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Chlorophyll a and b content (Cab) and leaf area index (LAI) are two key parameters of crops, and their quantitative inversions are important for growth monitoring and the field management of wheat. However, due to the close correlation between the spectral signals of these two parameters and the effects of soil and atmospheric conditions, as well as modeling errors, synchronous retrieval of LAI and Cab from remote sensing data is still a challenging task. In a previous study, we introduced the optimal estimation theory and established the inversion framework by coupling the PROSAIL (PROSPECT + SAIL) model with the unified linearized vector radiative transfer model (UNL-VRTM). The framework fully utilizes the simulated radiance spectra for synchronous retrieval of Cab and LAI at the UAV observation scale and has good convergence and self-consistency. In this study, based on this inversion framework, synchronized retrieval of Cab and LAI was carried out by real wheat UAV observation data and validated with the ground-measured data. By comparing with the empirical statistical model constructed by the PROSAIL model and coupled model, least squares support vector machine (LSSVM), and random forest (RF), the proposed method has the highest accuracy of Cab and LAI estimated from UAV multispectral data (for Cab, R2 = 0.835, RMSE = 14.357; for LAI, R2 = 0.892, RMSE = 0.564). Our proposed method enables the fast and efficient estimation of Cab and LAI in multispectral data without prior measurements and training. [ABSTRACT FROM AUTHOR]

Objective: To realize the regulation of the position of corn seed planting in precision farming, an intelligent monitoring system is designed for corn seeding based on machine vision and the Genetic Algorithm-optimized Back Propagation (GABP) algorithm. Methods: Based on the research on precision positioning seeding technology, comprehensive application of sensors, Proportional Integral Derivative (PID) controllers, and other technologies, combined with modern optimization algorithms, the online dynamic calibration controls of line spacing and plant spacing are implemented. Based on the machine vision and GABP algorithm, a test platform for the seeding effect detection system is designed to provide a reference for further precision seeding operations. GA can obtain better initial network weights and thresholds and find the optimal individual through selection, crossover, and mutation operations; that is, the optimal initial weight of the Back Propagation (BP) neural network. Field experiments verify the seeding performance of the precision corn planter and the accuracy of the seeding monitoring system. Results: 1. The deviation between the average value of the six precision positioning seeding experiments of corn under the random disturbance signal and the ideal value of the distance is less than or equal to 0.5 cm; the deviation between the average value of the six precision positioning seeding experiments of corn under the sine wave disturbance signal (1 Hz) is less than or equal to 0.4 cm; the qualified rate of grain distance reaches 100%. 2. The precision control index, replay index, and missed index of the designed corn precision seeding intelligent control system have all reached the national standard. During the operation of the seeder, an alarm of the seeder leaking occurred, and the buzzer sounded and the screen displayed 100 times each; therefore, the reliability of the alarm system is 100%. Conclusion: The intelligent corn seeder designed based on precision positioning seeding technology can reduce the seeding rate of the seeder and ensure the stability of the seed spacing effectively. Based on the machine vision and GABP algorithm, the seeding effect detection system can provide a reference for the further realization of precision seeding operations. [ABSTRACT FROM AUTHOR]

In order to explore the interaction between the axial-flow threshing unit and the agricultural materials, the vibration signal of the axial-flow threshing unit and the stress signal of the internal threshing tooth under the dynamic threshing condition were detected by using the three-way acceleration sensors and the resistance strain gauges. The results show that under the dynamic threshing condition, the amplitude of the vibration signal along the axial direction of the threshing unit experienced a small change, but changed greatly at the feeding direction of materials and vertical direction of fixed bearings, which was mainly caused by the internal impact between the threshing unit and the materials; Under the impact of materials, the threshing element of each measuring point has a continuous peak value of stress signal, and the stress signals gradually decreased along the axial direction. This study further reveals the interaction mechanism between axial-flow threshing unit and materials from the perspective of vibration and impact, and provides new methods for the development of condition detection technology of grain harvesting equipment. [ABSTRACT FROM AUTHOR]

In order to meet the requirements of peanut pods cleaning by the pick-up peanut combine harvester, and enhance the processing ability of branches, leaves, broken stalks and hard fruit stalks of the cleaning system, a kind of sieve-roll combined peanut pods cleaning device was designed based on the analysis of material cleaning characteristics. It mainly includes key components, such as cross flow fan, vibrating screen and separation roller. The force and movement state of the picking peanut extraction on the sieve surface and the separation roller were analysed. Through field loading test and verification, when the vibration screen width is 1200 mm, the crank speed is 650 rpm, the cross flow fan speed is 920 rpm, the separation roll speed is 410 rpm, the separation roll gap is 3 mm, then the peanut pod containing impurities rate is 1.52% and the loss rate is 0.64%, which meets the national industry standards. [ABSTRACT FROM AUTHOR]

The new cleaning system, mainly composed of light impurities cleaning device and stems separating device was designed. The former used cross-flow fan to clean light impurities, and the latter used the differences of radial geometry size between peanut pods and stems to separate stems from peanut pods. By means of theoretical calculation and graphic analysis, the structure motion parameters of the main working parts were determined and the performance experiments were carried out. According to the experimental results, the influence law of the structure motion parameters of peanut cleaning system on cleaning performance was analyzed and optimal combination of parameters was obtained. When the rotation speed of cross-flow fan was 920 r/min, the distance between sliding screen and fan impeller was 100mm, and the width of discharging exit was 50 mm, the clearance of separation rollers was 2mm, the rotation speed of separation roller was 300 r/min, and the angle between separating device and horizontal plane was 35°, the impurity content was 0.89%, and the cleaning loss rate was 0.62%, which met the requirement of peanut harvester. [ABSTRACT FROM AUTHOR]

