed to clarify the relationship between the peristaltic pump and ratchet speed, where the number of ratchet teeth was determined to be 4. As such, the matching was realized between the peristaltic pump and ratchet spotting glue. The ratchet profile was also designed, according to the spotting glue motion. A theoretical analysis was made on the movement of seeds in the seed guiding tube. It was found that the aggregation effect of seeds on the paper tape relied on the length and cross-sectional angle of the seed guiding tube, as well as the rotation speed of the socket wheel. The seed guiding tube was then optimized for the optimal structural parameters. A Box_Behnken test was also carried out, where the length and cross-sectional angle of the seed guiding tube, while the rotation speed of the socket wheel was taken as the test factors, whereas, the percentage of seeds in the effective area on the paper tape was taken as the test index. The results showed that the percentage of seeds in the effective area on the paper tape was 57.23%, when the length of the seed guiding tube was 56 mm, the section angle was 35°, and the rotation speed of the seed metering wheel was 30 r/min. An adhesion test was also performed on the Shanghai Qing 605 seeds with a diameter of 1.5-2.0 mm. The test factors were then selected as the roller angle of the peristaltic pump and the mass ratio of pregelatinized starch to water. An optimal combination of parameters was achieved, including the size of the glue point, and the mass ratio of pregelatinized starch to water. Once the roller rotation angle was 10° and the mass ratio of pre-gelatinized starch to water was 1:6, the single seed rate of glue point adhesion to seeds reached 97.4%. The adaptability test was also carried out on the whole machine on the working performance of different varieties of small seed vegetables. It was found that the single seed rates of Shanghai Qing 605 and Siji Chinese cabbage seeds were greater than 95% under the working speed of 0.5-1.5 m/s, whereas, the coefficient of variation of plant spacing was less than 10%, fully meeting the requirements of precision sowing indicators that specified in the standard NY/T1143-2006. Therefore, the single seed rate and the working efficiency of the improved seeder increased by 3.53%, and 50% than before. [ABSTRACT FROM AUTHOR]

This study aims to improve the utilization rate of abandoned corn bracts, while decreasing the production cost of the bag of fruit cultivating in sustainable agriculture. Five representative bags of fruit cultivating were collected in the market as the controlled samples, including the plastic film bag A, special bag B for grape, one-, two- and three-layered bag C, D, and E. The basic structural characteristics of controlled samples were firstly analyzed. Then, alkali cooking was used to degum the corn bracts for the extraction of fibers. Subsequently, the corn bract fibers were cut into (5±0.5) mm long, thereby mixing evenly with the waste paper scraps in a ratio of 1.5:1. The wet mesh forming was adopted to prepare the fruit-cultivating bag material F. The mechanical properties of material F were tested after fabrication. The experimental results showed that the prepared material F behaved a strong tensile resistance, where the tensile loads for the dry and wet material F were up to 20.02 N and 13.79 N, respectively. Moreover, the retention rate reached 68.9% for the tensile load of wet material F. Furthermore, an excellent extension performance was achieved for the high requirements of mechanical properties in the fruit bagging. A scanning electronic microscopy (SEM) was adopted to observe the microstructure of the prepared materials F and the controlled samples in the market, further to analyze the distribution of holes in the matrix. The bagging properties were also measured, including air permeability, heat-insulating property, shading property, and water management ability. The experimental data demonstrated that the fruit-cultivating bag material F presented an excellent air permeability. Specifically, the air-flow rate and permeability of material were 73.5 mm/s and 173.7 L/m2 •s, respectively. In contrast to the controlled samples, the maximum air-flow rate and permeability were only 2.5 mm/s and 0.3 L/m2 •s, respectively. In addition, the heat loss of boiling water was about 265 kJ, when the prepared material was used to keep the boiling water for 60 min. The transmittance of the material F was about 0.4 % in the visible light range, and 0.09 % in the ultraviolet light range. The shielding properties showed that the prepared material ensured the photosynthesis of fruits during the growth process. Meanwhile, material F was expected to protect the fruit from ultraviolet light burning. Excellent ability of water conductivity was also achieved, where the core suction height reached 31 mm within 30 min. As such, the water in the fruit-cultivating bag during rainy days discharged quickly to enhance the quality of fruit products. Low wet evaporation indicated that the water retention performance was obviously better than that of the special bag B for the grape in the market. Most significantly, the thermal insulation performance of the new fabricated material was better than that of the controlled samples in the market. [ABSTRACT FROM AUTHOR]

To solve the environmental pollution and harm to human health caused by the use of adhesives in traditional paperboard, the development of environmentally friendly mycelium paperboard has become a hot topic. Due to the high dipole moment and hydrogen bonding between macromolecules, hyphae can form clamp shaped connection structures for nutrient reproduction and transfer, and can grow on natural agricultural waste, forming a natural biocomposite material. This study determined the combination of Ganoderma weberianum/corn straw, and designed single factor cultivation conditions such as cultivation time, straw particle size, and fructose addition to explore the effects on the mechanical properties, waterproof properties, and thermal decomposition performance of the hyphal paperboard formed by hot pressing. The optimal cultivation process was obtained through orthogonal experiments. The experimental results indicate that various factors have varying degrees of influence on the edgewise strength, flexural stiffness, and static bending strength and waterproofness of paperboard. Firstly, when the cultivation time was 30 days, the flexural stiffness reached 16.63 mN/m, which was 21.2% higher than the flexural stiffness of kraft paper (13.10 mN/m). Moreover, with the increase of cultivation time, the water absorption (24 hours) had also decreased from 3.92% to 1.27%. Secondly, previous research results have shown that the addition of fructose, glucose, and sucrose increases the biomass growth rate of strains. In this study, fructose was chosen as an exogenous nutrient, and the results showed that when a mass fraction of 5% fructose was added, the mechanical properties were the best, with a flexural stiffness of 18.87 mN/m, which was 30.5% higher than that of kraft paper (13.10 mN/m). Thirdly, the particle size of the sbstrate has a significant impact on the appearance and properties of the material. The results show that, from the appearance perspective, the mycelium paperboard made of 0.165～0.250 mm particle size has the closest appearance and feel to kraft paper. At the same time, during the testing period, it was found that the mycelium paperboard made of 0.165～0.250 mm straw was the least prone to deformation and loosening, and the molding effect was significantly improved compared to other components. In terms of mechanical properties, the edgewise strength increased by 17.4%, the static bending strength increased by 14.9%, and the flexural stiffness increased by 57.3%. The experimental results show that the degree of influence of various factors on the edgewise strength, flexural stiffness, and static bending strength of paperboard is in the order of particle size, cultivation time,fructose addition amount. When the particle size is 0.165～0.250 mm, the cultivation time is 30 days, and the fructose addition of 5.0%, the edgewise strength of the prepared paperboard is 14.36 N/mm, the static bending strength is 10.89 MPa, and the flexural stiffness is 33.09 mN/m. As the particle size decreases, the water absorption rate and water thickness expansion rate of the paperboard after 24 hours of water absorption decrease to 0.86% and 0.59%. The mycelium paperboard prepared by different single factors has the same thermal decomposition process, starting at around 150 ℃ and reaching its maximum at 350 ℃. This study provides a reference basis for the development and application of mycelium composite materials in the future. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the dust mass measurement of the existing dust monitoring devices in the process of farmland dust monitoring was susceptible to the influence of ambient temperature and humidity, resulting in zero drift and a large measurement error, this study designed a dust monitoring device based on temperature and humidity compensation. The device included temperature and humidity sensors, mass sensor, data acquisition system, etc., which could realize cross-regional, remote, real-time online measurement of dust mass. The zero drift and temperature and humidity data of the four dust monitoring devices collected by the temperature and humidity correlation test showed that the temperature and humidity had a significant correlation with the zero drift of the dust monitoring device (the mean values of |p| were 0.59 and 0.62, respectively),and the correlation between zero drift of the dust monitoring device and absolute humidity was more stable than relative humidity performance, a linear expression of absolute humidity and zero drift of the dust monitoring device was provided, the humidity coefficient of the expression was positively correlated with temperature. When the temperature changed from 40 ℃ to 60 ℃, the single-factor test of the temperature and the output signal of dust monitoring device showed that the zero drift was 65.7 times that of the sensitivity change, zero drift was a key factor affecting the measurement accuracy of dust monitoring device. A compensation model of the zero-point drift dust monitoring device of BP network based on temperature and humidity was established, the experimental results showed that the data fluctuation range of the proposed model was 8.8% lower than that of the basing on temperature-humidity second-order polynomial compensation model, and 60.3% lower than that of the basing on temperature compensation model of BP network. A field measurement experiment was conducted for the dust monitoring device based on temperature and humidity compensation of this paper. The results showed the device in this paper reduces the final measurement error by 57.5% and the fluctuation of measurement results by 34.2% compared to the uncompensated device, effectively reduced the measurement error of dust mass. The research results could provide technical support for the research on online real-time monitoring of farmland dust. [ABSTRACT FROM AUTHOR]

Rice straw is an important renewable resource. Under the circumstance of global resource shortage and energy shortage, it is of great significance to vigorously develop comprehensive utilization of straw. It is not only an important way to realize agricultural circular economy and low-carbon economy, but also involves sustainable development issues such as soil fertility, environmental security and efficient utilization of renewable resources in the entire agricultural ecosystem. Straw fiber ecological blanket has a good function of air permeability and water permeability, and has a significant positive effect on the ecological protection of soil surface microenvironment. Mechanical kneading is the key technology for the dismantling and optimization of agricultural waste straw fiber. In this study, the optimal process input is the spindle speed, hammer tooth clearance and material water content, and the output is the straw silk rate and calibrated unit productivity. An intelligent model was developed using Box-Behnken response surface method (RSM) to determine the functional relationship between input and output parameters. The three evaluation index models all have high reliability, and the determination coefficients are R 2 > 0.95 and R 2 > 0.94, respectively. The variance analysis of RSM regression model clarified that the influence of various factors on straw silk ratio was in the order of spindle speed > water content > hammer tooth clearance, and the linear relationship between spindle speed and straw silk ratio was increasing. The significant influence on the calibrated unit power productivity of the kneading machine is in the order of hammer tooth clearance > spindle speed > moisture content. When the hammer tooth clearance is constant, it decreases first and then increases with the spindle speed and straw moisture content. The results of factor interaction analysis showed that when the water content of straw was 40%, the spindle speed was 2 500 r/min, and the hammer tooth clearance was 13 mm, the silk ratio of rice straw was 96.75%. When the straw moisture content is 60%, the spindle speed is 2 300 r/min, and the hammer tooth clearance is 11.08 mm, the maximum calibrated unit power productivity is 29.96 kg/(kW·h). Mathematical model process parameters optimization: straw moisture content is 41.2%, spindle speed is 2 498 r/min, hammer tooth clearance is 12.84 mm. Under the combination of these parameters, the rice straw silk ratio is 96.93%, and the calibrated unit power productivity is 29.33 kg/(kW·h). Under optimized conditions, the proportion of fibers with length of 40～70 mm and > 70 mm showed an increasing trend, especially the fiber with length of > 70 mm increased by 120.55%. The proportion of fiber length-to-width ratio > 50～80 and > 80 increased from 17.39% and 6.52% to 38.64% and 16.01%, respectively. Ultra depth of field microscopy showed that the self-interweaving formability of kneaded straw fiber was significantly improved. The optimized mechanical kneading cost of straw fiber can be reduced by 27.50 yuan/t. The mechanical kneading technology strategy of the RSM model developed in this paper is effective for improving the decomposition rate of biomass straw and reducing the consumption of biomass straw, and can be widely used in the production of high-value agricultural products based on large fiber bundles of straw. The mechanical kneading technique strategy of the RSM model developed in this paper is effective for improving the decomposing silk rate and reducing the consumption of biomass straw, and can be widely used in the production of large-size fiber bundle straw based high-value agricultural products. [ABSTRACT FROM AUTHOR]

Agricultural residue is an important biomass resource in China. Utilization of agricultural residue for production of high-quality fuel oil is an attractive and challenging topic towards the goals of “Carbon Peaking and Carbon Neutrality”. Biomass hydropyrolysis integrated with volatile catalytic hydrogenation is a new process that can efficiently improve the yield and quality of bio-oil. This work is purposed to explore this new biomass conversion technology by laboratory experiments using a tandem pressurized fixed bed reactor. A bimetallic molecular sieve catalyst (NiMo-HZSM-5) was prepared by an impregnation method and used for catalytic hydrogenation. Biomass was hydropyrolyzed under a condition of 5 MPa H2 pressure, 15℃/min heating rate and 700℃ final temperature to generate volatile matter, which was online hydrogenated through the NiMo-HZSM-5 catalyst bed under a condition of 5 MPa H2 pressure, 320℃ reaction temperature and 26 s residence time (corresponding to space velocity of 112.5 h−1 ). The paper firstly presents the oil production results of three types of agricultural residue, corn stalk, cotton stalk and peanut straw by the catalytic hydrogenetion. The paper is then devoted to further clarifying the conversion characteristics of different biomass fiber constituents by means of the water washing and acid washing pretreatments of three agricultural residues as well as by adopting cellulose and lignin as model compounds. Results showed that in the case of non-catalytic hydrogenation, all of the bio-oils obtained from three agricultural residues contained oxygenates and aromatic hydrocarbons as two main classes of compounds, and that from peanut straw also contained a high proportion of nitrogenous compounds. In contrast, all of the bio-oils derived from three agricultural residues by the catalytic hydrogenation (CH bio-oils) contained almost neither oxygenates nor nitrogenous compounds. The yields of CH bio-oils reached 10.3%–15.9% for three agricultural residues. The CH-bio-oils were composed of 73.0–84.8% saturated aliphatic hydrocarbons (mainly cyclopentanes, cyclohexanes, and perhydro- naphthalenes), 6.1%–13.1% partially hydrogenated aromatic hydrocarbons (mainly di-, tetra-hydronaphthalenes) and a small amount of monocyclic aromatic hydrocarbons. The result highlights that the two-stage process of hydrogenation pyrolysis integrated with NiMo-HZSM-5 catalytic hydrogenation can effectively convert agricultural residue into a kind of high-quality bio-oil composed mainly of alicyclic hydrocarbons. The water washing could remove about half of neutral extractives from each of three agricultural resides. After the water washing, three agricultural residues showed increases in the yields of CH bio-oil to 18.1%–18.5%, suggesting that neutral extractives are less conducive to the production of CH bio-oil compared to other fiber components. The acid washing enabled cellulose and lignin to be left in the agricultural residues as main components. Via this pretreatment, the yields of CH bio-oil increased to 18.2%–22.5% for three agricultural residues, with cotton stalk having the highest yield. The yields of typical aliphatic hydrocarbon compounds (methyl-cyclopentane, methyl-cyclohexane and decahydro-naphthalene) from all three agricultural residues were increased by the acid washing. The study using model compounds revealed that cellulose is a fiber for not only achieving a high yield of CH bio-oil but also selectively producing aliphatic hydrocarbon compounds such as cyclopentane, cyclohexane, and decahydro-naphthalene. Meanwhile, lignin has a lower yield of bio-oil, and it tends to produce monocyclic aromatic hydrocarbons. Lignin can also generate cyclohexane compounds and partially hydrogenated polycyclic aromatic hydrocarbons, but it has a low propensity to the formation of cyclopentane compounds. This study has provided a new technical route to utilizing agricultural residues for high-efficient production of fuel oil. [ABSTRACT FROM AUTHOR]

The discrete element method is widely used in the analysis and improvement of agriculture machinery performance. To realize digital reproduction of maize seeding process based on discrete element method and improve seeding performance of seeding machinery, the corresponding maize grain assembly model needs to be constructed. In this paper, maize grain shape and size distribution of 10 common varieties in Northeast China were statistically analyzed, and a general shape classification method of maize grains was proposed. That is, the actual assembly of maize grains could be considered as a collection of horsetooth, spherical-cone, and spheroid maize grains. On this basis, the corresponding assembly modelling of maize grains was constructed based on the sphere filling method and discrete element method. Namely, a single maize grain model was used as a template, the main size was randomly generated according to a normal distribution, the other feature sizes were calculated from the main size to generate each maize seed model, and this was repeated; In addition, the corresponding number of maize seeds was generated according to the percentage of quantity, so that the characteristics of generated maize grain assembly model should be closer to the actual maize grain. The number of filling spheres was optimized through the stacking test and sieve test with the two varieties of maize. The results showed that when the number of filling spheres in the maize grain model was 10 ～ 14, 18, and 1 for horse-tooth, spherical-cone, and spheroid maize grain models respectively, the population characteristics of the maize grain discrete element method model were consistent with those of the actual maize grains, which preliminarily proved the validity of the proposed maize grain assembly modelling method. Further, a discrete element method simulation of the seed discharging process of the inner nest hole rower was carried out based on the established maize grain assembly model, and the rotation speed of the seed discharging wheel was optimized, and the optimization result (30 r/min) was consistent with the experimental one, which further demonstrated the validity of the proposed maize grain assembly modelling method. Finally, with the increase of the rotational speed of the seed discharge wheel, the single grain rate was the first to rise and then fall, the double grain rate was the first to fall and then rise, and when the rotational speed was 30 r/min, the single grain rate reached a maximum of 88%, and the double grain rate was the minimum of 11%. Cavity rate was the first to fall rapidly after the basic stability in the vicinity of 1%. This is because when the row of seed wheel speed gradually increased, the particles in the row of seed wheel under the perturbation of the centrifugal inertia force gradually increased, when the rotation speed of the seed discharging wheel was less than 30 r/min, the single particles could reliably rotated with the row of seed wheels, while more than the particles fell back to the bottom of the seed chamber due to the size limitations of the holes, the role of their gravity, and other factors. However, when the rotational speed of the seed discharge wheel was greater than 30 r/min, the centrifugal inertial force dominating the maize seed made it difficult for the excess particles to fall back to the bottom of the seed chamber and rotate with the seed discharge wheel's nest hole, which led to a decrease in the single grain rate. The results of this paper can provide a certain reference for the digital design of maize-seeding machinery. [ABSTRACT FROM AUTHOR]