Maize is one of the important grain crops grown globally, and growth will directly affect its yield and quality, so it is important to monitor maize growth efficiently and non-destructively. To facilitate the use of unmanned aerial vehicles (UAVs) for maize growth monitoring, comprehensive growth indicators for maize monitoring based on multispectral remote sensing imagery were established. First of all, multispectral image data of summer maize canopy were collected at the jointing stage, and meanwhile, leaf area index (LAI), relative chlorophyll content (SPAD), and plant height (VH) were measured. Then, the comprehensive growth monitoring indicators CGMICV and CGMICR for summer maize were constructed by the coefficient of variation method and the CRITIC weighting method. After that, the CGMICV and CGMICR prediction models were established by the partial least-squares (PLSR) and sparrow search optimization kernel extremum learning machine (SSA-KELM) using eight typical vegetation indices selected. Finally, a comparative analysis was performed using ground-truthing data, and the results show: (1) For CGMICV, the R2 and RMSE of the model built by SSA-KELM are 0.865 and 0.040, respectively. Compared to the model built by PLSR, R2 increased by 4.5%, while RMSE decreased by 0.3%. For CGMICR, the R2 and RMSE of the model built by SSA-KELM are 0.885 and 0.056, respectively. Compared to the other model, R2 increased by 4.6%, and RMSE decreased by 2.8%. (2) Compared to the models by single indicator, among the models constructed based on PLSR, the CGMICR model had the highest R2. In the models constructed based on SSA-KELM, the R2 of models by the CGMICR and CGMICV were larger than that of the models by SPAD (R2 = 0.837), while smaller than that of the models by LAI (R2 = 0.906) and models by VH (R2 = 0.902). In summary, the comprehensive growth monitoring indicators prediction model established in this paper is effective and can provide technical support for maize growth monitoring. [ABSTRACT FROM AUTHOR]

Cow lameness is a complex locomotion behaviour, and effective characteristics are the key to identifying early lameness. Machine learning methods were used to quantify the movement of the hooves and head of cows, and they showed the possibility of using the head- and back-hoof linkage features extracted from motion analysis to classify early lameness. Firstly, the key points of the body in the side-view walking videos were tracked by DeepLabCut (DLC). Based on the curves of hoof movement and head swing, the head-hoof linkage pattern was analysed. Second, the curve of cow's back was extracted and the back-hoof linkage feature was built when the hoof touched the ground. At the same time, the movement curves were analysed to extract five other gait characteristics, including asymmetry of stride length, tracking up, asymmetry in the proportion of supporting phase, landing speed, and moving speed. Multiple models were trained on a dataset containing 212 videos, and 10-fold cross-validation was used to verify the performance of the algorithm. The classification performance was improved by optimising the model and algorithm. Overall accuracy and recall were 89.2% and 90.7%, respectively. The scores of the importance of features calculated by the Chi-square test showed that the head- and back-hoof linkage features play an important role in the classification of early lame cows. The hooves and their linkage features with the head and back of cows proposed in this paper can thus effectively detect early lameness in cows. • DeepLabCut was used to track four hooves and head of walking cow. • Head- and back-hoof linkage features was analysed. • Seven lameness indicators were used to build the model. • The recall of classification for early lame cows was 90.7%. [ABSTRACT FROM AUTHOR]

The discrete element computer simulation method is an effective tool that enables the study of the interaction mechanism between the pulping components and the paddy soil during the paddy field pulping process. The findings are valuable in optimizing the parameters of the paddy beating device to improve its working quality and efficiency. However, the lack of accurate soil models for paddy soil has limited the application and development of the discrete element method in paddy pulping research. This study selected the Hertz-Mindlin with Johnson-Kendall-Roberts discrete element model for the pre-pulping paddy loam soil and used the slump error as the test index to select nine parameters, including soil Poisson's ratio and surface energy, as test factors to calibrate the model parameters. The Plackett-Burman test identified soil shear modulus, surface energy, and soil-iron plate static friction coefficient as significant factors affecting the test index. The steepest ascent test results determined the test range of the above parameters. The Box-Behnken test obtained the regression model between the significant factors and the test index, and the regression model was optimized using the slump error as the target. The optimal combination of parameters was surface energy of 3.257 J/m2, soil shear modulus of 0.709 MPa, and static friction coefficient between soil and iron plate of 0.701. The slump simulation test using this combination of parameters yielded an average slump error of 2.04%. The collective results indicate the accuracy of the calibrated discrete element simulation parameters for paddy loam soil. These parameters can be used for discrete element simulation analysis of the paddy pulping process after paddy field soaking. [ABSTRACT FROM AUTHOR]

UAV (unmanned aerial vehicle) remote sensing provides the feasibility of high-throughput phenotype nondestructive acquisition at the field scale. However, accurate remote sensing of crop physicochemical parameters from UAV optical measurements still needs to be further studied. For this purpose, we put forward a crop phenotype inversion framework based on the optimal estimation (OE) theory in this paper, originating from UAV low-altitude hyperspectral/multispectral data. The newly developed unified linearized vector radiative transfer model (UNL-VRTM), combined with the classical PROSAIL model, is used as the forward model, and the forward model was verified by the wheat canopy reflectance data, collected using the FieldSpec Handheld in Qi County, Henan Province. To test the self-consistency of the OE-based framework, we conducted forward simulations for the UAV multispectral sensors (DJI P4 Multispectral) with different observation geometries and aerosol loadings, and a total of 801 sets of validation data were obtained. In addition, parameter sensitivity analysis and information content analysis were performed to determine the contribution of crop parameters to the UAV measurements. Results showed that: (1) the forward model has a strong coupling between vegetation canopy and atmosphere environment, and the modeling process is reasonable. (2) The OE-based inversion framework can make full use of the available radiometric spectral information and had good convergence and self-consistency. (3) The UAV multispectral observations can support the synchronous retrieval of LAI (leaf area index) and Cab (chlorophyll a and b content) based on the proposed algorithm. The proposed inversion framework is expected to be a new way for phenotypic parameter extraction of crops in field environments and had some potential and feasibility for UAV remote sensing. [ABSTRACT FROM AUTHOR]