A photoelectric detection method based on paper-based microfluidic chip in pesticide residues is proposed because the existing equipment has the problems of high cost, low degree of automation and large amount of reagent consumables. We design a kind of simple, portable, inexpensive paper-based microfluidic chip, whose bridge composite structure can enhance the uniformity of the color reaction of microfluidic enzyme, and also design the light path and circuit structure suitable for optical absorption reflection detection. The signal amplifying circuit and filter circuit are built to eliminate the interference of clutter and noise in photoelectric detection. Finally, a portable detection system of pesticide residue is established, which is an integrated test platform of the chemical reaction, the reflection effect in light absorption and the control of environment parameters. We observe and optimize the light source, chip placement angle, temperature and other process parameters of test platform in Jiangsu University in 2016. The reflection absorption effect of red LEDs (light-emitting diodes) is tested to be the best among the LEDs with different colors through detecting the paper-based chip color area. The response effect of the system is better when the angle is 10 according to the chip placement angle and reflected voltage data of photoelectric detection during the reaction. It is concluded that light absorption is the most under 35 ℃ and the chemical reaction is the most sufficient via comparing the color effect of the biochemical reaction of enzyme inhibition under different temperatures. We design different concentrations of pesticides, which are used for biochemical reactions of enzyme inhibition in different paper-based microfluidic chips. The resulting product displays blue color region in the paper-based chip, which can be characterized by the color of the different concentrations of pesticides. We use the photoelectric detection device to detect the color zone of the chip; the reflected voltage data can be obtained with this method, which correspond to different concentrations. Based on the experimental data, the model is established by the reflected voltage and the concentration of pesticide. The pesticide residues of test samples can be analyzed quantitatively based on the detection model. It can be concluded from the experimental results that with the method of photoelectric detection, the minimum detection limit of pesticide residues (trichlorfon and parathion) can reach 0.05 mg/L, meeting the national standard, and the resolution can reach 0.002 mg/L, much higher than that with the traditional method. Within the concentration range of pesticide residues, compared with the pesticide residues detector and the rapid detection card, the detection limit for the photoelectric detection is equivalent, but the consumable items are respectively 5.21% and 41.7% of the former 2 methods, and the reduction of time is respectively 23% and 33%. We propose a kind of pesticide residues detection system, which combines the photoelectric detection system and the paper-based microfluidic chip, and possesses the advantages of easy integration and low consumption. The research provides the theoretical basis and realistic foundation for making the pesticide residues detection develop to be portable and universal. [ABSTRACT FROM AUTHOR]

Soil water characteristic curve (SWCC) can show the soil water holding capacity, and is the relationship between the soil matric potential and saturation. The expansive soil has the characteristics of strong swell-shrink, developing fissures and over consolidation, especially under drying-wetting cycles. Its water holding capacity, which has an important impact on the soil properties, is totally different from the capacity of full soil. Therefore, how to exactly obtain the matric suction of expansive soil under drying-wetting cycles is very important to analyze the characteristics of mechanics and deformation of expansive soil. In order to analyze the change rule of matric suction of expansive soil under drying-wetting cycles, the artificial rainfall and evaporation was applied in drying-wetting tests of expansive soil. The specimens sized at 102 mm × 40 mm (diameter × height) were made by compaction apparatus and the initial dry density was 1.68 g/cm3. All the specimens were saturated after compaction and undergoing drying-wetting cycles in constant temperature (22 ℃) environment. The small electric fans were adopted to increase the rate of evaporation until the mass of specimens was kept constant under the condition of drying. Then the water vapours were sprayed to the surface of specimens by humidifiers until the process of infiltration stopped under the condition of wetting. That is one drying-wetting cycle so far. In this paper, 4 set of experiments under drying-wetting cycles were carried out and the cycle times were 1, 2, 3 and 4 respectively. Then 11 specimens sampled by cutting rings (61.8 mm × 20 mm (diameter × height)) in different volumetric water content were obtained in each process of drying and wetting respectively. The matric suction of each specimen was measured with filter paper of Whatman No. 42 and then the SWCCs were obtained under drying-wetting cycles. The corresponding air-entry values and residual values in different drying-wetting cycle times were calculated from SWCC. Ulteriorly, the different SWCCs obtained in different drying-wetting cycle times were fitted by the Fredlund SWCC model. Based on the above analysis, an SWCC model of expansive soil considering drying-wetting cycle effect was established finally. The results showed that: 1) With the increase of drying-wetting cycle time, the air-entry value of expansive soil was evidently reduced. Compared with the air-entry value (134.5 kPa) of expansive soil undergoing one drying-wetting cycle, the air-entry value was 58.5 kPa and reduced by 56.5% undergoing 4 drying-wetting cycles. The most effect of drying-wetting cycles on air-entry value occurred in the first cycle. 2) With the increase of drying-wetting cycle time, the residual value of expansive soil was also reduced. Compared with the residual value (1040.5 kPa) of expansive soil undergoing one drying-wetting cycle, the residual value was 528.5 kPa and reduced by 49.2% undergoing 4 drying-wetting cycles. Moreover, the residual value is reduced evidently after undergoing one drying-wetting cycle and it cannot yet be stabilized with the increase of drying-wetting cycle time. That means the effect of drying-wetting cycles on ari-entry value is much greater than on residual value. 3) The fitted parameters in the SWCC model of expansive soil considering the effect of drying-wetting cycle have a good linear correlation with the drying-wetting cycle time. With the increase of drying-wetting cycle time, the soil air-entry value and change rate of water content reduce while the residual value increases gradually. The results can provide a reference for the further analysis on soil matric suction and its application in engineering. [ABSTRACT FROM AUTHOR]

Most of the articles about the degradation performance of biodegradable nursery pots were focused on degradation morphology, strength of pot body, changes of soil, effect of raising seedings and so on. However, it rarely study on degradation rate, molecular structure of degradation process and changes of crystallinity. In order to study the degradation performance of biodegradable nursery pots, three types containing paper pot, undecomposed straw pot and decomposed straw pot were used to carry out the experiment in this paper. The degradation rate, molecular structure and crystallinity were evaluated and analyzed through the experiments of degradation test in soil, Fourier infrared scanning and x-ray diffraction. It is necessary to have a further research on degradation performance of biodegradable nursery containers and mechanism of degradation. The results showed that the degradation rate of paper pot with the best degradation property was growing at a rate of about 2 times. The maximum degradation rate was up to 85%. The undecomposed straw pot has always maintained a stable and increasing degradation rate. The undecomposed straw pot with degradation rate of 78% in the eighth week was higher than that of decomposed straw pot. The changes of mass loss and degradation rate were likely to be affect by a variety of microbial community structure. On the one hand, due to partial degradation of cellulose and semicellulose, the remaining part kept stable performance. On the other hand, the changes of microbial structure and activity affected the degradation performance. By comparison, FTIR absorption strength of the biodegradable nursery pots was decreased in different degree due to the changes of molecular structure during biodegradation. The absorption strength of paper pot decreased in the location of 3340 cm-1 and 2920 cm-1 indicated that the degradation of cellulose and hemicellulose. In addition, the relative peak of 1640 cm-1 and 1460 cm-1 decreased with the further degradation, showing that lignin was degraded in the biological reaction. The peak strength of CH stretch band was enhanced obviously at 892 cm-1. It showed that the substitution reaction of aromatic ring was an important part of the degradation process of lignin. The main group structure of undecomposed straw pot and decomposed straw pot were basically the same. The absorption peaks strength decreased at 3340 cm-1 and 1010 cm-1 might be due to the oxidative decomposition of benzene methanol unit. The peak strength of CH stretch band decreased at 2920 cm-1 probably because of the decomposition of carbohydrates and the fracture of partial fatty chain. The degradation of lignin resulted in the decrease of peak strength of 1650 cm-1. The x-ray diffraction peak intensity of paper pot with the best degradation property decreased obviously after 8 weeks test. The diffraction peak disappeared at 2 θ=15.6°. At the same time, The intensity of diffraction peak decreased at 2 θ=22.5°. In addition, it appeared new diffraction peaks at 2 θ=27° of undecomposed straw pot and decomposed straw pot. This might be due to the changes of crystalline which caused by the degradation of straw. The biodegradable nursery containers with excellent degradation property could improve the problem of environmental pollution effectively. [ABSTRACT FROM AUTHOR]

Aiming at the problems of incomplete efficiency composition, limitation to single working condition calculation and lack of power loss analysis in the current efficiency model research of hydro-mechanical continuously variable transmission（HMT）, this study establishes a full-condition efficiency model of HMT considering the power loss of transmission components and accessories. Firstly, the general efficiency model of HMT variable pump-quantitative motor is established, and the relevant parameters are recognized according to the efficiency test data; then the efficiency models of other HMT transmission mechanisms and accessories are established, and the complete HMT efficiency model is obtained by combining the efficiency models of each part. Based on the self-developed HMT, the calculation is carried out to obtain the full working condition efficiency map and the loss power of each component mechanism, the test bench is built, and the efficiency test is carried out on the HMT to validate the accuracy of the model, and the average absolute errors of the efficiencies of the HM1 section and the HM2 section under full working condition are 0.027 3 and 0.026 1, respectively, which improve the accuracy of the HM1section and the HM2 section by 64.59% and 55.46% respectively, comparing to the accuracy of the comparative methods. Combined with the efficiency diagram and power loss situation to analyze the HMT efficiency characteristics and influencing factors, the results show that: 1) the HMT efficiency model in this paper is mainly affected by the load and the displacement ratio e of the hydraulic unit, and the influence of the input speed is not significant; 2) the main influencing factors of this paper’s HMT efficiency model are the load under the low load, and the displacement ratio under the high load; 3) the power loss of the pump motor has the greatest influence on the HMT efficiency, and the hydraulic loss power is large when e ＜ 0. The HMT efficiency is low; when e = 0, the hydraulic loss power is 0, and the HMT efficiency reaches the highest; 4) the power loss of gears and clutches does not have a negligible impact on the HMT efficiency, and the consideration of the power loss of other accessories is conducive to the improvement of the modeling accuracy. Measures to improve the efficiency of HMT:1) Try to avoid HMT working under power cycling conditions for a long time, and recommend operating at purely mechanical points and their nearby work points under the premise of prioritizing load demand and ensuring engine economy; 2) Avoid prolonged low load operation, and should be kept in high load conditions; 3) Select high-efficiency hydraulic unit or improve the transmission efficiency of the hydraulic unit by improving the parameters; 4) Improve parameters to reduce power loss under the premise of meeting the performance requirements of gears and clutches. [ABSTRACT FROM AUTHOR]

High energy consumption and operating costs have affected the development of green and efficient granary warehouses. In order to promote energy saving and emission reduction in the reconstruction of granaries, this paper proposes a special solar photovoltaic-ground source heat pump cooling system for grain warehouses. The study investigates the feasibility and energy-saving effects of this combined system in grain warehouses, aiming to achieve "zero energy consumption" and "zero emissions" for green storage. Bungalow warehouses are typically single-story buildings widely used in grain reservoir, characterised by large roof area and spacious room between building rows. These features facilitate installing both underground heat exchangers and solar photovoltaic panels. Compared with residential buildings, adquate space can enable the solar photovoltaic-ground source heat pump system to integrate more well with warehouse buildings, and provide more cooling and power capacity. In summer, the electricity generated by photovoltaic panels is primarily consumed by the ground source heat pume for grain warehouse cooling. Then, the electricity is transmitted to living and office buildings in the grain reservoir, meeting the lighting requirements. Finally, excess power is sold to the State Grid. During other seasons when cooling is not required, the generated electricity first supplies the living and office buildings, and extra power is sold to the State Grid, too. Therefore, operating costs can be reduced through the surplus electricity sale. To maximize the combined advantages of solar and geothermal energy technologies in low-temperature grain storage, it is essential to conduct thorough research and design focused on the function and structure characteristics of warehouse buildings. This study aims to achieve deeper energy savings in storage application with current technologies. Simulation software was used to analyze the operation characteristics of the proposed system. Firstly, according to the actual size and working conditions of a grain warehouse, a simulation model for an air source heat pump system was established using TRNSYS software, which was verified with test data, and the the temperature response mechanism used in later simulation was obtained. Then, a TRNSYS simulation model for the solar photovoltaic-ground source heat pump cooling system was developed, considering the temperature and humidity requirements for low-temperature grain storage. System performance was simulated by this model over short and long-term periods, computing its operational characteristics. Finally, the cooling system was evaluated according to operational performance, such as photovoltaic adaptability, economic efficiency, energy savings, and environmental benefits. The results show that after oneyear operation, the system maximum COP is 4.76 and the minimum value is 3.3; after ten-year running, the maximum COP is 3.96 and the minimum is 3.07. In one-year operation, the soil temperature around underground heat exchangers increased by 0.65℃, and after ten years, it increased by 5.4 ℃. Underground thermal balance is an important factor for long time working performance. To avoid continuous soil temperature rise, the system should include a cooling tower into the equipment or use the ground source heat pump for space heating in the office and living buildings during winter. The ratio of system annual power generation to consumption is 192% to 270% for the warehouse and the investment payback period is 7.8 years. With ground source heat pump system, the standard coal consumption for cooling grain is 1.2 kgce/(t·a), compared to 2.93 kgce/(t·a) for the air source heat pump system, resulting in an energy saving rate of 59%. Additionally, harmful emissions are reduced by 3,841 t/a annually. These findings indicate that the solar photovoltaic-ground source heat pump cooling system is more suitable for warehouses than traditional grain cooling methods. This research can be expected to provide theoretical support and design references for developing more efficient granary cooling systems. [ABSTRACT FROM AUTHOR]

Nitrogen is a key nutrient for crop growth. Excessive or insufficient nitrogen affects crop growth, yield, and quality. Additionally, excessive nitrogen fertilizer can cause soil and water pollution. Applying panicle fertilizer during the late jointing stage can promote rice panicle growth. Therefore, accurately and timely monitoring of nitrogen status in rice fields during the late jointing stage and timely optimizing fertilization strategies is crucial for ensuring rice yield and environmental protection. This paper integrates multimodal data from unmanned aerial vehicle (UAV) remote sensing and ground observations to construct inversion models for leaf nitrogen content (LNC) and plant nitrogen content (PNC) of rice at the late jointing stage. The research was conducted at the Shapu Experimental Base of the Agricultural Science Research Institute in Zhaoqing City, Guangdong Province, with two field experiments carried out during the late rice seasons of 2021 and 2022. Each of Experiment 1 (2021) and Experiment 2 (2022) included 30 experimental plots, designed with 5 nitrogen fertilizer gradients, 2 planting densities, and 3 replications. Phosphorus and potassium fertilizers were applied uniformly across all plots. UAVs equipped with multispectral and RGB cameras were used to acquire remote sensing images of rice canopies during the late jointing stage. Vegetation indices (VIs) and texture feature values (TFVs) were extracted from the multispectral images, with TFVs derived using the gray level co-occurrence matrix (GLCM) method. Texture indices (TIs) were then constructed by combining TFVs. RGB images were used to generate digital surface models (DSM) for bare ground (pre-transplant) and rice fields (late jointing stage). These DSMs, combined with ground reference methods, were used to construct crop surface models (CSM) to derive estimated canopy heights (ECH) for each plot. Manually collected data included measured canopy height (MCH) and field nitrogen management data (FN) used as ground observations. For each experimental plot, three representative rice plants were selected as samples. After removing the roots, the leaves and stems were separated and dried at 85℃ to a constant weight, which was recorded as the aboveground biomass of the leaves and stems. The true values of leaf nitrogen content and stem nitrogen content were obtained using the Kjeldahl method. Combining these values with the dry weight data, the true values of plant nitrogen content were calculated. The maximal information coefficient (MIC) was used as an evaluation metric for feature assessment and selection. Random forest regression algorithms were employed to construct inversion models for rice LNC and PNC, respectively, using the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) as model evaluation metrics. The analysis and experimental results indicate: TIs constructed using combinations of TFVs significantly enhanced the correlation between texture information and LNC and PNC. When the UAV flight height was 100 m, the Ratio Texture Index constructed using a 9×9 sliding window size in the GLCM method showed the best performance, improving the MIC value by 11.48% compared to the best TFV. For conventional machine-transplanted rice planting density, the correlation between TIs and LNC and PNC was best when the GLCM sliding window size was set to 9×9 or 11×11 at a UAV flight height of 100 m. The ECH derived from the CSM showed a high correlation with the manual MCH in the field. Including canopy height (MCH or ECH) as an input feature in the random forest regression model significantly improved the inversion accuracy of rice nitrogen content. The ECH extracted from the CSM showed high estimation accuracy (R² = 0.77, RMSE = 3.4 cm, MAE = 2.8 cm). The inclusion of canopy height (MCH or ECH) in the model construction improved the inversion accuracy for PNC more significantly compared to LNC. Integrating UAV remote sensing and ground observation multimodal data, the random forest regression algorithm significantly improved the inversion accuracy of rice LNC and PNC at the late jointing stage. Considering both inversion accuracy and operational convenience, it is recommended to use a feature combination of VI+TI+ECH+FN in field production. Compared to using VI alone, this feature combination improved R² by 6.73%, and reduced RMSE and MAE by 13.62% and 14.68%, respectively, for LNC inversion. For PNC inversion, R2 was improved by 12.53%, and RMSE and MAE were reduced by 22.09% and 19.80%, respectively. The results demonstrate that constructing random forest regression models by integrating UAV remote sensing and ground observation multimodal data can accurately detect rice LNC and PNC, providing a scientific basis for rice field management and fertilization decision-making. [ABSTRACT FROM AUTHOR]

At present, the cleaning of water plants in crab pond is primarily relied on manual operations, which is characterized by high labor intensity and low efficiency. The automatic water plant cleaning boat can greatly reduce the labor intensity and improve the operation efficiency, and determining the water plant distribution in crab pond is the key basis for planning the efficient operation path of the cleaning boat. Addressing the challenges posed by the high similarity between water plants and shoreline vegetation, as observed in drone-captured images of the crab pond, which complicates the accurate distinction between them, a region segmentation and localization method for water plants in crab pond based on improved YOLOv8n-seg was proposed in this paper. The focus of the improved model lies in the reduction of the model size and the enhancement of the recall rate. Firstly, in order to diminish the model size, the lightweight HGNetv2 (hierarchical graph network) based on RTDETR (real-time detection transformer) was used as the backbone feature extraction network. Secondly, the neck network was reconstructed based on the lightweight structure of Efficient Rep to reduce the number of parameters and enhance the multiscale feature fusion ability of the mode. Finally, the SegNext attention mechanism was introduced in the feature extraction layer to enhance the sensitivity of the model to the water plant area. In order to eliminate the redundant regions generated by the model in the recognition process and further improve the segmentation accuracy, the binarization processing was used to optimize the segmentation results, and the image processing algorithm was combined to screen the area of the water plant area. After coordinate conversion, the precise longitude and latitude coordinates of water plant contour were obtained. The experimental results showed that the improved model exhibited a strong discriminatory and segmentation effect on water plants in crab ponds. The parameter number, calculation quantity and model size of the improved model were only 1.49 M, 8.4 GFLOPs, and 3.27 MB, respectively. Compared with the original YOLOv8n-seg model, the parameter number, calculation quantity and model size of the improved model were reduced by 54.3%, 30.6% and 49.7%, respectively. The model before and after improvement was deployed to the Jetson Orin Nano embedded AI computer for testing. It was found that the preprocessing speed of the improved model was almost the same as that of the YOLOv8n-seg model. The inference speed, post processing speed and segment speed were increased by 30.6%, 46.4% and 32.3%, respectively. In addition, the recall, precision, and mean average precision of the improved model achieved values of 91.5%, 89.3% and 95.6%, respectively. The improved model achieved the best balance of calculation, parameters, recall and detection accuracy comparing with YOLOv5sseg, YOLOv8s-SwinTransformer, YOLOv8s-seg and YOLOv8n-GoldYOLO models. The coordinate conversion test showed that the minimum distance error of water plant location accuracy was 0.13m, the maximum distance error was 0.33 m, and the average distance error was 0.22 m, which verified that the improved model can meet the requirements of water plant region segmentation and localization in crab pond. The finding can provide an important reference for the automatic operation path planning of water plant cleaning boat. [ABSTRACT FROM AUTHOR]

Mechanized harvesting of fruit has been the ever-increasing trend in smart agriculture. However, the picking quality has been confined to the commonly-used unstructured planting for fruit trees. The robots can easily encounter the collisions with the dense branches and leaves during harvesting. The random spatial distribution of orchard obstacles can be highly influenced by the variation in the lighting conditions. It is very challenging on the real-time reconstruction of obstacles. In this study, a real-time reconstruction of orchard obstacles was proposed to combine the semantic segmentation and 3D point cloud analysis. Firstly, taking guava orchard as an example, the image acquisition was carried out using Intel RealSense D435i depth camera at a distance of about 15-50 cm from the guava fruit trees with a resolution of 640×480 in the orchard of Haiou Island Jiashuo Farm, Guangzhou City, Guangdong Province, China. Data enhancement was performed to improve the generalization and robustness of the model, according to the mirroring, luminance enhancement, luminance attenuation, adding Gaussian noise and pretzel noise. A total of 1,250 samples were obtained to randomly divided into the training, the validation and the test set in a ratio of 6:3:1, where there were 750 samples in the training set and 375 samples in the validation set; The test set was 125 samples. Then, a real-time semantic segmentation model was built using DeepLabV3+. The CoT module was introduced on the low-level feature map and high-level feature map of the encoder, with the MobileNetV3-large as the backbone network. The segmentation accuracy was improved on the thin obstacles like twig groups. The detail information of feature maps was strengthened to suppress the useless information. Furthermore, an edge-assisted loss function was proposed using the Sobel operator. Laplace edge loss function was used to further increase the accuracy of semantic segmentation, in order to improve the accuracy of picture edge segmentation. Next, the intrinsic parameters of the depth camera were combined to transform the semantic map of obstacles into a 3D point cloud. Statistical analysis was applied to remove the outliers. The voxelization was used to approximate the reconstruction of tree trunks and branches. Moreover, Euclidean clustering was employed to identify the individual 3D fruits. Among them, the minimum bounding box of the fruit was served as the reconstruction. Finally, the pose change matrix between the camera and the robot was obtained using the hand-eye calibration with a chessboard pattern. The minimum bounding box of the fruit and the voxelized cubes of branches and tree trunks were transformed into the working space of robot picking. The experimental results show that the MIoU (mean intersection over union) of obstacles in MobileNetV3-large-DeepLabV3+ was improved from 69.7% to 74.3% after the introduction of the CoT block, indicating an increase of 4.6 percentage points; The Sobel algorithm also considered both horizontal and vertical gradient information, indicating some anti-noise performance. The MIoU of the obstacle was further improved from 74.3% to 77.2%, indicating an improvement of 2.9 percentage points. The overall MIoU of the improved model was 7.0 percentage points, 0.6 percentage points, and 1.0 percentage points higher than those of Xception-DeepLabV3+, BranchNet, and LRASPP, respectively. There was 1.7 percentage points lower than BiSeNetV3; The ACC was 3.1 percentage points, 0.5 percentage points, and 0.6 percentage points higher than those of Xception-DeepLabV3+, BranchNet, and LRASPP, respectively, while there was 2.2 percentage points lower than BiSeNetV3; The FPS of the improved model was as high as 78.8 frames per second, which was faster than the comparison; The number of parameters was 4.4 M, which is 8% and 31% of Xception-DeepLabV3+ and BiSeNetV3, respectively, while BranchNet and LRASPP are 68% and 73% of the parameters in this paper, respectively. Besides, the IoU (intersection over union) values of the voxelized cubes for the fruit and branches were 65.7% and 56.6%, respectively, and the voxelization time was 0.34 s and 0.84 s, respectively. The better robustness and real-time performance can fully meet the obstacle avoidance requirements of guava picking robots. The real-time semantic segmentation of irregular obstacles was optimized to utilize more sophisticated attention modules or backbone networks. The finding can provide the better ways to enhance the effects of obstacle reconstruction. [ABSTRACT FROM AUTHOR]

The matric suction of saline soil is the driving force that causes water migration, and it is of great significance for exploring the mechanism of water and salt migration in saline frozen soil. In order to investigate the effect of phase transition of soil pore solution induced by temperature on the matric suction of saline soil, the matric suction of different types of saline soils was measured by pF meter under different temperature conditions. The natural soil was first washed to obtain the desalinized soil, then different salt mass ratios of sodium chloride and sodium sulfate (0:6, 4:2, 2:4, 6:0) were added into the desalinized soil to model the composite saline soil, the salt content and water content were set to 6% and 20%, respectively. Subsequently, the remolded saline soil was compacted into the test chamber (0.37 m×0.25 m×0.22 m), and its dry density was controlled at 1.6 g/cm³. Hydra Probe II and pF meter were embedded at 11cm of the sample to measure the liquid water content and matric suction versus temperature. Freezing-thawing box (TMS 9 018-R30), produced by Zhejiang Tomos company, was employed to control the experimental temperature by step cooling method. The temperature range was from 30 to -24 °C, and the temperature interval was 3 °C. The data was collected by CR300 at an interval of 6 h. When the experiment was complete, the temperature effect of saline soil matric suction was analyzed and the effect of water/salt phase transition was explored. The experimental results reveal that the matric suction of saline soil increases almost linearly with the decrease of temperature when no phase transition occurs, which is caused by surface tension and wetting coefficient. Different types of saline soils have different effects on the phase transition process of pore solution, resulting in significant differences in the matric suction of different types of saline soils. The mirabilite crystallizes at 19 °C and the second phase transition occurs at -9 °C for the composite saline soil with 2% NaCl+4% Na2SO4, and the phase transition of composite saline soil with 4% NaCl+2% Na2SO4 occurs at 11 and -16 °C.Sodium chloride can inhibit the occurrence of ice formation, then the matric suction caused by ice formation is reduced, and the segregated frost heave caused by water migration is restrained indirectly. Besides, sodium sulfate is prone to crystallization at positive temperatures, and ice is also produced in the secondary phase transition process, both salt crystallization and ice formation increase the matric suction in sodium sulfate soil, and water and salt redistribute in this process, salt expansion and frost heave are induced. For composite saline soil, sodium sulfate mainly affects the salt crystallization of the soil at a positive temperature, while sodium chloride mainly affects the ice formation of the soil at a negative temperature. The different proportions of the two salts cause significant differences in the salt crystallization temperature and the secondary phase transition temperature, resulting in more complex phase transition laws in composite saline soil, and it is more difficult to predict the matric suction of composite saline soil. In addition, this paper further explores the contribution of ice and salt crystallization to the variation of matric suction, providing effective references for a deeper understanding of the relationship between phase transition processes and matric suction in saline soils. [ABSTRACT FROM AUTHOR]

Sericulture has a pivotal position in the agricultural production of China. The number of silkworms is one of the key indicators in the process of silkworm breeding, which is crucial to the feeding and healthy growth of silkworms. Accurate detection of the number of silkworms can ensure the quality of sericulture products. However, manual counting has not fully met the needs of large-scale production in recent years. There is a high demand to combine AI technology for silkworm counting. In this study, silkworm detection and counting were studied from the perspective of deep learning. Several challenges were solved in the detection of silkworms. Since the silkworms belonged to the small targets, it was difficult to detect the fewer features; When the number of silkworms was high, serious masking occurred to cause interference in the detection of densely distributed silkworms; It was difficult to ensure that the clear images of silkworms were captured from the actual production. The blur images were easy to deteriorate the accuracy of detection. Two tasks were covered as follows. 1) The image dataset of silkworms was constructed; 2) MAM-YOLO, a silkworm detection and counting model was proposed using multi-dimensional perception. The pictures of silkworms were first taken from the Guangxi Sericulture Technology Promotion Station. These pictures of silkworms were preliminarily processed. The polygon annotation was then selected, according to the body and distribution of silkworms. The pictures of silkworms were annotated with the open-source tool (Labelme). Finally, the dataset was expanded using image transformation and noise addition. The silkworm picture dataset was obtained for the subsequent training, verification, and testing. An improved YOLOv8 (MAM-YOLO) was proposed to detect the silkworm, in order to realize the accurate detection and counting. A silkworm detection and counting model was proposed using space-to-depth transformation and image deblurring. The fine-grained detection performance of blurred images was improved in the MAMYOLO model. The space-to-depth transformed convolution was utilized instead of stride convolution for the pooling layer in the feature extraction. The fine-grained features were extracted from the images, in order to further improve the detection of the low-resolution and small targets. In addition, the impact of blurred images on the detection performance was reduced by introducing a deblurring module into the model. RCS-OSA module was added to the YOLOv8 model. The network feature extraction was improved to integrate the information of different sizes in the receptive fields. The detection head of the original network was improved into the multi-dimensional sensing head. The three-dimensional features of size, space, and channel were aggregated to enhance the detection of silkworm targets. The loss function was replaced with the EIOU loss function. The regression accuracy of the detection frame was improved to reduce the object occlusion. Experimental results show that the MAM-YOLO more accurately detected the silkworm targets. The detection speed of mAP@0.5:0.95 reached 74.8% and 22.2 frames per second, indicating the better functions of silkworm detection and counting with high precision and high rate. The detection requirements were fully met in the actual work of the Guangxi sericulture station. The results show that the silkworm detection and counting system of the technical indicators met the application requirements. The finding can provide the detection services for the actual production work of the silkworm breeding station. In the end, a generalization experiment was conducted on the Global Wheat Challenge 2021 dataset. It was found that the MAM-YOLO was also achieved in the better detection performance of wheat ears, particularly on the small targets similar to dense occlusion.Sericulture has a pivotal position in China's agricultural production. The number of silkworms is a key indicator in the process of silkworm breeding, which is crucial to the feeding and healthy growth of silkworms, and accurate detection of the number of silkworms can ensure the quality of sericulture products. The traditional way of counting silkworms manually is inefficient, and it needs to be combined with technology to intelligently complete this kind of heavy work. In this thesis, from the perspective of deep learning, a silkworm detection and counting method is studied, and several problems in silkworm detection are solved: silkworms belong to small targets, which have fewer features so that they are not easy to detect; silkworms are densely distributed, and when the number of silkworms is high, serious masking phenomenon occurs which causes interference in the detection; it is difficult to ensure that the images of silkworms obtained in the actual production are clear, and blur images will make the detection affected. The main research content of this thesis is as follows: This chapter completed two main tasks: 1) Completed the production of silkworm image data set; 2) MAM-YOLO, a silkworm detection and counting model based on multi-dimensional perception, was proposed.In this paper, pictures of silkworms were taken by workers from Guangxi Sericulture Technology Promotion Station. First, these pictures of silkworms were preliminically processed. Then, according to the characteristics of the body and distribution of silkworms, the polygon annotation method was selected, and the pictures of silkworms were annotated with the open-source tool Labelme. Finally, the data set is expanded by means of image transformation and noise addition, and finally the silkworm picture data set is obtained for subsequent training, verification and testing.This chapter proposes an improved silkworm detection method based on YOLOv8, MAM-YOLO, which realizes accurate detection and counting of silkworm. Secondly, aiming at the problems of loss of fine-grained information and poor detection performance of blurred images in the MAM-YOLO model, a silkworm detection and counting model based on spaceto-depth transformation and image deblurring is proposed. The model utilizes space-to-depth transformed convolution instead of stride convolution and pooling layer in the feature extraction process, so that fine-grained features in the image can be extracted, which further improves the detection capability for low-resolution and small targets. In addition, the impact of blurred images on the detection performance is reduced by introducing a deblurring module into the model. By adding RCSOSA module to YOLOv8 model, the capability of network feature extraction is improved, and the information of different size receptive fields is integrated. The detection head of the original network is improved into multi-dimensional sensing head, and the three-dimensional characteristics of size, space and channel are aggregated to enhance the detection ability of silkworm targets. By replacing the loss function with EIOU loss function, the regression accuracy of detection frame is improved and the problem caused by object occlusion is reduced. Experiments show that MAM-YOLO can more accurately detect silkworm targets, and the detection speed of mAP@0.5:0.95 reaches 74.8% and 22.2 frames per second, which realizes the functions of silkworm detection and counting with high precision and high rate, and can meet the detection requirements in the actual work of Guangxi sericulture station. results show that the silkworm detection and counting system of the technical indicators have met the application requirements, and can provide detection services for the actual production work of silkworm breeding station.In the end, a generalization experiment was conducted on the Global Wheat Challenge 2021 data set, and it was found that MAM-YOLO also showed a high improvement in the detection performance of wheat ears, proving that the proposed method can generalize to other small targets similar to dense occlusion. [ABSTRACT FROM AUTHOR]

Blueberry fruits have been highly susceptible to decay and deterioration after harvest. Only a very small proportion can be used for fresh consumption. The majority must be frozen for storage and subsequent processing at present. However, the commonly used direct freezing of blueberries after harvest can reduce the utilization rates for subsequent processing. Some pretreatments (addition of cryoprotectants and osmotic dehydration) before freezing have great potential to improve the quality of blueberries after thawing. The aim of this paper was to investigate the effect of Lactobacillus plantarum PC11 fermentative bacterial metabolites, dextran, and winter wheat protein combination sugar solution maceration treatments on the freeze-thaw quality of blueberries. According to previous experiments and pre-tests, the concentrations were determined and used in the test. Five groups were established with direct freezing and sugar solution impregnation (65 g/mL) as the control group. The experimental groups were PC11 fermentation bacterial metabolites (10 g/mL), dextran (1 g/mL), and winter wheat proteins (0.1 g/mL) in combination with sugar-liquid impregnation pre-treatment and then freezing. A comparative analysis was performed on the blueberry antioxidant capacity (free radical scavenging capacity, total antioxidant capacity), phytochemical content (total phenolics, ascorbic acid, total anthocyanins, and proanthocyanidins), textural and conformation (adhesion, hardness, chewiness, and elasticity), freezing parameters (subcooling point, freezing point, time to pass through the zone of the maximum ice crystal formation, time difference between subcooling and freezing point), juice loss rate, color difference, cosmetic qualities and cell membrane permeability. Three biogenic freezing acids were also used in the test. The results showed that the treatment with the acids was effective in protecting the quality of blueberries after freezing and thawing. The treatment group with the addition of PC11 fermentation bacterial metabolite (10 g/mL) was significantly different (P<0.05) from the rest groups; The indices of blueberries after freezing and thawing were effectively maintained, where the color difference value was 6.38, which was the closest to that of fresh samples. The shape of fruits was also much closer to the round shape of fresh blueberries, with relatively few wrinkles and browning; The hardness of 1.22 N increased by 9%, compared with the sugar impregnation group. The adhesion, chewability, and elasticity were 0.89N, 0.56 N, and 0.65 mm, respectively; The anthocyanin and proanthocyanidin content shared a significant difference (P<0.05), with the content of 2.59 mg/g and 0.54 mg/g. The total retention of anthocyanin increased by 1.78 times, compared with the sugar impregnation group; The lowest juice loss rate was 6.32%, which was significantly lower than that of the sugar solution impregnation group (P<0.05, 1.36%); The lowest supercooling and freezing points were −7.63 °C and −3.47 °C, respectively. Compared with the sugar solution impregnation, the time to pass through the maximum ice crystal formation zone was shortened by 348.5 s, indicating a high freezing rate. It inferred that the PC11 fermentation bacterial metabolites belonged to the microbial fermentation metabolism, with the lower molecular weights to enter the cell by osmosis for the antifreeze effects. In conclusion, the combination of cryoprotectant impregnation pretreatment and freeze-thawing can be expected to improve the quality of color, texture, and active substance content of freezethawed blueberries. The pretreatment conditions and parameters were also optimized to successfully improve the nutrient retention and quality of blueberries. The finding can provide a strong reference for the frozen storage of blueberry during raw materials processing. [ABSTRACT FROM AUTHOR]