The reciprocating cutting system is one of the key parts of a tea harvester; and its cutting performance directly determines the cutting power consumption and harvesting quality of the machine. Because the structural parameters of reciprocating cutting systems do not match the tea cut; resulting in larger cutting resistance, it is necessary to optimize the structural parameters. The cricket mouth part has outstanding performance in tea tree fiber cutting; and the curved structural characteristics of the upper jaw of the cricket have been useful to improve the cutting efficiency of cutting system. Quantitative analysis of the structure of the upper jaw revealed that the arc-shaped structure of the incisor lobe would inspire new bionic blades and bionic cutters to solve the above problems. The cutting performance experiment of the cutting blade was designed for investigating the effects of inter-node number; tea variety and blade type (ordinary blade; bionic blade e and bionic blade f) on the cutting force and cutting power consumption. Experimental results of cutting performance have shown that the bionic blade could reduce cutting resistance and cutting power consumption. Tea varieties had little effect on cutting force and cutting power consumption. In addition, the orthogonal test was carried out to study the influence of cutter type with the cutting speed ratio and cutting angle on the integrity rate and missing rate of tea shoot. The field cutting experiment showed that the cutting angle was the most important for the integrity rate and missing rate of tea shoot; followed by the cutter speed ratio; and finally, the cutter type. The optimum combination of parameters was a cutting speed of 0.8 m/s; a forward speed of 1.0 m/s; a cutting angle of −3°, and using the bionic cutter e. With the optimal parameter combination, the integrity rate and missing rate of the tea shoot were 92.7% and 3.9%, which were increased by 13.2% and decreased by 6.4% compared to those under the condition of the 0° cutting angle and an ordinary cutter. As a result, the bionic cutter could obviously reduce cutting resistance; reduce cutting power consumption and improve the harvesting quality. These results would provide guidance for the design of the reciprocating cutting system of tea harvesters and other stalk cutting machinery. [ABSTRACT FROM AUTHOR]

With the rapid progression of agricultural informatization technology, the methodologies of crop monitoring based on spectral technology are constantly upgraded. In order to carry out the efficient, precise and nondestructive detection of relative chlorophyll (SPAD) during the booting stage, we acquired hyperspectral reflectance data about spring wheat vertical distribution and adopted the fractional-order differential to transform the raw spectral data. After that, based on correlation analysis, fractional differential spectra and fractional differential spectral indices with strong correlation with SPAD were screened and fused. Then, the least-squares support vector machine (LSSSVM) and the least-squares support vector machine (SMA-LSSSVM) optimized on the slime mold algorithm were applied to construct the estimation models of SPAD, and the model accuracy was assessed to screen the optimal estimation models. The results showed that the 0.4 order fractional-order differential spectra had the highest correlation with SPAD, which was 9.3% higher than the maximum correlation coefficient of the original spectra; the constructed two-band differential spectral indices were more sensitive to SPAD than the single differential spectra, in which the correlation reached the highest level of 0.724. The SMA-LSSSVM model constructed based on the two-band fractional-order differential spectral indices was better than the single differential spectra and the integration of both, which realized the assessment of wheat SPAD. [ABSTRACT FROM AUTHOR]

• g-C 3 N 4 /CuNps was prepared by facile Thermal polycondensation. • Nanocomposite was successfully characterized by XRD,XPS, FTIR, SEM and EDS analysis. • The proposed method exhibited high sensitivity and sub micro-molar detection limits. • The suggested method showed good recoveries in real water samples. • The developed electrode substrate showed a satisfactory linear range, high sensitivity and selectivity, good repeatability, stability, negligible interference of other metal ions. Cadmium ions (Cd2+) are one of the highly poisonous heavy metal ions in the environment, but at present, the detection of Cd2+ on the market has the disadvantages of complex operation, high detection cost and slow detection speed, so it is urgent to develop rapid and sensitive detection platforms for detecting Cd2+. In this study, a novel electrochemical sensor (g-C 3 N 4 /CuNps) was successfully fabricated. g-C 3 N 4 /CuNps composite has a large and unique surface area that is beneficial to the adsorption and preconcentration of Cd2+. The experiment parameters such as deposition potential, deposition time, pH of supporting electrolyte, supporting electrolyte, the amount of g-C 3 N 4 /CuNps being coated on the GCE, were optimized. Under the optimized conditions, the electrochemical performances of g-C 3 N 4 /CuNps composites modified sensor have been investigated, which shows excellent wider linear range and lower detection limit (0.098 nM). The linear ranges of Cd2+ detections were 1-100µgL-1. Moreover, the sensor shows desirable stability, selectivity, and reproducibility for detecting Cd2+. For real water samples, the sensor has a satisfactory recovery rate, indicating that the sensor could be successfully applied in the determination of Cd2+ in environmental water. Therefore, the designed sensor provides a potential simple and low cost material for electrochemical detection of Cd2+. [ABSTRACT FROM AUTHOR]