In 2021, China achieved a comprehensive victory in the battle against poverty, and fully advancing the work of rural revitalization became the new focus of efforts. As an important component of rural areas, cultivated land is the material foundation and important support for rural revitalization.The multifunctional evaluation of cultivated land can help to reveal the multifunctional value of cultivated land, providing more reasonable cultivated land use and optimization measures by exploring the key areas and types of trade-offs and synergy between functions. It is of great significance for advancing the rural revitalization strategy and achieving the modernization of agriculture and rural areas. Taking Hebei Province as the research area, this paper constructs a functional evaluation index system to classify and evaluate cultivated land's production, ecological and living functions and subfunctions. Based on Spearman rank correlation and spatial autocorrelation, we examined the tradeoffs and synergy among different cultivated land functions, used the natural discontinuity classification method to divide cultivated land functions into secondary, tertiary and fourth-level classifications, compared the Spearman rank correlation coefficients between the functions in each group of data, determined regional differences in functional correlation, identified key functional trade-off and synergy regions as well as sub-functional role types, and finally cultivated land functional zones and core areas were divided. The results show that: (1) From 2010 to 2020, the production function of cultivated land in Hebei Province remained stable, the ecological function decreased, and the living function gradually improved. The three functions are all in a synergy relationship and apart from a weakening in the synergy between living and ecological functions from 2015 to 2020, the degree of synergy among other types of functions has gradually increased. (2) The key areas for functional synergy are mostly high-value areas. In the zoning based on the level of production function, the relationship between production function and ecological function shifted from low-value synergy to high-value trade-off. And in the zoning based on the level of living function, the synergy between the living function and the other two types of functions decreased as the living function improved. (3) There are more complex trade-offs and synergy among the subfunctions of cultivated land in Hebei Province. The synergy between landscape aesthetic subfunction and the three types of production subfunctions is the key to the synergy between production function and ecological function, and the synergy between landscape aesthetic subfunction and food security subfunction contributes the most to the synergistic relationship between ecological function and living function, and the synergy between grain production subfunction, large-scale production subfunction and food security subfunction is the key synergy between production function and living function. (4) Based on the values of cultivated land function, 6 types of cultivated land function advantage areas are divided, and 3 types of core functional areas are identified by combining spatial autocorrelation analysis. Our research analyzes the regional differences in trade-offs and synergy of cultivated land functions, identifies the important types of subfunctions that make significant contributions to functional synergy and divides cultivated land functional zones accordingly, to provide a more accurate optimization scheme for cultivated land use. [ABSTRACT FROM AUTHOR]

Heat exchangers serve as pivotal components in heat exchange systems. Compared with the straight channels, spiral channels have better performance due to the curvature of the centrifugal force generated by the secondary flow. Consequently, heat exchangers employing spiral channels find widespread application in agricultural engineering, including greenhouse air conditioning, water circulation systems, and agricultural product processing and storage for effective heat management. In this paper, we propose enhancing the heat transfer efficiency of spiral channels by introducing corrugations with longitudinal fins on the outer channel wall. To elucidate the impact of corrugation, we establish three-dimensional models of smooth spiral channels, corrugated spiral channels, and corrugated spiral channels with longitudinal fins. Initially, we investigate the influence of corrugated number on flow and heat transfer performance numerically. Through comparative analysis involving the development and variation of velocity and vorticity fields, average Nusselt number Nu, fanning friction factor f, average Dean number Dnm, secondary flow intensity Se under different corrugated numbers, and the Performance Evaluation factor of heat exchanger based on Comprehensive (PEC0), insights are drawn. Subsequently, longitudinal fins are integrated into the corrugated spiral channel, and a numerical study is conducted to assess the effect of varying geometric parameters of the longitudinal fin on the fluid flow and heat transfer performance inside the channel. Analysis of different longitudinal fin widths and heights on velocity and temperature fields, Nu, f, and Performance Evaluation factor of heat exchanger based on Comprehensive (PECw) of the channel is performed. The findings indicate that corrugated spiral channels outperform smooth spiral channels in heat transfer and flow performance. With increasing corrugated number n, secondary flow intensity within the corrugated spiral channel intensifies. Notably, at a Reynolds number of 550, a multi-vortex structure emerges in the corrugated spiral channel with a corrugated number of 21. At Reynolds number 750 and corrugated number 21, the Nusselt number of corrugated spiral channel increases by 47.08% compared to the smooth spiral channel. At the same time, the resistance loss in the channel also increases, and the Fanning friction factor of the corrugated spiral channel increases by 59.20%. When the Reynolds number is 750, the PEC0 of the corrugated spiral channel with 18 corrugations is the highest, which increases by 27.66% compared to the smooth spiral channel. On this basis, longitudinal fins are added to the corrugated spiral channel, and it is observed by numerical simulation that symmetrical longitudinal vortices are induced by the fins. Further, the longitudinal fins induce symmetrical longitudinal vortices, enhancing flow and heat transfer in the channel's middle section. Under the condition of constant fin width and increasing fin height, the comprehensive heat transfer performance of the channel has a maximum value, that is, when fin width w=1/3W, fin height h=1/6H and Reynolds number Re=250, the highest PECw is 1.157. When the fin height is fixed, increasing the fin width w will also make the PECw of the channel first increase and then decrease. The correlation formulas for Nusselt number Nu, Fanning friction factor f and secondary flow intensity Se in corrugated spiral channel are fitted, with deviations within ±14.0%, ±10.2% and ±4.4%, respectively. This provides a certain reference for the application of corrugated spiral channels with longitudinal fins in the thermal design and usage of heat exchangers. [ABSTRACT FROM AUTHOR]

Agricultural machinery dispatching operations, as an innovative model of socialized agricultural machinery services, have been widely implemented in county- or district-level administrative areas across the province. Due to the similar crop maturity periods within the same province, the demand for agricultural machinery is concentrated during peak operation periods, leading to a supply-demand imbalance where some machinery owners have no work while others have jobs but no machinery. This not only affects agricultural production efficiency but also increases the difficulty and complexity of agricultural production due to the lack of scientific and rational scheduling strategies. To address this challenge, many studies have adopted traditional heuristic algorithms to optimize the scheduling of harvester cooperative operations. However, issues such as low work efficiency and high operational costs remain. In response to these challenges, this study constructs a harvester co-scheduling model aimed at minimizing the transfer costs between fields. An inter-regional collaborative optimization scheduling algorithm based on deep reinforcement learning (DRL-ICOSA) was designed. This paper first analyzes the Markov decision process of harvester collaborative scheduling operations and constructs a deep reinforcement learning environment. For the attention mechanism, a policy network and a value network based on the encoder-decoder architecture were designed to enable the model to automatically learn the complexity of the environment, effectively utilize raw data, and improve performance and generalization capabilities. Dynamic Gaussian noise was introduced into the random sampling strategy to prevent the policy network from falling into local optima during the initial training stage while enhancing the model's performance and robustness. The model was effectively trained using a proximal policy optimization algorithm. Finally, the trained model was validated on a farmland test set, and the optimal path was selected using a greedy action selection strategy, resulting in an optimized solution for cross-county harvester scheduling. To verify the algorithm's effectiveness, four combined operation scenarios were considered, based on effective operation durations of 40 and 24h, with the agricultural machinery scheduling center located at the center and edge of the operation area, respectively. The DRL-ICOSA algorithm, genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) were used to calculate scheduling strategies and conduct comparative analysis. The experimental results indicate that: when the scheduling center is located at the center or edge of the area and the effective operation duration is 40h, the DRL-ICOSA algorithm reduces the average scheduling cost by no less than 13.9% compared to the GA, PSO, and SA algorithms; when the effective operation duration is 24h, the average scheduling cost reduction is no less than 11.5%. when the operation duration is 40 or 24h and the scheduling center is located at the center of the area, the DRL-ICOSA algorithm reduces the average scheduling cost by no less than 12.3% compared to the GA, PSO, and SA algorithms; when the scheduling center is located at the edge of the area, the average scheduling cost reduction is no less than 11.5% for the DRL-ICOSA algorithm compared to the GA, PSO, and SA algorithms. Therefore, regardless of the effective operation duration or the geographical location of the scheduling center, the DRL-ICOSA algorithm consistently achieves the lowest scheduling cost compared to the other three algorithms. In summary, this study provides a more scientific and reasonable scheduling solution for the complex problem of collaborative harvester scheduling operations. The DRL-ICOSA algorithm demonstrates outstanding effectiveness in reducing scheduling costs and shows significant potential and application value in addressing the optimization problem of collaborative harvester scheduling. Compared with traditional heuristic algorithms, the proposed method is more suitable for complex environments and possesses stronger generalization capabilities. It avoids the manual feature design steps of traditional methods, thereby reducing dependence on prior knowledge. This study can effectively reduce resource waste and cost expenditure. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development and expansion of the global aquaculture industry, and the continuous enlargement of aquaculture farms, the industrialization, intelligence, and informatization of aquaculture have become a trend in the industry. China has become the largest producer of fisheries and aquaculture. Fish farming is an important component of aquaculture, and fish farming monitoring has become an important technology to enhance the efficiency, production, and management of fish farming. Fish farming monitoring can provide real-time and accurate data for farms, assisting farm managers in making decisions to improve efficiency and production. With the emergence of artificial intelligence technology in recent years, deep learning has rapidly developed and been widely applied in various fields such as image and audio recognition, natural language processing, robotics, bioinformatics, chemistry, and finance. The monitoring of fish farming focuses on the quantity, growth, behavior, and health status of fish. Using deep learning technology, we can quickly and accurately obtain information related to fish farming and enhance its efficiency and management. This paper presents a deep learning-based method for fish farming monitoring and reviews the literature progress in fish length measurement, fish counting, fish feeding, fish swimming behavior, and fish disease diagnosis. Although deep learning-based fish length measurement has achieved high accuracy in underwater environments, some errors still exist. The counting methods based on deep learning can be categorized into segmentation counting, detection counting, tracking counting, and density regression counting. Deep learning models based on video data have higher accuracy in recognizing fish feeding behavior than imagebased models. There have been many studies on fish tracking, but practical applications still face challenges such as fish feature extraction, the influence of fish size and obstructions, and occlusion issues. In fish disease diagnosis, it is necessary to establish standardized and shared fish disease datasets and utilize data fusion, data level information fusion, feature level information fusion, and decision level information fusion. This article also summarizes the main problems of deep learning-based visual technologies in fish farming monitoring from the aspects of monitoring data acquisition and transmission, dataset standardization and processing, deep learning model design, and the lack of business application in fish farming intelligent monitoring. The problems in data acquisition include a limited variety of experimental subjects, a small number of samples, and poor performance of experimental equipment. In the data transmission process, there are challenges in data security and realtime transmission. In terms of datasets, there is a low level of standardization and a lack of large-scale unified datasets. There is also a lack of research on large models and embedded models in deep learning model design. Furthermore, there is a realistic problem of insufficient business application in practical settings. The paper also proposes future research directions, including establishing fish farming monitoring datasets, super-scale parameter models for fish farming, edge computing for terminal monitoring devices, and digital twinning in fish farming monitoring, aiming to provide scientific references for the widespread application of deep learning in fish farming monitoring. [ABSTRACT FROM AUTHOR]

With the rapid advancement of economic development and urbanization, the dietary patterns of Chinese residents have undergone significant transformations, which has produced serious environmental consequences. To identify the key drivers of the carbon-water footprint of food consumption of urban and rural residents, to mitigate water depletion and to contribute to the realization of the dual-carbon goal, this paper calculated the carbon and water footprint of per capita food consumption of urban in 31 provinces and rural residents in 30 provinces in China from 2000 to 2020, compared these differences between urban and rural residents, and decomposed the driving factors of carbon footprint and water footprint by the Logarithmic Mean Index Method (LMDI). The results showed that: 1) From 2000 to 2020 years, the per capita carbon footprint of food consumption in urban China increased by 29.63% and water footprint increased by 32.94%, and carbon footprint and water footprint in rural China increased by 4.59%, and 7.91% respectively. The consumption of animal-based foods has increased and the structure of food consumption is gradually shifting towards animal-based foods. 2) From the perspective of spatial evolution, the areas with high carbon footprint and water footprint of urban per capita food consumption gradually spread from coastal provinces to inland areas, while rural areas showed a distribution pattern, that is the north and south are high and the middle is low. The change value from 2000 to 2020 of carbon footprint and water footprint in rural areas of provinces such as Qinghai, Guizhou, Hubei, Hunan, and Jiangxi are much lower than that in urban areas, which is the main reason for the spatial distribution of rural areas. 3) The driving factors include food consumption structure, the economic level (disposable income per capita), the consumption level, the population urbanization, the population size, and the food quantity that can be purchased per unit of food consumption expenditure. Specifically, the economic level is the main driver for the increase of the carbon footprint and water footprint of food consumption of urban and rural residents, and the consumption level is the opposite, which reduced the increment of carbon footprint and water footprint; the food consumption structure has driven the increase of the carbon footprint and water footprint of rural residents' food consumption in various provinces, partially for urban areas, which showed that urban areas have changed in the direction of reducing carbon and water footprints. Population urbanization has driven urban residents’ carbon footprint and water footprint, while playing a restraining role in rural areas, mainly due to the migration of the population to urban areas. Similarly, the population size has driven the increase of carbon and water footprint of food consumption, while the provinces such as Gansu, Jilin, and Heilongjiang are inhibiting effects, mainly because of the population emigration and difficulty in returning floating population. Food quantity that can be purchased per unit of food consumption expenditure has played a restraining role in the demand theory that with the price increase, the demand could be reduced. Finally, this paper put forward suggestions from the perspectives of improving dietary structure developing sustainable dietary guidelines for Chinese residents, and reducing economic disparities between different provinces, aiming to promote sustainable food consumption among urban and rural residents in China. [ABSTRACT FROM AUTHOR]

The mobile flume has the advantages of not easy to be silted up and can be moved and reused in many places, etc. Compared with the fixed type, the streamlined shape of the measuring flume has good hydraulic performance, and the diffusion section of the flume has a great influence on the hydraulic performance, however, there is a lack of research on the design principle and method of the diffusion section at present. In order to study the influence of flume structure on hydraulic properties, and to provide theoretical guidance for the design of streamlined flume structure, This paper establishes a D40Ushaped channel simulation model, and then uses FLOW-3D to simulate the semi-elliptical with semi-elliptical, semi-elliptical with parabolic, semi-elliptical with arc, semi-elliptical with Myring curve, and fish-shaped flume under flow rates of 35, 45, and 55 L/s. Furthermore, the accuracy effect of numerical simulation is verified by prototype test, and then the design principle and method of diffusion section are discussed. Finally, based on the isight platform, the semi-elliptical with Myring curve and fish-shaped flume with better hydraulic performance are optimized. The results show that the relative error between the numerical simulation and the experimental water depth is 0.1%-1.3% and the relative errors of velocity in upstream, throat and downstream sections are 0.19%-6.63%, 0.06%-6.15% and 0.02%-6.22% at 45 L/s flow rate, respectively. The numerical simulation results have high accuracy. The Froude number in the upstream of the five flumes is less than 0.5, and the maximum backwater height is 5.71 cm, which meets the requirements of flow measurement accuracy and channel safety. Among them, the head loss and backwater height of the semi-elliptical with Myring curve flume are the lowest, the critical submergence degree of the semi-elliptical with semi-elliptical flume is the highest, and the Froude number is the smallest. The design principle of the diffusion section of the flume is: under the condition of slightly reducing the critical submergence degree, the first half of the diffusion section is designed as a curve with larger curvature to reduce the upstream backwater; the second half is designed as a curve with opposite curvature and smaller curvature to improve the flow state, smooth the water flow and reduce the head loss. After optimization, the curvature of the first half of the semi-ellipse with Myring curve and the fish-shape flume increases, and the curvature of the second half decreases, the backwater and Froude number are reduced, and the head loss is reduced by 5.5% and 6.3%, respectively. The study can provide theoretical guidance for the design and optimization of streamlined measuring tank structures. [ABSTRACT FROM AUTHOR]

Cultivation and sowing agricultural machinery equipment plays a pivotal role in influencing the quality and efficiency of cultivation operations across 133 million hectares of arable land in China. The rapid advancement of agricultural mechanization has led to heightened demands for enhanced wear resistance and extended service life of soil-engaging components in cultivation and sowing agricultural machinery, including rotary tillers, plowshares, deep loosening shovels, rake blades, and trenchers. Consequently, there is a paramount need to bolster research efforts aimed at enhancing the surface wear resistance of soil-engaging components in agricultural machinery and equipment. Building upon this premise, this study provides a comprehensive overview of the current research status regarding the enhancement of surface wear resistance in soil-engaging components within agricultural machinery and equipment. Initially, the primary forms of wear and failure in soil-engaging components of agricultural machinery and equipment are elucidated, encompassing abrasive wear, fatigue wear, and corrosion wear. Concurrently, the wear mechanisms of soil-engaging components in agricultural machinery, influenced by external factors such as abrasive particle shape, external load, soil moisture content, and pH value, are delineated, alongside an overview of diverse research methodologies concerning these mechanisms. Subsequently, detailed elaboration is provided on the progress in research and development of surface treatment processes, including surface melting, welding, thermal spraying, laser surface strengthening, chemical heat treatment, and brazing. Furthermore, an exploration and analysis of the disparity in wear resistance and strengthening technology for soil-engaging components between China and foreign nations are conducted from four perspectives: wear mechanism, material research and development, structural design, and manufacturing processes. Although domestic enterprises and researchers have conducted significant research, a noticeable disparity persists between soil-engaging components in domestic tillage and broadcasting equipment and their foreign counterparts. The research and development system remains imperfect, and there is a misalignment between promotion and application, thus hindering the formation of an internationally competitive industry chain for wear-resistant soil-engaging components. Finally, addressing the current challenges and deficiencies in the failure mechanisms, materials, structures, and processes of soil-engaging components in tillage and sowing machinery, this paper discusses the research and development directions aimed at enhancing wear resistance in agricultural machinery and equipment. 1) Enhance research on the wear resistance of soil-engaging components in diverse operational environments of tillage and sowing machinery; 2) Utilizing tribological theory, analyze the interaction laws of "environment, component, and material," thereby intensifying the development and research of substrate and coating materials for soil-engaging components; 3) Improve the optimization design of soil-engaging component structures to fulfill requirements for wear resistance, longevity, drag reduction, and lightweight properties; 4) To ensure the synchronized attainment of precision, strength, toughness, wear resistance, and prolonged service life in soil-engaging components, research on the process system of such components will be fortified, thereby furnishing theoretical foundations and technical support for research and engineering applications pertaining to enhancing surface wear resistance. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) remote sensing is widely used in land cover classification, but at present, object recognition of UAV images in farmland environment is cumbersome, requiring manual image stitching to preprocess the image, and finally training the model for recognition. In order to improve the computing speed and efficiency of UAV images in farmland environment, this paper proposes a parallel Mosaic recognition algorithm for UAV images in farmland environment. Firstly, based on SURF algorithm, KNN algorithm and RANSAC algorithm, the panoramic image Mosaic of UAV based on image transformation is realized. Then the inverted binary tree parallel processing algorithm is proposed. The parallel processing algorithm is planned by the idea of divide and conquer, and the image Mosaic recognition task is automatically divided into multiple sub-processes according to the number of CPU cores of the edge device and the number of images. And they are distributed to different computing cores to execute, and to improve the computing efficiency in the field environment. Finally, by extracting the transformation matrix in image Mosaic, the recognition was realized at the same time in the process of image Mosaic, and the recognition results were also spliced by using the transformation matrix. The parallel splicing recognition algorithm greatly improves the efficiency of UAV image splicing recognition, which is of great significance to realize real-time monitoring of UAV images. The experimental data were taken in the Science and Education Park of Henan Agricultural University and the Pest Field Observation Station base of the Ministry of Agriculture and Rural Affairs in Korle, Yuanyang County, Henan Province. In order to increase the diversity of data, Phantom4RTK and Mavic3E were used to avoid the influence of different Uavs on the Mosaic effect. The collected images are RGB three-band images, and the shooting height is about 20 ～100m. The resolution of the Sprite 4RTK camera is 5 472×3 648, and the resolution of the Imperial 3E camera is 5 280×3 956. The CPU model of the equipment selected for the experiment is i7-11700, the frequency is 2.50 GHz, there are 8 cores and 16 logical processors, and the memory is 64GB. The graphics card of the model training device is NVIDIA A100-PCIE, the neural network is U-Net, and the development framework is Pytorch. The training epoch is 300 rounds, and the backbone network is frozen for training in the first 50 rounds to avoid too much data noise. The batch size (batch_size) is set to 16 and the Loss function is the Cross Entropy Loss (CE). Model training significantly reduces the loss value after the start of training and converges at 150 rounds and becomes stable at 200 rounds. The final mIoU of the model training was 89.01%. We use the reverse binary tree parallel stitching algorithm and other stitching algorithms for comparative experiments. The experimental results show that under the same experimental environment and data set, using the inverted binary tree parallel stitching scheme, the stitching time is reduced by about 60%～90% on average compared with commercial software. We test the inverted binary tree parallel splicing recognition algorithm in a farmland environment, and deploy the initially trained model to the device. The experimental results show that compared with the serial Mosaic recognition, the parallel Mosaic recognition of inverted binary tree not only reduces the time consumption by 70%, but also improves the mIoU of image recognition by 10.17%. It shows that in the farmland environment, the multi-threaded inverted binary tree parallel scheme can make better use of the computing resources of edge devices in the farmland environment, greatly improve the speed of UAV image stitching and recognition, and provide technical support for rapid real-time monitoring of UAV. [ABSTRACT FROM AUTHOR]

Here the mild and simple enzymatic hydrolysis was developed to prepare the plant polysaccharides with the high purity. The pepsin (endopeptidase) and koplase (telopeptidase) were used as the complex enzymes. Kinetic models were established for the enzymatic hydrolysis. Water extraction was used to prepare the polysaccharide at 45 ℃ and 90 ℃. Also included are pepsin extraction and double enzyme extraction. Nutritional components of polysaccharides extracted by these four methods were characterized by their structure (monosaccharide composition, infrared spectrum, thermal features, low field nuclear magnetic field, and dynamic thermomechanical properties). In this paper, the kinetic models of single enzyme (pepsin)/and double enzyme segmental hydrolysis were constructed respectively. The highest contents of polyphenols (0.42±0.008 mg/mL) and uronic acid (15.65%±0.98%) were obtained in the polysaccharide of lotus seed that extracted by double enzyme. While the lowest protein content (0.73%±0.24%) was found. Among them, uronic acid was a derivative of sugar. The high contents of uronic acid and galactose were detected in all the polysaccharides that extracted from lotus seed, indicating the acidic polysaccharide. Glc, Ara, Man and Rha were contained in the four kinds of lotus seed polysaccharides, among which more Gal-UA and Man were found in the polysaccharides that extracted by double enzyme, which were 10.700 and 10.752% respectively. This acidic polysaccharide was benefit to the dissolution of active polysaccharide. The four samples of lotus seed polysaccharide also shared the similar infrared spectral characteristic peaks, αtype pyranose. The wider characteristic peaks of 1654 cm-1 were observed in 45 ℃ water extraction, indicating the more bound water. The double enzyme was also used to break the binding between polysaccharide and protein. The highest content of bound water was detected with the less fixed and free water in the double enzyme after water distribution analysis. It infers that the double enzyme extraction was more beneficial to the stability of polysaccharide structure. In addition, only the first-order relaxation was found in the polysaccharide of lotus seed that treated by double-enzyme. The energy storage modulus and loss modulus were also higher than before. The glass transition temperature was dropped to about 60℃ (close to that of plant polysaccharide). In summary, the double enzyme extraction can be expected to effectively reduce the protein content and Tg of lotus seed polysaccharide, compared with conventional methods. The content of bound water and hydrophilic ability were improved to extract the active components. The specific gravity values of polysaccharide aldehyde and rehydration were also improved for the purity of polysaccharide. The kinetic model of enzymatic hydrolysis can also provide the theoretical support to the purification of lotus seed polysaccharides, in order to understand the degradation of lotus seed protein. The two enzyme extractions can share the stronger specificity and simple steps, compared with the high-temperature ones. The mannan polysaccharide components can be extracted to improve the polysaccharide purity. Therefore, the enzymatic hydrolysis of lotus seed polysaccharide can ensure the protein to release from the polysaccharide. The effective and moderate hydrolysis can be achieved for the synchronous separation of protein and polysaccharide in the step of polysaccharide alcohol precipitation. The mathematical models were further simulated and validated, according to the mechanism of enzymolysis reaction. The kinetic models of enzymatic hydrolysis with single and double enzymes were also established to determine and characterize the nutritional structure of polysaccharide products between the low/high temperature extraction and low temperature enzymolysis. This finding can also provide the theoretical guidance for the further development and utilization of animal and plant polysaccharides. [ABSTRACT FROM AUTHOR]

Under the background of rapid urbanization, promoting high-quality land use transformation is an important way to achieve sustainable and coordinated development of regions. The transformation of regional land use function is the inevitable result of rapid urbanization and one of the important characteristics of regional spatial function transformation and reconstruction. This study was based on the district and county scale and the dominant function of land use, and used synthetically index method, coupled coordination degree model, and geographically and temporally weighted regression model (GTWR). This paper analyzes the spatio-temporal evolution track and spatial equilibrium state of land use function transformation in the urban agglomeration around Poyang Lake under a long time series, divides the transition stage of each county and region, and reveals the driving mechanism of land use function transformation. The results show that: 1) From the perspective of temporal changes, the land use function index of the urban agglomeration around Poyang Lake increased from 0.20 to 0.55 during 2000-2020, with continuous optimization and improvement, but it was still at a medium and low level in general, showing staged characteristics and experiencing a development process of slow developm-rapid increase and slow growth. From the perspective of spatial distribution characteristics, the functional index gradually presents a spatial distribution pattern of high in the east and west, low in the east, showing a significant spatial difference. 2) The index of coordination degree of production-life -ecological function and production-life function continued to rise, while the other coupling degree and coordination degree index showed a trend of fluctuating development. The production-life function showed a low level and high coupling phenomenon from 2000 to 2010, and the life and ecological function lagged behind the development of production function from 2010 to 2015. The coupling coordination index showed a spatial distribution pattern of high in central and southwest, low in northeast and northwest, and the gap between regions and counties widened. The overall transformation is mainly low-quality, with the average number of districts and counties accounting for about 50%, but the number of districts and counties in the transition period and high-quality transformation has increased, and the quality of functional transformation has improved. The quality of land use function transformation in the central and western regions is obviously better than that in the northern and eastern regions. The high-quality transformation regions are mainly located along the Chang-jiu Economic Corridor and Xinyiping city cluster, while the eastern and northern regions are mostly low-quality transformation. 3) There are significant spatial differences in the driving effects of different influence factors. The positive driving effect of population density and urbanization level continued to increase, the negative driving effect of the proportion of secondary and tertiary industries increased, and the influence of per capita GDP, per capita fixed asset investment and per capita grain output tended to weaken. The Poyang Lake city cluster should implement differentiated development policies according to the transition stage of land use function of each district and county, make up for the development shortcomings of district and county, and enhance the coupling and coordination degree of functional systems. The research results can provide reference and decision-making basis for improving regional land use efficiency and promoting multi-functional cooperative development of land use. [ABSTRACT FROM AUTHOR]

In 2020, the Xinjiang Production and Construction Corps (XPCC), as a typical representative of oasis agriculture in Northwest China, produced 2.41 million tons of grain, with a total planting area of 14.15 million hectares. Cotton, corn, and wheat accounted for 61.14%, 8.25%, and 7.98% of the total planting area respectively. The systematic evaluation of nitrogen and phosphorus utilization efficiency in regional farmland and analysis of nutrient management issues have become important topics for sustainable agricultural development. Therefore, this paper focuses on the agricultural production system in the XPCC, aiming to elucidate the characteristics of nitrogen and phosphorus nutrient flow in farmland and calculate the surplus and efficiency of nitrogen and phosphorus nutrients from 2000 to 2020. Additionally, the Spearman correlation analysis method was used to explore the synergistic relationship between nutrient management and production levels, economic benefits, and environmental benefits. A comparative country analysis was carried out. The results showed that: 1) From 2000 to 2020, farmland nitrogen input and output in the XPCC both showed an initial increase followed by a stabilization trend. In 2020, farmland nitrogen input and output were 4.58×108 kg and 2.49×108 kg respectively. Chemical fertilizer was the main source of nitrogen input, accounting for 87.37% of total nitrogen input. Straw, grains, and ammonia volatilization were the major pathways for nitrogen output, accounting for 42.89%, 24.63%, and 20.16% of total nitrogen output respectively. Farmland phosphorus input exhibited an initial increase followed by a stabilization trend, while phosphorus output showed a continuous increase trend. Farmland phosphorus input and output were 1.35×108 kg and 0.68×108 kg respectively. Chemical fertilizer accounted for 94.1% of total phosphorus input. Grains and straw were the primary pathways for phosphorus output, accounting for 70.43% and 28.31% of total phosphorus output respectively. The farmland nitrogen surplus showed a fluctuating increase trend from 2000 to 2020, increasing from 91.90 kg/hm² in 2000 to 147.54 kg/hm² in 2020, representing a 61% increase. The phosphorus surplus exhibited an initial increase followed by a decreasing trend, rising from 26.57 kg/hm² in 2000 to 61.26 kg/hm² in 2015 and then declining to 47.61 kg/hm² in 2020. The relatively high farmland nitrogen and phosphorus surpluses in the XPCC posed a high risk of environmental pollution. Farmland nitrogen and phosphorus utilization efficiency from 2000 to 2020 showed stable fluctuations, ranging from 34.33% to 44.05% for nitrogen and from 39.25% to 49.54% for phosphorus. Nitrogen and phosphorus utilization efficiency were relatively low, indicating room for improvement. 2) There was a trade-off effect between farmland nutrient utilization and production levels, economic benefits and environmental benefits in the XPCC, while there was a synergistic effect between production levels and economic benefits. The high yield of agricultural mechanization production in the XPCC was at the expense of nutrient loss and environmental pollution. 3) The nutrient utilization level in developed countries was relatively high, which synergistically promoted the production levels and economic benefits without sacrificing environmental benefits. However, the nutrient utilization in Central Asian countries and the XPCC presented a trade-off effect with the production levels and economic benefits. In conclusion, two recommendations were proposed for farmland nutrient management. Firstly, based on the farms, clear reward and punishment systems should be established, nutrient accounting and management were initiated from high-standard farmland, and promoted smart agricultural technology to control fertilizer and water. The science and technology backyard could be employed to lead and demonstrate nutrient management, facilitating technology dissemination and application. Secondly, regional crop and livestock structure and layout should be optimized, sufficient equipment was provided for manure fermentation and fertilizer application, production mechanization, and the use of chemical fertilizers reduced. [ABSTRACT FROM AUTHOR]

In order to keep up the research dynamics in the field of nitrogen monitoring in wheat, this paper explores the current status and content evolution through visual analysis. Using bibliometric methods, we mined the trend of publications, subject areas, countries, institutions, journals and highly cited papers, research hotspots, and theme evolution between 1990 and 2022, and analyzed the intersection of disciplines and the concentration of literature. The research found that the global publication volume is generally on the rise, and China is the fastest growing country in recent years, but it can be further improved in terms of the number of local citations and the total number of citations. The field of wheat nitrogen monitoring has changed from one single discipline to multidisciplinary, the hotspots have evolved from the analysis of a single physiological process to the monitoring of remote sensing, crop modeling, growth simulation, and other directions. The research hotspots have evolved from the early stage of single physiological process analysis to the direction of digital agriculture, forming two major research nodes in the world represented by China and the United States. Six of the top ten authors are from China, and China occupies six seats in the top ten organizations, and China ranks in the top in all four indicators of highly cited papers. As time goes on, the tendency of teamwork and international cooperation has become evident, and the proportion of collaborative publications between China and the top ten countries is 31.48%. Agronomy journals are the main publications, with Field Crops Research being the most representative. The results show that the cross-disciplinary trend has become more and more obvious, the development trend of research hotspots and fields has become clearer and clearer, and in the future, the in-depth cooperation among different countries, disciplines, and teams will bring greater and more far-reaching opportunities. [ABSTRACT FROM AUTHOR]