Subsoiling practice is an essential tillage practice in modern agriculture. Tillage forces and energy consumption during subsoiling are extremely high, which reduces the economic benefits of subsoiling technology. In this paper, a cicada-inspired biomimetic subsoiling tool (CIST) was designed to reduce the draught force during subsoiling. A soil–tool interaction model was developed using EDEM and validated using lab soil bin tests with sandy loam soil. The validated model was used to optimize the CIST and evaluate its performance by comparing it with a conventional chisel subsoiling tool (CCST) at various working depths (250–350 mm) and speeds (0.5–2.5 ms−1). Results showed that both simulated draught force and soil disturbance behaviors agreed well with those from lab soil bin tests, as indicated by relative errors of <6.1%. Compared with the CCST, the draught forces of the CIST can be reduced by 17.7% at various working depths and speeds; the design of the CIST obviously outperforms some previous biomimetic designs with largest draught force reduction of 7.29–12.8%. Soil surface flatness after subsoiling using the CIST was smoother at various depths than using the CCST. Soil loosening efficiencies of the CIST can be raised by 17.37% at various working speeds. Results from this study implied that the developed cicada-inspired subsoiling tool outperforms the conventional chisel subsoiling tool on aspects of soil disturbance behaviors, draught forces, and soil loosening efficiencies. This study can have implications for designing high-performance subsoiling tools with reduced draught forces and energy requirements, especially for the subsoiling tools working under sandy loam soil. [ABSTRACT FROM AUTHOR]

At present, potato seeders in China generally have poor uniformity of seed rows and high coefficients of variation in plant spacing during seed rows, causing difficulties for subsequent mechanized plant protection and harvesting. Based on the effect of seed discharge to analyze the sowing process, a potato seed discharger with a double-layer seed picking spoon structure was designed. By analyzing the seed discharging mechanism and its operation process, the shape and size structural parameters of the seed picking spoon were determined. Finite element simulation of the seed pickup process and seed carrying process of the seed discharging mechanism was carried out by EDEM software to determine the double-layer seed scoop scheme and the range of factors for subsequent tests. A two-factor test was conducted with seeding line speed and seed drop height as test factors, and plant spacing coefficient of variation and seed potato lateral offset dispersion as test indexes. The test results showed that the double-layer seeding spoon chain seeder reduced the coefficient of variation in plant spacing by 5.8%, and the dispersion in lateral offset by 5.5 mm, compared with the single seeding spoon seeder, when the seeding speed was 0.184 m/s and the height of falling seed was 9 cm. [ABSTRACT FROM AUTHOR]

In this report, we describe the combination of [5,5]‐rearrangement‐triggered dearomatization recently developed in our laboratory with an intramolecular formal [2+2]‐cycloaddition for constructing an intriguing polysubstituted tricyclo[4.2.1.03,8]nonane derivatives. The synthesis features the use of simple and readily available three distinct starting materials including aryl sulfoxides, allyl nitriles, and certain nucleophiles or dienes which directly enriches the diversity of target molecules. This study provides an entirely new protocol for the synthesis of cyclobutane‐centered tricyclic caged skeleton. [ABSTRACT FROM AUTHOR]

Subsoiling is an essential practice in conservation tillage technology. The amount of disturbed soil at various depths resulting from subsoilers with different parameters has an important effect on soil properties (e.g., bulk density and water infiltration). The information regarding the effects of subsoiling on the characteristics of soil water infiltration is essential for the design of subsoiling tools. In this study, the effects of the wing mounting height (h) (75–155 mm) of the subsoiler on soil disturbance and soil water infiltration were modelled using HYDRUS-2D and validated using field experiments. Results showed that reducing h values resulted in larger soil disturbance area ratios, soil water infiltration rates (f(t)), distances of vertical wetting front movement (DVWs), accumulative infiltrations (AINs), and soil moisture contents at depths of 10–30 cm. The relationships among characteristics of soil water infiltration, h and time (t), were developed. The stable infiltration rates (fs) varied quadratically with h and the corresponding coefficient of determination (R2) was 0.9869. The Horton model is more suitable for describing the relationship between f(t) and t under the tested soil conditions, as compared with the Kostiakov and Philip models. According to the results of soil water content at different depths from the HYDRUS simulations and field experiments, the developed soil water infiltration model had a good accuracy, as indicated by RMSEs of <0.05, R2 values of >0.95, and mean relative errors of <12%. The Above results indicated that increasing the hardpan disturbance by optimizing wing parameters of the subsoiler could improve soil water infiltration characteristics. [ABSTRACT FROM AUTHOR]

Vegetable production is facing the problems of low resource utilization rate and serious environmental pollution. Vegetable sowing and planting with fertilizer have the characteristics of extensive and low efficiency. An intelligent monitoring system of vegetable fertilization and seeding is designed in this paper. The system uses CAN bus communication protocol to collect sensor signals and PLC to control the execution part. The seeding and fertilization monitoring were carried out by photoelectric sensing technology and piezoelectric thin film technology, respectively. The system is composed of sowing and fertilizing machinery, monitoring system of seed leakage, alarm system of fertilizer blockage, variable-rate fertilization control system and on-board computer. The system uses the GPS module to get the location information of the machine. Combined with the pre-loaded plots map of soil prescription, the target fertilization amount in the current position is determined. Pressure sensor and microcomputer control are used to calculate the current fertilization rate and flow information, and the fertilization rate is adjusted in real time according to the moving speed of the machine. Finally, the detection of miss-seeding, blocking and precision fertilization is realized. The field test results show that the accuracy of monitoring is over 91%. The alarm error of fertilizer blockage is less than 4%. The accuracy of variable fertilization is more than 94%. The whole machine monitoring system can adapt to complex field environment and has better practicability and economy. [ABSTRACT FROM AUTHOR]