The water hammer simulation for pump station water supply systems with free-surface flow commonly employs the method of characteristics (MOC), often neglecting the influence of unsteady friction and the interaction between pressure and free-surface flow. In complex pipeline network systems, MOC often requires interpolation calculations or adjustments to wave speeds for computation. However, this can lead to issues such as numerical instability and reduced accuracy. This study establishes a second-order finite volume method model that considers unsteady friction for real-time joint calculations of pressure and free-surface flow. The second-order Godunov scheme is utilized to discretize the flow control equations independently for the pressure and free-surface flow. Riemann solvers are employed to compute the discretized fluxes. Simultaneously, the unsteady friction effect is taken into account to accurately simulate energy dissipation during hydraulic transient processes. The model is validated using experimental data. And a variable time-step approach is adopted at the interface between the pressure and free-surface flow. Furthermore, sensitivity analyses are conducted on the dimensions and locations of the throttled surge chamber in practical engineering scenarios. The results indicate that when unsteady friction effects are not considered, the model only matches experimental values at the first peak. However, when unsteady friction effects are taken into account, the model's simulation results are in good agreement with experimental results in terms of pressure peak, decay, and periodicity, and the maximum value of the water hammer pressure error at the valve is only 4.58%. Compared to the characteristics method, the second-order Godunov scheme exhibits higher stability and produces simulation results that are essentially the same when the Courant number is less than 1 as when the Courant number is set to 1.0. The real)time joint calculation method established in this paper can adequately consider the impact of water level fluctuations of the pressure flow outlet pool and the backflow of the free-surface flow. The water hammer pressure and the water levels of the throttled surge chamber obtained from the joint calculation of pressure and free-surface flow exhibit a faster decay compared to the results obtained from pure pressure flow calculations. For power outage conditions of pumps, the engineering scenarios studied in this paper would result in significant water hammer pressures and negative pressures if surge chambers were not installed. Optimization studies on the throttled surge chamber show that as the diameter of the connection tube increases, the maximum pressure head, the highest surge level of the throttled surge chamber, and the maximum reverse rotation speed of the pump increase, and the lowest surge level of the throttled surge chamber decreases. As the diameter of the throttled surge chamber increases, the highest surge level decreases, the lowest surge level increases and the maximum pressure decreases all the time when the connection tube diameter is relatively small, and will decrease first and then increase when the connection tube diameter is relatively large; when the connection tube diameter 3.5 m, throttled surge chamber diameter 15 m can meet the requirements of the system control parameters and also reduce the amount of engineering work. The closer the throttled surge chamber is to the pump station, the larger the maximum pressure, reverse rotation speed, and highest surge level, and the smaller the lowest surge level. When the throttled surge chamber is arranged 1.0 km behind the pump, the maximum surge level will reach 172.35 m, which does not meet the requirements of the control standards. In summary, the established model exhibits high accuracy, stability, and applicability, providing valuable insights for water hammer simulations in pump station water supply systems with free-surface flow. [ABSTRACT FROM AUTHOR]

Rice shrimp co-cultivation, as a circular agriculture model that integrates ecological, economic, and social benefits, has been widely applied in China. Accurately obtaining information on the planting area, spatial distribution, and changes of rice shrimp co-cultivation is of great significance for ensuring safe production, social supply of rice and shrimp, and formulating corresponding management and planning policies by government departments. Most of the existing methods for extracting rice shrimp farming fields used medium-resolution temporal images as data sources to analyze the characteristics of water or vegetation changes within a year to construct models. The accuracy of the extraction depended on the results of water or vegetation analysis, the boundaries were unclear and there was a lot of noise. To solve the problems of complex temporal analysis, low accuracy of extraction results, and incomplete boundaries in existing extraction methods, this paper used high-resolution images of a single temporal phase as the data source, without analyzing the temporal features of rice shrimp farming fields. Only the spatial features of rice shrimp farming fields on high-resolution images were used for high-precision extraction. We proposed a deep learning semantic segmentation model named EASNet (edge assisted segmentation network), which was mainly composed of three modules: feature extraction module (FE Module), edge assist module (EA Module), and information fusion Module (IF Module). The FE Module was used to extract multi-level and fused contextual semantic information features of the target object. The EA module used the unique edge "shrimp groove" of rice and shrimp farming fields as an auxiliary information for separate segmentation in the designed EA Module. The IF Module integrated the outputs of the FE Module and the EA Module, enabling the main task to not only enhanced the boundary structure information of rice shrimp farming fields but also learned the unique spatial and semantic information of rice shrimp farming fields. The experimental results showed that with the enhancement of the EA Module, the segmentation results of rice shrimp farming fields were more complete and the boundaries were clearer. The IoU (Intersection over Union) of semantic accuracy and the F1-Score of boundary accuracy were improved by 1.5% and 5.8%, respectively. The recall, IoU, and F1-Score of overall semantic accuracy reached 0.970, 0.964, and 0.930, respectively. The Recall and F1-Score of boundary accuracy reached 0.864 and 0.859, while the Recall and F1-Score of relaxed boundary accuracy reached 0.876 and 0.913. The trained EASNet model was applied to the whole area of Xuyi County, and the spatial distribution map of rice shrimp farming fields in Xuyi County in 2020 was obtained. In comparison with the results of rice shrimp breeding fields extracted by the traditional Water Seasonal Difference method and the Random Forest method, our method obtained the optimal results with an OA(overall accuracy) of 96.71% and a Kappa coefficient of 0.934. The method used in this paper has higher accuracy, lower missing rate, more regular boundaries, and fewer instances of similar integration in extracting results. It can provide a basis for natural resource surveys, and government departments to formulate corresponding rice shrimp breeding management and planning policies. [ABSTRACT FROM AUTHOR]

The number of trees is essential information for modern forestry management, which affects managers' formulation of development strategies. At present, tree inventory mainly relies on manual counting, which is costly, time-consuming and labor-intensive. Using unmanned aerial vehicle (UAV) to count targets has become increasingly popular in agriculture and forestry due to its low cost, ease of operation, and flexibility of use. This study focused on using UAV images to count spruce numbers. A total of 603 images with an average canopy density of 81% and an average plant density of 6 667 plants/hm² were selected for the dataset, each containing an average of 354 spruce trees. Among these, 205 images had interference factors such as weeds. The images were divided into a training set and a test set at a ratio of 7:3. The training set was expanded by randomly flipping the images to improve the robustness to different flight attitudes of UAV. After data augmentation, 844 training images and 181 test images were obtained. Aiming at the problem of dense spruces in natural environments including severe adhesion and background interference such as weeds with similar characteristics to spruces, we selected IntegrateNet, a model known for its strong performance in dense target counting tasks as baseline model for spruces. This study then worked to improve the IntegrateNet model to achieve a more accurate counting of dense spruce that is closer to real-world conditions. First of all, this paper used the self-calibrated convolutions (SCConv) to replace the ordinary convolutional layers at 1/8, 1/16 feature maps and density maps in the baseline model to expand the convolution receptive field. Secondly, in order to deal with background interference problems such as weeds and the serious adhesion of the target, this paper added the criss-cross attention mechanism (CCA) to the feature fusion of the IntegrateNet model. It can consider the horizontal and vertical context information of each pixel to generate richer semantic features to improve the contextual information extraction ability of the model. The mean counting accuracy(MCA), mean absolute error(MAE), root mean square error(RMSE), and the coefficient of determination R² are used as evaluation indicators. The ablation experiments and comparative experiments are designed to verify the performance of the proposed model. The ablation experiments show that the improved methods proposed in this paper can effectively improve the counting accuracy of the model. The comparative experiments show that the improved IntegrateNet model proposed in this study has MCA, MAE and RMSE reached 98.32%, 8.99 plants and 13.79 plants respectively, and the R² was 0.99.Compared with TasselNetv3_lite, TasselNetv3_seg, and IntegrateNet models, the MCA of the improved IntegrateNet model increased by 16.44, 10.55, and 9.26 percentage points, the MAE of the improved IntegrateNet model decreased by 25.62, 10.45, and 6.99 plants, and the RMSE of the improved IntegrateNet model decreased by 48.25, 13.84, and 12.52 plants. In summary, the improved IntegrateNet model proposed in this study demonstrated a significant increase in accuracy for counting dense spruce in natural environments. Moreover, our research is not limited to spruce, as the methodology can be applied to other tree species as well. It can providing technical support for subsequent forestry intelligent statistics. [ABSTRACT FROM AUTHOR]

Food is the top priority for the people, and food safety has always been a key concern for the people's livelihood. Building a Knowledge graph for food safety and food events has attracted many scholars' attention in recent years. One of the key technologies to build a large structured knowledge graph is to extract triples from unstructured text. A triplet typically consists of a pair of entities and semantic relations between them. When extracting entities and relationships from Chinese food inspection announcements, many long sentences often appear in the text, which contain a large number of food names and food types. If we learn about their features by using the current deep learning models, the strong dependencies between words will be weakened by the long distance between words, this issue has received little attention in the extraction of entity and relationships in the food safety field. So to solve the problem of dependency relations between words in those sentences, this paper proposes a joint entity relation extraction model (TAG-JE) based on improved Chinese dependency syntax tree and multi feature fusion. TAG-JE improves the expression of Chinese dependency syntax tree that is adding a "CLS" tag before the natural sentence and establish dependency relations between the "CLS" tag and each word in the sentence. Then we use it to describe the syntactic structure of text, and converts the syntax tree into a character adjacency graph so as to match the syntax tree with the word processing mode of Chinese BERT encoding. Then we learn syntactic structure features from the encoded word adjacency graph through a graph neural network(GCN), and fuse them with the context features extracted by BERT. Context features and structural features can describe the linear and spatial representation of sentences. The weight of the fusion is automatically adjusted by a gate network. After the above steps, the fusion features of unstructured text will be obtained which can fully reflect the representation of the food inspection announcement. In the process of entity and relationship extraction, the joint extraction mode is adopted, because compared to separate extraction of entities and relationships, joint extraction reduces error accumulation and TAG-JE also adds a relation selector in relation analysis, relation selector is used to optimize the embedding features of relations, and the relation selector would be trained by reinforcement learning to improve the accuracy of relation extraction. To verify the effectiveness of TAG-JE, this paper compared its extraction performance with mainstream deep learning models such as BiLSTM+CRF, combined with IDCNN, BERT, and Attention to obtain three BiLSTM+CRF framework models. In addition to the classic models mentioned above, this paper also compares the TAG-JE model with mainstream models GraphRel and CasRel. The results showed that the accuracy, recall, and F1 values of TAG-JE reached 90.86%, 90.50%, and 90.68%, respectively, which showed a significant improvement compared to other models and demonstrated its knowledge mining ability in Chinese food inspection documents. TAG-JE provides technical support for the construction of food safety knowledge graph. In the experiments targeting Chinese public datasets, TAG-JE also achieved better results compared to GraphRel and CasRel, proving that TAG-JE also has good generalization performance. The research results provide reference for the construction of food safety knowledge graph. [ABSTRACT FROM AUTHOR]

In arid areas, the rapid expansion of agricultural activities has caused a series of ecological and environmental issues such as vegetation degradation, declining groundwater levels, and land desertification, posing significant challenges to the stability and development of inland river basins. The development of agricultural water suitability is the key to ensure the water and land resources efficiency and ecological health in arid areas. The core is how to implement the scientific control of irrigation scale and agricultural irrigation water consumption. Taking the Shiyang River Basin as the study area, this paper analyzed the variations of high and low flow for surface runoff in the basin based on the anomaly percentage and the runoff modulus ratio coefficient. After estimating the non-agricultural irrigation water demand, the water balance model was employed to calculate the available surface water and groundwater resources specifically for agricultural irrigation, while also considering water transformation process. Based on this, a multi-objective planting structure optimization model was established to determine the water-saving and efficient crop planting structure. To determine the appropriate irrigation scale in arid areas, a comprehensive model was constructed by improving the water-heat balance model, which incorporates various factors such as the water transformation process, crop coefficient in different growth stages, agricultural water saving level, and crop planting structure. Lastly, the suitable irrigation scales of the Shiyang River Basin under different scenarios were explored by using this model. The results showed that the runoff in Shiyang River Basin presented obvious high and low flow variations during the studied period. Specifically, the basin’s total surface water resources amounted to 17.47×108, 14.19 ×108, and 12.25×108 m³ during high, normal and low flow years, respectively. Regarding non-agricultural irrigation water demands, the critical and suitable ecology water demand scenarios were 3.43×108 and 5.57×108 m 3, respectively. Following the fulfillment of water resources demand for non-irrigation sectors, and under the joint regulation of surface water and groundwater resources, the available water consumption for agricultural irrigation were 18.97×108 -21.75×108, 14.75×108 -17.51 ×108, and 12.31×108 - 14.95×108 m³ in high, normal and low flow years, respectively. With the improvement of water saving level, the decrease of groundwater recharge led to the decrease of irrigation water consumption in the basin. After optimizing the crop planting structure, 14.13% of irrigation water saving can be achieved by reducing the economic benefit of 2.94%. The suitable irrigation scale determined by the suitable irrigation scale calculation model in arid areas considering the water transformation process was consistent with the actual situation, which is more reasonable than the conventional method in the inland river basin. Based on this method, the suitable irrigation scales of Shiyang River Basin under the current condition were 27.73×104 -31.66×104, 21.55×104 -25.76×104, and 18.01×104 -22.03×104 hm² in high, normal and low flow years, respectively. After improving the water-saving level and adjusting the planting structure, the suitable irrigation scale of the basin has increased. In 2020 (normal flow year), the actual irrigation area exceeded the critical suitable irrigation scale, necessitating reducing the irrigation area of 2.13×104 -6.34×104 hm² to balance the water and soil resources in the basin. The research results provide a theoretical foundation for decision makers to formulate water-appropriate agricultural development plans at the macro level. [ABSTRACT FROM AUTHOR]

The adoption of blockchain technology can guarantee the safe and reliable storage of IoT data. In the agricultural products traceability system based on blockchain + Internet of Things, the storage method used in most existing schemes is to save the original data in the off-chain database system, but the real data cannot be agreed on the blockchain. However, if all the data in the production Internet of things are stored directly on the blockchain, it will cause great storage pressure. In order to balance the reliability and storage pressure of the IoT data stored on blockchain in the agricultural industry chain, a high-value density blockchain compression storage scheme for agricultural IoT data is proposed. This paper connects all production links of the agricultural product chain based on consortium blockchain and collects field data based on the Internet of Things composed of sensors. Firstly, a blockchain storage framework for agricultural Internet of Things data is proposed. The collected Internet of Things data is summarized through gateways or edge server devices before being stored in the blockchain. The value density of on-chain data is increased by compression method. An adaptive lossy compression method with outliers is designed for the data compression process. The agricultural IoT data is divided into normal values and outliers based on the outlier separation method. According to the time correlation between sensor data and the sparse of abnormal data, an adaptive compression algorithm for normal data and outlier data compression algorithm is designed to compress normal data and outlier data respectively. Compressed data are stored in the blockchain instead of original data, reducing storage space occupation; The application service platform can obtain the data on the blockchain and reconstruct the data that meets the accuracy need. The results of experiments show that when the proposed scheme is used for data compression, k values can be adaptively selected for different data sets, and good compression effects can be obtained, on-chain storage space can be reduced, and the value density of data can be improved, satisfying the reconstruction accuracy of users. This method has a good compression effect for data with low change frequency, such as temperature and humidity data, but fails to achieve a high compression effect for data with high change frequency such as wind speed data. In the scheme, storing data on the blockchain can solve the problem of traceability data caused by off-chain storage. In view of the storage redundancy caused by storing all the data on the blockchain, lossy compression method is adopted to reduce the data volume and improve the value density of on-chain data within the acceptable error range of the Internet of Things data provider. Experiments show that suppose the compression ratio of normal data is 10:1, this method saves about 85% of on-chain storage space and increases the corresponding data value density by about 85%. The scheme only stores data with a compression factor through the blockchain network, reducing the storage and management overhead of on-chain data. At the same time, the scheme has the characteristics of availability, data traceability and non-repudiation. Apart from being a mere technical solution, this research endeavor emerges as a robust response to the imperatives of secure and reliable IoT data storage within the agricultural product traceability landscape. Looking ahead to the agricultural product traceability scenario based on blockchain, this innovative solution calls for the possibility of harmonious integration with technologies such as sidechain and data offloading. This integration will further mitigate blockchain storage redundancy while ensuring data consensus on the blockchain. The ultimate goal is to ensure the transparency and traceability of the entire agricultural product data traceability process. With the development of the industry and the continuous progress of the technology field, it provides technical support for the realization of distributed agricultural Internet of Things data compression storage. [ABSTRACT FROM AUTHOR]

In order to provide new technical support for the variable temperature drying process and realize the adaptive control of drying temperature based on the change of moisture content, the study designed a temperature adaptive control system with the function of online detection of material moisture content. A moisture content online detection model with weight detection value, air velocity, the temperature of load sensor elastic substrate, airflow impingement distance as inputs, and the real material weight as outputs was established by using a convolutional neural network. A validation test of the moisture content online detection model was carried out. The results showed that the model meets the accuracy requirements of online moisture content detection in the variable temperature drying process, and the coefficient of determination R² and root mean square error (RMSE) of the five groups of model validation tests were 0.993 4 and 1.20% in that order. In this reasearch, an improved neural network-PID (INN-PID) controller was designed to realize temperature control in the variable temperature drying process. The dynamic performance of PID, neural network-PID (NN-PID), and INN-PID controllers was simulated in MATLAB software with unit step signal as input. The three controllers were tested for drying temperature control at 50-55 ℃. The results showed that the control stability and regulation time of the INN-PID controller were significantly better than the other two controllers in the simulation test, the drying temperature control results had approximately the same law with the simulation results, and the peak time of the INN-PID controller was 208.00 s, the regulation time was 120.59 s, and the maximum overshooting was 4.87%, which meets the requirements of temperature control in the variable-temperature drying process. In this paper, a temperature adaptive control system was built in the air-impingement dryer, and the temperature adaptive control test based on the moisture content change was carried out. The results showed that the system could quickly and effectively regulate the drying temperature in the variable temperature drying process based on the change in moisture content. This research is of great significance for improving the automation level of drying equipment, developing new variable-temperature drying processes, and providing reference for multi-information fusion detection and control strategy research in other fields. [ABSTRACT FROM AUTHOR]

Autonomous navigation is an important guarantee for intelligent agricultural machinery to complete harvesting operations. Navigation line recognition is an important step in achieving autonomous navigation of intelligent agricultural machinery. Real-time and accurate detection of harvesting navigation lines can effectively accelerate the progress of operations and reduce crop loss rates. Visual sensors have the advantages of low cost and rich information acquisition, making them widely used in route detection. Frequent changes in lighting under cloudy weather make it difficult for a single feature to cope with wheat field environments. In this study, a wheat harvesting boundary detection method based on multi texture feature fusion was proposed. In the harvested area, the characteristics of low stubble and no top wheat ears result in differences in uniformity, density, and other aspects when harvesting under both light and backlight compared to the non-harvested area. At the same time, due to the exposed wheat stubble in the harvested area, it appears more regular on the image, and its gradient direction is more consistent than that in the non-harvested area. Therefore, a two-dimensional feature vector composed of image entropy and directional gradient was constructed to classify the harvested and unharvested areas of the wheat field, and then the harvesting boundary was extracted. Due to the susceptibility of image entropy features to perspective phenomena, this paper compared the entropy features of different regions in the image when fixing the camera installation angle and height, and ultimately selected the middle region of the image as the region of interest. After analyzing the characteristics of image entropy feature extraction, a histogram statistical method based on sliding windows was proposed to accelerate the speed of image entropy feature extraction. By further dividing the window to be calculated into several sub windows, sliding and combining these sub windows significantly reduced the computation. The entropy feature extraction in this paper took 0.53 seconds, which is 49.52% faster than the traditional method of directly extracting entropy from the entire window. After extracting twodimensional features, based on the distribution characteristics of the feature histogram and combined with the maximum entropy threshold segmentation algorithm, the wheat field image was preliminarily classified. For entropy features, low noise points affected the threshold segmentation effect. By comparing the impact of discarding different proportions of low entropy points on the segmentation threshold, this paper discarded 10% of low entropy points, enhancing the threshold segmentation effect. The method of removing small connected regions was used to eliminate the misclassified regions in the initial classification binary image. Then, the Canny operator was used to extract edge contour points distributed near the harvesting boundary. Due to the consistent direction between the harvesting boundary and the agricultural machinery's forward direction, the Ransac algorithm was used to restrict the fitting line in the region, obtaining the accurate harvesting boundary. In order to verify the feasibility of the method proposed in this article, wheat field images under different lighting conditions were collected, including 2 200 weak light images, 758 local strong light images, and 1 134 strong light images. After selecting the region of interest, the size of the image to be processed was 240 pixels×1 280 pixels. The algorithm in this paper took an average of 0.88 s to process each image on laptops. The detection rates under weak light, local strong light, and strong light were 90.41%, 88.26%, and 89.68%, respectively, with an average of 89.45%. Compared with the traditional method of using Adaboost algorithm for machine learning, the detection speed of this algorithm was improved by 73.89%, and the detection rate was increased by 46.19 percentage points, 46.00 percentage points, and 49.64 percentage points under weak light, local strong light, and strong light, respectively. Compared with traditional algorithms, the method proposed in this paper significantly improved real-time performance and detection rate, which can provide reference for the detection of field agricultural machinery navigation lines. [ABSTRACT FROM AUTHOR]

To explore the spatial-temporal evolution and driving factors of cultivated land multifunctional coupling coordination development, this paper applying the data of 30 provinces in China from 2000 to 2018 evaluated cultivated land production-living-ecological functions and revealed the spatial-temporal evolution of cultivated land multifunction with kernel density estimation and visualization mapping. Furthermore, this paper adopted coupling coordination degree model to analyze the cultivated land multifunctional coupling coordination development. Finally, this paper explored driving factors and driving mechanism of cultivated land multifunctional coupling coordination development by geographical detector. The results showed that: 1) from the perspective of temporal evolution, cultivated land production function level showed a fluctuation evolution trend of “rising-falling-rising”, the living function level showed an evolution trend of “rising-falling”, and the ecological function level increased slightly. All cultivated land functions had polarized characteristics. From the perspective of spatial pattern, the cultivated land production function level changed within [0.030, 0.608], showing a non-equilibrium spatial pattern of “high in the east, low in the west”. The living function level changed within [0.042, 0.672], showing a non-equilibrium spatial pattern of “high in the north, low in the south”. The ecological function level changed within [0.058, 0.897], showing a non-equilibrium spatial pattern of “high in the west, low in the east”. The areas with high level of production and living function were concentrated in the Northeast Plain, the Huang-Huai-Hai Plain and the Yangtze Region. The areas with low level of production function were located in the northwest and southwest regions, and the areas with low level of living function were located in the Beijing-Tianjin, Yunnan-Guizhou and southern regions in China. The area with high level of ecological function areas were located in the northeast and southwest regions, while the low-level areas were located in the Beijing-Tianjin-Hebei region, the Yangtze River Delta region and the central region of the Yangtze River. 2) The cultivated land multifunctional coupling coordination level changed within [0.093, 0.554], and the overall level had steadily improved but was still low. The cultivated land multifunctional coupling coordination levels of most provinces were still out of balance. The spatial pattern of cultivated land multifunctional coupling coordination level showed an evolution trend of agglomeration and promotion. In detail, the cultivated land multifunctional coupling coordination level of the northeast China, Beijing-Tianjin-Hebei region and the Yangtze River Delta were relatively high, while that was low in the northwestern part of China. 3) The evolution of cultivated land multifunctional coupling coordination level was significantly affected by the business entities conditions, agricultural development level, industrial development level and urbanization level. In short, the driving force of the business entities conditions and urbanization level were more significant than that of the agricultural development level and industrial development level. Moreover, the interaction type of two driving factors was two-factor enhancement or nonlinear enhancement, which indicated that the driving force of various factors had obvious divergence and interaction of factors positively strengthened the single-factor driving force, indicated that the divergence and interaction of various driving factors positively strengthened the single-factor driving force. In conclusion, the research results provide the guiding effect for deepening the study on cultivated land multifunction and decision support for promoting the differential governance of cultivated land resources and mutual promotion of cultivated land functions. To explore the spatial-temporal evolution and driving factors of cultivated land multifunctional coupling coordination development, this paper applying the data of 30 provinces in China from 2000 to 2018 evaluated cultivated land production-living-ecological functions and revealed the spatial-temporal evolution of cultivated land multifunction with kernel density estimation and visualization mapping. Furthermore, this paper adopted coupling coordination degree model to analyze the cultivated land multifunctional coupling coordination development. Finally, this paper explored driving factors and driving mechanism of cultivated land multifunctional coupling coordination development by geographical detector. The results showed that: 1) from the perspective of temporal evolution, cultivated land production function level showed a fluctuation evolution trend of “rising-falling-rising”, the living function level showed an evolution trend of “rising-falling”, and the ecological function level increased slightly. All cultivated land functions had polarized characteristics. From the perspective of spatial pattern, the cultivated land production function level changed within [0.030, 0.608], showing a non-equilibrium spatial pattern of “high in the east, low in the west”. The living function level changed within [0.042, 0.672], showing a non-equilibrium spatial pattern of “high in the north, low in the south”. The ecological function level changed within [0.058, 0.897], showing a non-equilibrium spatial pattern of “high in the west, low in the east”. The areas with high level of production and living function were concentrated in the Northeast Plain, the Huang-Huai-Hai Plain and the Yangtze Region. The areas with low level of production function were located in the northwest and southwest regions, and the areas with low level of living function were located in the Beijing-Tianjin, Yunnan-Guizhou and southern regions in China. The area with high level of ecological function areas were located in the northeast and southwest regions, while the low-level areas were located in the Beijing-Tianjin-Hebei region, the Yangtze River Delta region and the central region of the Yangtze River. 2) The cultivated land multifunctional coupling coordination level changed within [0.093, 0.554], and the overall level had steadily improved but was still low. The cultivated land multifunctional coupling coordination levels of most provinces were still out of balance. The spatial pattern of cultivated land multifunctional coupling coordination level showed an evolution trend of agglomeration and promotion. In detail, the cultivated land multifunctional coupling coordination level of the northeast China, Beijing-Tianjin-Hebei region and the Yangtze River Delta were relatively high, while that was low in the northwestern part of China. 3) The evolution of cultivated land multifunctional coupling coordination level was significantly affected by the business entities conditions, agricultural development level, industrial development level and urbanization level. In short, the driving force of the business entities conditions and urbanization level were more significant than that of the agricultural development level and industrial development level. Moreover, the interaction type of two driving factors was two-factor enhancement or nonlinear enhancement, which indicated that the driving force of various factors had obvious divergence and interaction of factors positively strengthened the single-factor driving force, indicated that the divergence and interaction of various driving factors positively strengthened the single-factor driving force. In conclusion, the research results provide the guiding effect for deepening the study on cultivated land multifunction and decision support for promoting the differential governance of cultivated land resources and mutual promotion of cultivated land functions. [ABSTRACT FROM AUTHOR]

Purple soil is widely distributed in southwestern China and is one of the important natural resources. Because of the loose parent rock and low degree of differentiation, purple soil is characterized by a loose structure that is easily eroded and high soil fertility. Soil erosion is very serious in the purple soil region, which is one of the main sources of sediment in the upper reaches of the Yangtze River, and has a certain impact on the agricultural production and engineering development in the purple soil region. After erosion, purple soil shows the characteristics of reduced dry density of soil and serious loss of fine particles, which affects its water-holding characteristics. In order to explore the changes of soil water-holding properties of purple soil after rainfall erosion, this study was carried out to analyze the effect of missing grain groups on the water-holding properties of sandy clayey purple soil under rainfall conditions by selecting three test methods, namely, axial translation technique, filter paper method, and saturated salt saturated saline solution vapor equilibrium technique for the three grain groups which are the most susceptible to changes in the rainfall conditions, and carrying out the Soil-Water Characteristic Curve (SWCC) test with the absence of grain groups within the full suction range to analyze the effect of the missing grain groups on the water-holding properties of sandy clayey purple soil. The results show that: 1) the missing grain group reduces the water-holding characteristics of sandy clayey purple soil, which is most obvious in the boundary effect zone and transition zone, and has less effect on the residual zone. The mass of the missing grain group is inversely proportional to the air intake value and the residual water content. 2) Using the grain size parameters (Constrained diameter、Average diameter、Median diameter、Effective diameter) to characterize the soil grading condition, the relationship between the soil grading curves and the characteristic values of SWCC was established. It was found that except for effective diameter, the other parameters were linearly inversely related to the air intake value and residual water content; 3) The data obtained from different methods were fitted using the Fredlund-Xing model, and it was found that the results of the data obtained from a single test method fitted better, but the SWCC significantly deviated from the test data points in the boundary effect zone and the transition zone. Root mean square error (RMSE)、 percent bias (PB) and mean absolute percent relative error (MAPRE) were introduced as evaluation indexes, and it was found that the suction range was the main factor affecting the fitting effect of SWCC, and within a certain suction range, the larger the range of data points involved, the better the model fitting effect was, and after a certain suction value was reached, the suction range had very little effect on the model fitting effect. It is proposed that to obtain the accurate SWCC of purple soil quickly, only the maximum suction value needs to be determined, and the number of test points with high suction is reduced, which can effectively shorten the SWCC test period. The results lay a foundation for the subsequent study of the role of soil erosion and water-holding characteristics of purple soil, and also provide reliable ideas for the prevention of soil erosion and the application of soil-water characteristic curves in the Three Gorges Reservoir area. [ABSTRACT FROM AUTHOR]

This study aimed to establish, calibrate and verify a discrete element method (DEM) particle model of rototilled soil particles for investigating the particle migration and machine soil interaction mechanism when forming micro-ridge seed beds by the rapeseed direct seeding machine. The soil type was rototilled yellow-brown soil, which is widely distributed in the mid)lower Yangtze River. The study consisted of two parts: parameter calibration and application verification. The parameter calibration followed these steps: (1) testing the soil plastic limit and selecting the contact model accordingly; (2) measuring the basic parameters, such as the soil particle size distribution and repose angle after rototilling; (3) using the EDEM particle filling and particle factory function to reproduce the particle bed based on the particle size distribution; (4) solving the optimal contact parameter combination by using factorial design, steepest ascent method, and quadratic orthogonal rotation regression method. The application verification test used two sets of structural parameters of a rotary-cutting micro-ridge seedbed preparation device (RMSD, which is the key device of rapeseed direct seeding machine to forming micro-ridge seed beds) to conduct DEM simulation and field tests, and compared the particles migration patterns and the landform after operation. A soil plastic limit tester was used to determine the plastic limit of the soil. The plastic limit of the sampled soil was 24.79%, which is close to the upper limit of the moisture content for rototiller operation. Hence, the Hertze-Mindlin (no slip) contact model was selected. The particle size range and mass ratio of the rototilled soil particles from the rapeseed direct seeding machine were sieved and counted. The rototilled soil particles of each particle size range were scanned and modelled using the EinScan-Pro 3D scanner. Based on the 3D models of the soil particles, the EDEM particle filling function was used to reconstruct the particles with different particle sizes and mass ratios. The soil particles bed was re-generated in EDEM according to the particles` mass ratio. The contact parameter calibration test includes a two-level factorization test, steepest ascent test, and quadratic orthogonal regression test, respectively, to perform a preliminary analysis of test factors and eliminate non-significant tone system, narrow down the range of factors, and solve the combination of contact parameters. The contact parameter calibration test uses the repose angle as an indicator. The two-level factorization test showed that the impact restitution coefficient had no significant effect on the repose angle; the rolling friction coefficient, static friction coefficient, and the interaction term of the two significantly affected the repose angle. The steepest ascent test was designed based on the model regression equation from a two-level factorization test. The steepest ascent test reduced the range of factors tested 0.20-0.60 (static friction coefficient) and 0.05-0.40 (rolling friction coefficient) to 0.32-0.51 and 0.11-0.27, a reduction of greater than 50%, providing effective support for obtaining accurate regression models. Since the restitution coefficient had an insignificant effect on the repose angle, the impact recovery coefficient was set to an intermediate level of 0.350 for the quadratic orthogonal regression test. The calibrations resulted in restitution, static friction and rolling friction coefficients of 0.350, 0.351 and 0.257, respectively. The verification consists of simulations and field tests conducted on the RMSD with two structural parameters. The field verification test used the PIVlab tool and the Trimble TX8 3D laser scanner to obtain the soil particles` velocity and the land-form. The simulation of 2 structural parameters was consistent with field experiments. The error of micro-ridge distance is 8.25%, which increases with the number of vanes of RMSD. The calibration parameters are accurate. This paper provides fundamentals for the DEM simulation and structural improvement for rapeseed micro-ridge seedbed preparation device. [ABSTRACT FROM AUTHOR]

Hyperspectral technology has been widely used in many fields due to its excellent performance characteristics such as high sensitivity, fine resolution, multi-channel data acquisition and processing capabilities, and non-destructive testing. However, despite its outstanding performance in data acquisition and information extraction, it also faces the challenges of high requirements and costs of hardware equipment. Hardware equipment such as high-precision optics, high-quality detectors, and complex data acquisition devices required for hyperspectral imaging have become one of the key factors limiting its popularity and application. To overcome this dilemma, researchers have begun to explore the reconstruction of RGB images into hyperspectral images by extracting information from traditional RGB images through spectral reconstruction techniques at relatively low cost. This innovative approach significantly reduces the complexity and cost of spectral data acquisition and provides a brand new way for wider application of hyperspectral technology. In the study of this paper, we use surimi adulteration detection as an example to explore the performance of different spectral reconstruction algorithms in depth. By mixing the dorsal muscle of silver carp with commercially available starch in different ratios and simulating the real state of surimi when it is sold, we successfully acquired RGB images and corresponding spectral data of a series of samples. In order to ensure the scientific validity and reliability of the experiment, we adopted the spectral-physical-chemical value covariance distance method (SPXY) for the sample set and divided the data set into calibration and validation sets according to the ratio of 5:3. In the process of spectral reconstruction, we explored two mainstream methods, the multivariate polynomial least squares regression algorithm (PMLR) and the deep learning hierarchical regression network (HRNet). Comparative experimental results show that the HRNet-based reconstruction technique has relative and root-mean-square errors of 0.010 4 between the spectra and the actual spectra, respectively, while the PMLR-based algorithm corresponds to relative and root-mean-square errors of 0.012 6. These results indicate that both methods are within acceptable error ranges, which provides a subsequent experimental results provide a solid foundation. In order to verify the reliability of the reconstructed spectra in practical applications, we established models of extreme learning machine regression (ELMR), support vector machine regression (SVR), and partial least squares regression (PLSR) based on the reconstructed spectra, and combined S-G convolutional smoothing (SG), mean centering (MC), standard normal variable transformation (SNV), derivative smoothing (DE), and normalization (NOR) methods were used to preprocess the spectra with a view to improving the model performance. By using corrected correlation coefficient (RC) corrected root mean square error (RMSEC) predicted correlation coefficient (RP) and root mean square error of prediction (RMSEP ) as evaluation metrics, we successfully detected the adulteration ratio of adulterated surimi. The experimental results show that the support vector machine regression model with standard normal transform (SNV) preprocessing achieves the best results with a prediction correlation coefficient (RP) of 0.983 0 and a root mean square error of prediction (RMSEP ) of 3.954 4% in the model based on the reconstruction of spectra by the PMLR algorithm. In the model based on the reconstructed spectrum of deep learning HRNet network, the support vector machine regression model, also using SNV preprocessing, achieved the best performance, with a prediction correlation coefficient of 0.998 7 and a prediction root-mean-square error of 4.080 8%. Therefore, the PMLR algorithm and HRNet network reconstruction technique based on RGB images not only realized efficient spectral reconstruction, but also provided a reliable solution for surimi adulteration detection. This research result provides new ideas and methods for quality and safety detection of food and agricultural products using low-cost hardware devices. [ABSTRACT FROM AUTHOR]