• A vibration control system based on intelligent sensor nodes was developed. • Each intelligent detection node can work independently or networking as IoT system. • The vibration isolation system can effectively reduce the engine vibration. Vibration of agricultural machinery not only affects the quality and effect of the operation, but also reduces the service life of machinery. A vibration detection and control system based on intelligent sensor nodes was developed to detect and control the frame vibration caused by high-power engine in agricultural machinery. Each intelligent detection node can work independently or networking as Internet of Things system. The effect of the vibration isolation system can be assessed by installing the test nodes at the measuring points of the two-stage vibration isolation system. In this paper, intelligent sensor nodes were used to reduce the vibration of an agricultural machinery engine. According to the optimized stiffness of the double-layer vibration isolation system, a test platform system is designed. The intelligent sensor nodes were used to assess the vibration spectrum characteristics of engine under various working conditions, and acceleration values of upper and lower measuring points of stage 1 and stage 2 isolators. The relationship between acceleration transfer rate and engine speed was analyzed before and after double-layer vibration isolation was used, and the vibration isolation effect of double-layer vibration isolation system is verified. The results show that the double-layer vibration isolation system can play a significant role in changing the stiffness of the whole system and avoiding the occurrence of coupling resonance. The transmission rate of vibration acceleration decreases from 0.94 to 0.54 at the engine speed of 2700 r/min, with a decrease of 42.3%. The results show that the proposed intelligent detection node is able to detect the engine vibration in agricultural machinery, and the proposed double-layer vibration isolation system can effectively reduce the engine vibration. [ABSTRACT FROM AUTHOR]

To control the key structures vibration of combine harvesters, e.g. the driver's seat, the partial coherence mode of the cutter driving system has been set up based on the structural model. The vibration of the cutter is transmitted to the cutting platform through three connection positions - the cutter chute, the rocker shaft seat and the middle shaft seat of the header. For the three junction points from the cutter system to the cutting platform, the time-domain and frequency-domain characteristics of the vibration response are measured and analysed. The main exciting source sequence of Y direction vibration on seat fixing point is considered as the Y direction of cutter chute, Y direction of rocker shaft, Y direction of the middle shaft of the header, and Z direction of cutter chute. Moreover, the correctness of the order above is verified using partial-coherence method. Considering that the cutter chute is the main source, a nut with a rubber sleeve is designed to reduce the vibration transformed from cutter bar to chute. The experiment has verified that this method is able to reduce the Y direction vibration intensity of the seat fixing points by 10.78%. • Determine vibration transmitted to the platform through 3 connecting positions. • Partial-coherence method used to order the methods of vibration transmission. • Nut with rubber sleeve used to reduce vibration transmission from cutter bar to chute. [ABSTRACT FROM AUTHOR]

Image processing technology has been used in precision dairy farming to support management decisions. Vision-based animal identification systems can become a potential alternative to RFID. In this paper, a vision system is proposed to extract body images and identify Holstein cows. Side view videos of dairy cattle walking in a straight line were collected. Cow mask was detected using Adaptive SOM method. The largest inscribed rectangle was extracted to locate the cow's body area. A total of 528 videos were collected from 66 cows, and 3 videos were randomly selected for each cow to build template datasets, while the rest of the videos were used as test data. Feature points of the body image were extracted and matched with the template dataset to identify the unknown cow. Four feature extraction methods and two matching methods were investigated and evaluated. The results showed that the highest identification accuracy was 96.72% when the FAST, SIFT and FLANN methods were used for feature extraction, descriptor, and matching, respectively. However, the combination of ORB and BruteForce had better computational efficiency on the basis of high accuracy. Software was implemented and can realise accurate identification of dairy cattle in real-time. • Vision system for dairy cow individual identification is proposed. • Body image of cows was extracted automatically. • An identification model was designed on the basis of ACE-V principle. • Accuracy of one-step match is 95.41% using ORB features. [ABSTRACT FROM AUTHOR]

At present, the measurement of tillage depth is mainly based on manual measurement, but the manual raking method results in low measurement accuracy and high labor intensity. Due to the complexity of soil, theoretical research on tillage depth is relatively scarce. In order to provide a new research direction and research idea for soil stratification, topsoil was taken as the research object of this paper. The correlation between penetration resistance and penetration depth of a probe in a cultivated soil was studied, and a mathematical model was established. There is a certain similarity between the process of spherical cavity expansion and the process of probe penetration, so we introduced the theory of spherical cavity expansion into the modeling of penetration resistance of the cultivated soil. In this paper, the spherical cavity expansion theory of unsaturated soil was used as the basis for solving the penetration resistance. And the unified strength criterion was employed as a yield condition of the soil to set a stress solution and a displacement solutionin into of the probe penetrating into the elasto-plastic zone of the cultivated soil to determine the model of expansion force. We have carried out indoor tests to revise the expansion force model. Firstly, according to the range of soil density and water content in the field, the soil densities were classified into 1.1×103kg/m3, 1.2×103kg/m3 and 1.3×103kg/m3, and the water contents were divided into 10%, 15% and 20%. In addition, the orthogonal tests were performed at different levels. The soil was put into the barrel, and the probe was inserted into the soil in the barrel at the speed of 8mm/s to determine the test values of the change of the probe penetration resistance with depth. Finally, the expansion force model was fitted with the results of the indoor test, and coefficient B was introduced to express the influence degree of density and water content on the resistance. Coefficient B was substituted into the expansion force model to obtain the penetration resistance model of the cultivated soil. Through the goodness of fit analysis of the penetration resistance model, the results show that the overall average goodness of fit of the penetration resistance modelat was up to 0.871 at different water contents and densities, which was a good fit and could present novel insights into the study relating to soil stratification theory. [ABSTRACT FROM AUTHOR]