Recirculating aquaculture is an important aquaculture model with the advantages of high density, low environmental pollution and high economic benefit. However, recirculating aquaculture needs to add a large amount of feed to the system. Due to the problems of solid feed particles and fish feces deposition in the process of water circulation, the accumulation time is too long, which can easily lead to the decomposition of such sediments, and produce harmful substances to pollute the water body, while further consuming dissolved oxygen in the water body, and ultimately lead to the sub-health and death of fish. Therefore, how to quickly and efficiently discharge the residual bait and feces in the aquaculture tank and reduce its impact on water quality is the primary problem facing the recirculating aquaculture model, which needs to be solved urgently. In this paper, physical experiments were conducted to study the effects of fish culture density on the hydrodynamic characteristics of circular recirculating aquaculture tank and the accumulation of dirt movement, and to reveal the response relationship between culture density and self-cleaning capacity of aquaculture tank driven by different flow rates. Based on different breeding density (ρ=0, 2.65, 4.41, 6.2 kg/m³ ) and influent flow rate (Q=5.2, 6.54, 9.81 L/min), 12 groups of test conditions were designed for the single tube wall inlet mode of circular aquaculture tank. The Acoustic Doppler Velocimeter (ADV) was used to measure the flow velocity of the water body in the aquaculture tank, and the de-extremization operation was carried out to remove 5% of the maximum and minimum values, and then the average treatment was carried out, which approximately represented the flow velocity of the ADV measuring point. The weighted average flow rate, water resistance coefficient, turbulence intensity and other parameters of the whole breeding pond were calculated by the formula. The self-cleaning capacity of the aquaculture tank was analyzed by using these flow field parameters and combining the data of collecting time under different working conditions obtained from the pollution collection test. The results show that the low water flow rate in the circulation process is easy to lead to the deposition of part of the sewage and aggregation, and it is difficult to discharge the sewage. Increasing the density of fish culture could reduce the average flow velocity (vavg) of the overall flow field in the pond, the attenuation amplitude was within 0.05 m/s (less than 25%), and the resistance coefficient Ct in the water was increased. Turbulence caused by fish swimming can cause the sewage in the pool to re-suspension, which helps the dirt to be discharged; The sewage collection time was affected by the breeding density and flow rate at the same time, and the sewage collection time was within 5 min under the influent flow rate of 9.8 L/min. When the influent flow rate was 6.54 L/min, the breeding density increased from 0 to 6.2 kg/m³, the turbulence intensity increased by 2.4 times, and the sewage collection time was reduced by more than 40 min. The research results can provide reference for the design and daily management of circular aquaculture ponds. [ABSTRACT FROM AUTHOR]

It is widely recognized that the value of rice in the market is highly depends on its variety, with some varieties commanding significantly higher prices than others. Unfortunately, this has led to a situation where unscrupulous vendors may attempt to pass off inferior or lower-value rice varieties as more valuable ones. This urgently requires a reliable and efficient methods for accurately detecting and identifying different rice varieties. The use of near-infrared spectroscopy and deep learning for rice variety detection is an important research direction in this field. The establishment of an accurate detection model depends on a large-scale sample data, however, the process of collecting and preprocessing samples is very timeconsuming, which limits the improvement of accuracy. To address the aforementioned needs and limitations, this paper proposes a rice variety detection method based on near-infrared spectroscopy combined with an improved deep convolutional generative adversarial network (DCGAN) for data augmentation. Based on the classical deep convolution generative adversarial neural network, the network increases the number of convolution layers and adjusts the network structure. Subsequent experiments showed that the network has better generation ability. In this study, we selected four rice varieties, namely Wuchang, Xiangshui, Yinshui, and Yueguang, and collected 80 samples for each variety, amounting to a total of 320 rice powder samples as experimental samples. Firstly, we collected near-infrared spectra of the samples and preprocessed the raw spectral data by applying detrend correction (DC) and uninformative variable elimination (UVE) algorithms to eliminate irrelevant spectral features. Then, we established an improved DCGAN model to augment the preprocessed spectral data. Three traditional data enhancement methods, random left and right translation, superimposed Gaussian noise and translation noise, were used for comparison. Comparative experiments demonstrated that the spectral data generated by the improved DCGAN showed significant superiority in terms of structural similarity compared to traditional data augmentation methods. Finally, we investigated different data augmentation methods combined with different classification methods to establish a rice variety classification model. We employed five classifiers, namely partial least squares discriminant analysis (PLS-DA), k-nearest neighbors (KNN), random forest (RF), decision tree (DT), and one-dimensional convolutional neural network (1D-CNN), and combined them with four data augmentation methods to build the rice variety classification model. The results showed that the model built using DCGAN-augmented data achieved the highest accuracy on the test set, the accuracy rate was 98.21%. Through quantitative analysis of the models built using DCGAN combined with the five classifiers, it was found that the variety detection model established by DCGAN-1D-CNN method performed the best on the test set, with a coefficient of determination R 2 of 0.992 2, mean absolute error (MAE) of 0.008 9, and root mean square error (RMSE) of 0.050 5, indicating its optimal performance. Based on the comprehensive experimental results, the model established by combining the improved DCGAN data augmentation with the 1D-CNN classification algorithm exhibited the best performance in terms of accuracy on the test set, providing a new approach for convenient and accurate rice variety detection applications. By utilizing near-infrared spectroscopy and deep learning methods, combined with the improved DCGAN for data augmentation, we can enhance the accuracy and reliability of rice variety detection, thus meeting the market demand and consumer expectations. [ABSTRACT FROM AUTHOR]

The shovel type seedbed preparation machine is suitable for the preparation of rapeseed seedbeds in the rice-oil rotation area in the mid-lower Yangtze River. It can complete the functions of soil plowing, soil leveling, straw burying, and furrow opening in one operation. However, the unit had large vibration and noise during idling and field operations, which can easily cause wear and tear of key components, reduce work reliability and trouble-free working time, and affect the health and comfort of the driver. In order to solve the problem of large vibration during unit operation, this paper analyzed the theoretical excitation frequency of the main vibration source during the operation of the tractor coupling unit through theoretical calculation, and the vibration acceleration of the unit under idle speed and field operation conditions was tested by using Coco80 dynamic signal Test and analysis system. By using time-domain analysis and frequency-domain analysis, the vibration acceleration at different measuring points under two working conditions was determined, and the main excitation source of the unit was the movement of the shovel type tillage and soil preparation device. Based on the results of the vibration test, a scheme to reduce the vibration of the shovel type seed bed preparation machine was proposed by optimizing the arrangement of the crank phase angle to reduce the combined inertial force and inertia moment of the machine. According to the mass substitution method and kinematics analysis, the inertial force variation trend of a single group of shovel tillage and land preparation devices was obtained. Base on the calculation results, it was further clarified that the inertial force on the whole machine can be reduced by rationally designing the crank phase angle arrangement of the ten groups of mechanisms of the unit. Based on the calculated inertia force on a single group of mechanisms and combined with the crank phase angle of the existing machine tools arrangement, the inertial force and inertial moment force models of ten sets of crank-rocker mechanisms of the whole machine were established, and the arrangement of the crank phase angle that minimizes the total inertial force and inertial moment of the whole machine was obtained by using MATLAB software. Under this crank phase angle arrangement, the total inertial force of the whole machine was basically balanced, and the average absolute value of the total inertia moment in the x and y axes were reduced by 37.40% and 30.09% respectively. In order to verify the vibration reduction effect of changing the crank arrangement on the unit, the acceleration time domain signal was collected from the improved unit at the place where the vibration was relatively large. Compared with the unit before improvement, the total vibration of the measuring points after the improvement was respectively reduced by 36.42% and 31.97% under the conditions of idle speed and field operation. This research can provide reference for the inertia force balance design of planar linkage mechanisms and the research on vibration reduction of agricultural machinery based on planar linkage mechanism groups. [ABSTRACT FROM AUTHOR]

Assessing the sustainable development of agriculture is a hot topic in global agricultural research and an important task of rural revitalization and agricultural upgrading in the new era. Reform and opening up promote the rapid development of the agricultural economy, but the conflict between agricultural development and agricultural resources and ecological environment is serious. Since the 18th National Congress of the CPC, the sustainable development of agriculture has been given more prominence. It is of great significance to study the evaluation of agricultural sustainable development. Firstly, this paper studies the relevant literature on agricultural sustainable development evaluation at home and abroad. Based on wavelet transform, support vector machine(SVM), genetic algorithm (GA)and ant colony optimization(ACO) algorithm, a combination evaluation method of wavelet and intelligent optimization support vector machine is proposed. Secondly, by referring to the existing evaluation index system and combining with the actual situation of China's agricultural development, the alternative index set of agricultural sustainable development evaluation is established. Using SPSS software, the significance and correlation of 150 primary indexes are analyzed, and an evaluation index system including 62 indexes is established. Thirdly, using one-dimensional discrete stationary wavelet analysis, data denoising processing, genetic algorithm, ant colony algorithm, optimize the parameters of support vector machine, get better penalty parameters, kernel function, insensitivity coefficient, and then train the support vector machine. Fourth, taking 31 provinces (municipalities) in China as an example, this paper evaluates the agricultural sustainable development by applying the wavelet-GASVM model and the wavelet-ACOSVM model, and using the GA-SVM model and the ACO-SVM model to evaluate the agricultural sustainable development of 31 provinces (municipalities) in China. Fifthly, the reliability of the wavelet intelligent optimization SVM method is verified by changing the number of test sets and conducting multiple tests. The evaluation and simulation results show that: 1) The mean square error of agricultural sustainable development evaluation of Wavelte-GASVM is 9.641×10-5, and the correlation coefficient is 0.980; however, under the same training set and test set, the mean square error and correlation coefficient obtained by GASVM are 0.006 and 0.979, respectively. 2)The mean square error of Wavelet-ACOSVM agricultural sustainable development evaluation was 9.318×10-5, and the correlation coefficient was 0.972; the mean square error MSE and correlation coefficient of ACO-SVM are 0.016 and 0.953, respectively, under the same training set and test set. 3) Change the number of test sets and perform several tests and compare the results of GASVM model and ACO-SVM model before and after wavelet denoising respectively. It can be seen that under the GASVM model, the mean square error of the running results after wavelet denoising is smaller than that before wavelet denoising, and the correlation coefficient is larger than that before wavelet denoising, and the running time is shorter. Under the ACOSVM model, the mean square error of the running result after wavelet denoising is also smaller than that before wavelet denoising, and the correlation coefficient is larger than that before wavelet denoising, and the running time is shorter. Conclusion: The prediction accuracy and convergence speed of agricultural sustainable development evaluation by wavelet-GASVM and wavelet-ACOSVM are better than GA-SVM and ACO-SVM, respectively, and the effect of agricultural sustainable development evaluation by wavelet-GASVM is more ideal. This study provides a method reference for agricultural sustainable development assessment. [ABSTRACT FROM AUTHOR]

It is important to judge the density of pear flower accurately for automatic thinning. In order to judge the density of pear flowers precisely and achieve the purpose of automatic flower thinning, a density classification of pear flower images method based on improved density peak clustering algorithm was proposed in this study. The pear flower image samples used in the experiment were taken from the research base of Hebei Agricultural University, Niugang Village, Yi County, Baoding City, Hebei Province, and the variety was three-year-old Qiuyue. In this study, the images of pear flowers in natural environment were collected on April 18, 2021, when the pear flowers were in full bloom. The pear flower recognition images with high detection accuracy were obtained by using the depth learning model to detect the pear flower. The central coordinates of the target detection frame were extracted from the recognition images to obtain the data points that needed to be clustered. Secondly, in order to achieve the density classification of pear flower images, the method of selecting the cluster center was improved according to the shortcomings of the original density peak clustering algorithm in the density classification of pear flower and the requirements of pear flower density classification. The decision graph was divided into four parts by four groups of local density and center offset distance segmentation thresholds to select the cluster centers. These four parts corresponded to four grades of high, medium and low density and no thinning treatment. The accurate classification of pear flower images with reasonable density was realized. Finally, to solve the problem of inaccurate clustering classification of pear flower distribution with only cluster distribution, sparse distribution and large-scale close-up, the calculation method of dij parameter of distance between two points was improved. The pear flower scale and density grading standard were unified, and reasonable density grading could be achieved for all distribution types of pear flower images. The experimental results showed that the proposed algorithm could adapt to pear flower images of different scales. The accuracy of scale prediction was 94.89%, and the accuracy of density classification was 94.29%. Compared with the existing methods, the proposed method could achieve the density classification of local flower clusters in natural environment. the algorithm in this paper was compared with K-means algorithm and DBSCAN algorithm, and the accuracy of density classification was 94.29% and 68.57% respectively. Although the density classification accuracy of K-Means clustering algorithm was high, it lacked the algorithm steps to automatically select the clustering centers of different density classification, and was too sensitive to noise points. The density calculation method of DBSCAN algorithm lacked continuity, and the division of density grade was not precise enough. Based on the comprehensive analysis of density classification accuracy and cluster center selection method, the improved density peak clustering algorithm in this paper had the best effect. The clustering centers of different density grade could be automatically selected and the error of random initialization of clustering centers could be reduced. The proposed algorithm was robust to noise points and could eliminate noise points effectively. The proposed algorithm adopted soft statistics method for density calculation, which had continuity and was more accurate for density classification. It provides technical support for analyzing the density of pear flower and machine intelligent flower thinning. [ABSTRACT FROM AUTHOR]

In order to solve the problem of the deviation between the conventional theoretical results and the experimental results of the suspension velocity of LASC (Light And Soft Compressible) seed cotton ball, the relationship between the physical properties of LASC seed cotton ball and the resistance coefficient were established by using the dimensional analysis method and the force balance principle, then the theoretical algorithm of LASC seed cotton ball suspension velocity was proposed based on the conventional particle suspension velocity theory. Then, the diameter of LASC seed cotton ball containing a completely cracked single cotton peach were tested in the cotton field, and the minimum diameter of pneumatic cotton conveying pipeline was determined, and the drag coefficient was determined by calculating and analyzing the Reynolds number of pipe flow and around flow. Spherical LASC seed cotton ball with 6 types of densities and 18 types of diameters were prepared by using equal density variable diameter method and the suspension velocities were tested. When the LASC seed cotton balls were just in completely suspended and stabilized at the middle of the glass tube on the test platform, then the suspension speed of the LASC seed cotton balls were calculated according to the relationship between frequency recorded by the frequency modulator and inlet wind speed, and following results were obtained. When a single cotton ball was fully cracked, the value range of the diameter of the seed cotton balls were between 38-54 mm, then the average diameter of the seed cotton ball and the minimum diameter of the cotton conveying pipe were determined to be 50 mm, so the both Reynolds numbers meet the conditions of 500

Nitrogen is one of the most essential elements for crop growth. Nitrogen fertilizer has been widely applied to increase crop yields. At the same time, a large number of negative impacts have posed a great threat to the ecological environment in recent years. Among them, nitrogen leaching can be attributed to the excessive application of chemical fertilizers in farmland. Fortunately, the combined application of organic and chemical fertilizers can be expected to effectively reduce soil nitrogen leaching in normal fertilization during agricultural production at present. Therefore, this study aims to explore the combination application mode of organic and chemical fertilizers with a low risk of nitrogen leaching in farmland. The search terms were selected as chemical fertilizer, organic fertilizer, and nitrogen leaching using the two databases of China National Knowledge Infrastructure (CNKI) and Web of Science. The peer-reviewed and published papers were then obtained up to January 2022. Finally, a total of 35 papers (22 papers from Web of Science, 13 papers from CNKI) and 331 effective data pairs were collected after screening for the combination application of organic and chemical fertilizers in farmland. The target variables were taken as the total nitrogen (TN), nitrate nitrogen (NO3 - -N), and dissolved organic nitrogen (DON), while the chemical fertilizer was the control. After that, Metawin 2.1 software was used to determine the overall effects of the total amount of fertilization, fertilization structure (organic fertilizer substitution ratio), fertilization time (basic topdressing), and the types of organic fertilizers on the nitrogen leaching, where the chemical fertilizer was as the control. The results showed that there was a significant influence of the above fertilization behavior on nitrogen leaching. Once the total amount of nitrogen was less than 200 kg/hm2, the leaching of TN and NO3 - -N in farmland decreased by 36.77% and 65.05%, respectively. When the substitution ratio of organic fertilizers was higher than 70%, the TN leaching was reduced by 39.64%, whereas the risk of dissolved DON leaching increased by 15.78%. Especially, there was a 26.31% increase in DON leaching in the application of animal-based organic fertilizers combined with chemical fertilizers. Correspondingly, the application of nitrogen fertilizer significantly reduced the leaching of TN and NO3 - -N by 43.58% and 70.51%(P<0.05), respectively. A certain impact was also found in the soil pH and land use patterns on nitrogen leaching. For example, the combined application of organic and chemical fertilizers on the alkaline dryland soil effectively inhibited the leaching of TN and NO3 - -N, whereas, there was an increase in the leaching of DON by 26.63%-42.95%. Nitrogen leaching in dryland was dominated by the NO3 - -N leaching. By contrast, the emission factor (EF) was higher than that in the paddy field. The increasing replacement ratio of organic fertilizers can be expected to greatly reduce the soil nitrogen leaching in dryland, but to enhance the DON leaching. In addition, the Matlab software was used to analyze the importance of factors using the random forest model. Specifically, the replacement ratio of organic fertilizer demonstrated a dominant effect on TN leaching. There was also the more important effect of the nitrogen application level on the NO3 - -N and DON leaching. Therefore, the low level of nitrogen application and the high substitution ratio of animal-based organic fertilizers can be used to effectively reduce the soil nitrogen leaching loss in the alkaline dryland, compared with the chemical fertilizers only. The finding can provide the practical basis for the combined application of organic and chemical fertilizers in farmland. [ABSTRACT FROM AUTHOR]