• A mechanical claw for seedlings fetching is designed to be suitable for low-damage seedling fetching methods. • The Unet network structure is improved, and the accuracy and robustness of the segmentation of the seedling leaves are improved. • A seedling point estimation algorithm is proposed to realize the calculation of seedling fetching posture and reduce seedling damage. • The seedling fetching effects of different seedling fetching methods were explored, and the effectiveness of the low-damage seedling fetching method was verified. Automatically, seedling fetching is a critical link in the automatic planting of facility agriculture. However, the damage caused by the mechanical claws to the seedling during the seedling fetching process seriously affects the survival rate of the seedling after being planted. To reduce the damage of the mechanical claw on the seedling leaves during seedling fetching, this paper proposes a low-loss seedling fetching method and designs a set of end-effectors suitable for the low-loss seedling fetching method. The low-damage seedling fetching way consists of a semantic segmentation algorithm and a seedling fetching point estimation algorithm, which can position the seedling leaves and calculate the optimal fetch seedling angle of the mechanical claw so that the mechanical claw can avoid seedling leaves during the seedling fetching to reduce the damage to the seedling. The backbone network of the Res-Unet model is composed of Resnet50, which can accurately extract the characteristics of plug seedlings. Comparative test results with other models show that our semantic segmentation model has high accuracy in positioning seedlings, with an MIoU value of 96.49%. Finally, a seedling fetching verification experiment was conducted, and the seedling damage rate was 4.16%. Compared with the ordinary seedling fetching method, the damage rate of the seedling was reduced by 15.28%. This method can minimize damage during the seedling fetching process and provide technical support for automated planting. [ABSTRACT FROM AUTHOR]

• Firstly, an interactive interface for human–machine interaction was developed based on kinematic analysis and the system's hardware configuration. • Simultaneously, we constructed a negative feedback control model for the lateral movement speed of the conveying device. We integrated an adaptive genetic algorithm-optimized fuzzy PID control algorithm with the servo motor's S-curve acceleration and deceleration control. • In order to determine the relevant control parameters, the control accuracy of the system, and the transplanting performance indexes, simulation and comparative tests, indoor whole machine dynamic speed control transplanting tests, and field tests were carried out in terms of response speed, steady state, and robustness, to provide a theoretical basis and reference for the precise control of rice potting. Aiming at the problems of low transverse conveying accuracy and poor speed adaptability in the mechanized transplanting of rice potted seedlings, this study focused on a precision transplanting machine with an independent motor drive and designed a precision control system using a GA-Fuzzy PID controller. Firstly, an interactive interface for human–machine interaction was developed based on kinematic analysis and the system's hardware configuration. Simultaneously, we constructed a negative feedback control model for the lateral movement speed of the conveying device. We integrated an adaptive genetic algorithm-optimized fuzzy PID control algorithm with the servo motor's S-curve acceleration and deceleration control. This combination enabled variable-speed input control for the lateral movement of the conveying device, enhancing lateral conveying accuracy while reducing vibration interference. Then, we conducted comparative experiments to evaluate the system's control precision and the performance of transplanting operations. Simulation results showed that the response speed, steady state, and robustness of GA-Fuzzy PID were better than those of traditional PID and fuzzy PID control. The dynamic speed transplanting test of the indoor whole machine showed that the average transplanting success rate of the GA-Fuzzy PID control system was 92.21 %, the average maximum speed error was 1.50 mm/s, the average speed coefficient of variation was 0.19 %, and the average plant spacing coefficient of variation was 1.29 % when the walking speed was in the range of 0.26 ∼ 0.48 m/s. A comparison of the field test results and the indoor test showed that the system could still operate stably under external interference, and the system had strong robustness and operational stability. Therefore, it showed that the GA-Fuzzy PID control system can adapt to different speeds, has good control performance, and meets fast and stable design requirements. The results of this study can provide a theoretical basis and reference for the precise control of rice pot seedling transplanting. [ABSTRACT FROM AUTHOR]

Biochar can be prepared from biomass through pyrolysis in a low-oxygen or anaerobic environment. The characteristics of biochar are influenced by raw materials and the specific preparation process employed. To enhance the efficacy of biochar, various modification methods can be applied. These primarily include chemical modification, physical modification, and biological modification. This paper conducts a literature review covering the period from 2018 to 2023, focusing on biochar preparation, modification methods, and environmental applications. It is a comprehensive overview of the processes involved in preparing biochar, analyzes its characteristics, and explores various modification techniques. Additionally, the paper discusses the diverse and highly efficient application of biochar in environmental remediation, with a particular emphasis on its use in soil, water, and the atmosphere. While biochar holds significant potential for environmental remediation, a thorough understanding of its mechanism in environmental applications is still lacking. Further in-depth studies are required to elucidate the complex roles biochar plays in various environmental contexts. • A summary of the application of modified biochar to soil, water, and atmosphere remediation is presented to show its remediation effect. • Alkali modification of biochar greatly enhances its specific surface area. • Metal salt modification significantly boosts oxygen-containing functional groups in biochar. • Some research directions and future perspectives for modified biochar are highlighted. [ABSTRACT FROM AUTHOR]

Objective: To improve the accuracy of parameters used in discrete element simulation test of Chinese cabbage seeds harvesting process. Methods: Firstly, the key physical parameters of Chinese cabbage seeds were measured. According to the results, the discrete element simulation model was established and the value range of simulation test parameters was determined. Then, the actual repose angle of Chinese cabbage seeds was obtained by physical accumulation test using bottomless conical cylinder lifting method. Plackett-Burman test, steepest climb test, Box-Behnken test and parameter optimization test were carried out in sequence with the actual angle of repose as the response value. Finally, the obtained parameters are verified. Results: 1. The Plackett-Burman test showed that the seed-seed rolling friction coefficient, the seed-steel rolling friction coefficient, the seed-seed static friction coefficient, and the seed-steel static friction coefficient had significant effects on the repose angle of Chinese cabbage seeds (P<0.05). 2. The optimization test showed that the seed-seed rolling friction coefficient was 0.08, the seed-steel rolling friction coefficient was 0.109, the seed-seed static friction coefficient was 0.496, and the seed-steel static friction coefficient was 0.415. 3. The validation test showed that the repose angle of Chinese cabbage seeds under such parameter was 23.62°, and the error with the repose angle of the physical test was 0.73%. Conclusion: The study show that the discrete element simulation parameters of Chinese cabbage seeds model and calibration are reliable, which can provide reference for the discrete element simulation of Chinese cabbage seeds. [ABSTRACT FROM AUTHOR]

Appropriate discrete element modelling (DEM) parameters are essential for accurate prediction of soil properties and disturbance. This study aims to provide an efficient method for accurately determining DEM parameters of a loose cohesive soil in a range of soil conditions. DEM parameters of the loose cohesive soil modelled using hysteretic spring and linear cohesion contact models were determined by a combination of Plackett–Burman, steepest ascent and central composite tests. The accuracies of DEM models developed under different soil moisture contents (0.27–22%) were evaluated using slumping angle of repose test and funnelling angle of repose test. Heap angles under different soil moisture contents decreased quadratically with time with determination coefficients ranging from 0.750 to 0.969. For the given soil, friction or rolling friction coefficient of soil–soil changed with a cubic function as moisture content increased. Similar coefficients of friction (0.23–0.25) and rolling friction (0.038–0.049) of soil–soil were found for moisture contents from 12 to 22%. Based on ANOVA outputs, soil properties tested were significantly affected by soil moisture content at p < 0.05, including angle of repose (AOR), releasing time, yield strength of soil and coefficients of friction and rolling friction of soil-steel. Low relative errors (<2.8%) were found for simulated AORs using developed soil DEM models under different soil moisture contents. Simulated releasing time agreed well with high-speed video measurements. The suggested method has good potential to accurately determine DEM parameters for loose cohesive soils in a range of conditions. • An efficient method for determining cohesive soil DEM parameters has been developed. • Method has potential to accurately determine DEM parameters for loose cohesive soil. • Soil DEM models with 7 levels of moisture content were calibrated using method. • Relationships between moisture content and soil properties crucial for field models. [ABSTRACT FROM AUTHOR]

• An online monitoring system for the uprightness of transplanting machines based on machine vision and deep learning is constructed is constructed. • A seedling planting status dataset is constructed and an efficient algorithm YOLO-RDS for the detection of the planting posture of seedlings is proposed. • Improved traditional (HBB) object detection box, the introduction of angular predictor headers, and the RWBF detection head constructed. • Integrated development of a seedling quality monitoring system and field experiments conducted. During the mechanized transplanting process, the planting angle and standing posture of seedlings are essential factors in evaluating the survival of the planting, and the uprightness of the planting is an essential indicator in evaluating the planting quality. With the gradual expansion of mechanization, the traditional method of manually checking the uprightness of mechanized planting has gradually become inapplicable. To address this problem, an online monitoring system for the uprightness of transplanting machines based on machine vision and deep learning is constructed. The system mainly consists of an image acquisition device, a vehicle-mounted computer, and an interactive interface. Furthermore, to improve the identification errors caused by the unstructured environment in the field, a field planting uprightness monitoring device was designed to integrate LED light sources, photoelectric sensors, a CCD camera, and light hoods. An efficient algorithm YOLO-RDS for the detection of the planting posture of seedlings is proposed using a single-stage detection paradigm. First, the traditional (HBB) target detection box is improved and the circle smooth label is introduced to adapt to different seedling planting inclined postures. Secondly, the RWBF detection head network was constructed, and the stem incline angle was extracted using adaptive rotation to calculate the actual incline angle of the seedlings in three-dimensional space. Experimental results show that YOLO-RDS is better than the classic rotating target detection method, the average accuracy (mAP) is 97%, and FPS is 22. By encapsulating the algorithm, a seedling planting uprightness monitoring system was integrated and developed. Field experiments show that the average relative error rate of uprightness identification monitored by the system is 3.09%, the accuracy rate is 92.56%, and the absolute angle error is 0°-3°. The system has good accuracy and stability, meets the requirements for seedling uprightness monitoring, and can provide technical support for the quality evaluation of seedling transplanter operations. [ABSTRACT FROM AUTHOR]

• Design a high-speed and precise seeding control system for rice seedlings, and develop human–computer interaction software. • The dual position feedback adjustment (DPFA) method is used to improve the high-speed sowing effect of rice pot seedlings. • Use fuzzy PID to optimize the control system to improve system response time, speed regulation accuracy and robustness. • Explore the changing rules of seeding effect and verify the effectiveness of the control system based on DPFA. To improve the alignment accuracy of the mechanical drum-type rice bowl seedling raising precision seeding line under the high-speed sowing operation state between the seed outlet and the bowl hole. This paper proposes a high-speed and precise positioning seeding control system for rice seedlings based on dual position feedback adjustment (DPFA). Based on Siemens PLC controller, through sensor selection and installation, controller parameter acquisition and calculation, and human–computer interaction software development. We have completed the design of the high-speed and precise alignment seeding control system for rice pot seedlings. Realize the dual position detection of the mechanical drum-type seed wheel and the 406-hole tray. Applying the fuzzy PID controller optimized the dual-position feedback regulation rice pot seedling high-speed and precise alignment planting control system. The response time was shortened to 0.0432 s, the overshoot was 4.5 %, and the adjustment time was 0.1105 s. The sowing test was conducted with sowing speed and height as the main influencing factors and sowing uniformity, leakage rate, and sowing pass rate as the evaluation indexes. The test results show that the sowing speed is within 500 trays/h ∼ 600 trays/h, the sowing uniformity is higher than 65 %, the missed seeding rate is less than 1.5 %, and the qualified rate of sowing is above 94 %. Among them, the maximum sowing uniformity was 66.27 %, the minimum value of missing seeding was 0.89 %, and the sowing qualification rate was 97.06 %. The accuracy rate of material shortage alarms is 100 %. The DPFA-based high-speed and precise alignment sowing control system for rice pot seedlings can realize the exact alignment control function of the mechanical drum-type seedling raising and sowing line. [ABSTRACT FROM AUTHOR]

This paper reports on the investigation of the gasification from biomass using a laboratory scale fluidized bed gasifier. Main influencing factors, such as the temperature, steam flow rate and CaO to biomass mass ratio were studied. A model was developed for hydrogen-rich gas production from biomass via using ASPEN PLUS software. Sawdust was used as raw material for this study. Different operating conditions were considered and the model was verified. The results showed that the production of H2 and CO2 increased while the production of CO and CH4 decreased with increasing temperature. When the steam was used as the gasifying agent at different temperatures, the production of H2 increased significantly and its growth rate was ~48%. The increase of CaO to biomass mass ratio from 0% to 50% resulted in hydrogen content increasing from 40% to 68%. Simulation results showed that the model was suitable for biomass gasification process, which could provide some reference value for further biomass gasification experiment. [ABSTRACT FROM AUTHOR]

In recent years, many imaging systems have been developed to monitor the physiological and behavioral status of dairy cows. However, most of these systems do not have the ability to identify individual cows because the systems need to cooperate with radio frequency identification (RFID) to collect information about individual animals. The distance at which RFID can identify a target is limited, and matching the identified targets in a scenario of multitarget images is difficult. To solve the above problems, we constructed a cascaded method based on cascaded deep learning models, to detect and segment a cow collar ID tag in an image. First, EfficientDet-D4 was used to detect the ID tag area of the image, and then, YOLACT++ was used to segment the area of the tag to realize the accurate segmentation of the ID tag when the collar area accounts for a small proportion of the image. In total, 938 and 406 images of cows with collar ID tags, which were collected at Coldstream Research Dairy Farm, University of Kentucky, USA, in August 2016, were used to train and test the two models, respectively. The results showed that the average precision of the EfficientDet-D4 model reached 96.5% when the intersection over union (IoU) was set to 0.5, and the average precision of the YOLACT++ model reached 100% when the IoU was set to 0.75. The overall accuracy of the cascaded model was 96.5%, and the processing time of a single frame image was 1.92 s. The performance of the cascaded model proposed in this paper is better than that of the common instance segmentation models, and it is robust to changes in brightness, deformation, and interference around the tag. [ABSTRACT FROM AUTHOR]

• Segmentation of seedling individuals and recognition of seedlings vacancy were realized. • Fuzzy C-means (FCM) method was developed to detect the health of potted seedlings. • An automatic transplanting machine is designed to verify anti-leakage planting system. • The accuracy of seedling identification was 97.33% at transplanting speed of 70 plants/min. Selective operation of potted seedlings on transplanter is the fundamental way to improve the survive rate after the transplanting operation. However, due to the problems of high transplanting speed and complicated working condition of the transplanter, higher requirements for information detection, mechanism control and their coordination are needed. In this paper, the Fuzzy C-means (FCM) method is used to detect the image information of potted seedlings with high throughput. Segmentation of seedling individuals and recognition of seedlings vacancy were realized. An automatic transplanting machine is designed to verify the reliability of the proposed on-line FCM method. The proposed algorithm is implemented in VB and MATLAB, and is used to control the automatic transplanting machine. The algorithm can effectively identify the status of the seedling during the transplanting process. The test results show that when the speed of seedling delivery was 70 plants per minute, the rate of leakage planting was 1.08%, and the accuracy of seedling identification was 97.33%. The monitoring system designed in this paper can realize the vacancy detection of potted seedlings. With the use of information detection by FCM, the leakage rate of automatic transplanting machine can be reduced significantly. [ABSTRACT FROM AUTHOR]

Conservation tillage technology (CTT) is a promising and efficient method for farmland protection and utilization. As one of the core technologies of CTT, mechanical subsoiling has been identified as an effective practice to disrupt hardpans and reduce soil bulk density to promote better water infiltration and crop root development. The tillage force during subsoiling (TFDS) is extremely high and methods for reducing the TFDS have been developed. A comprehensive summary about typical subsoiling tools and corresponding tool characteristics and current methods for TFDS reduction were reported in this review. The TFDS reduction methods were introduced in five aspects: biomimetic, oscillatory, lubrication, experimental, and others (i.e., line element design, layered subsoiling, crop straw and root anti-blocking, high-pressure gas splitting, rotary subsoiling, and selection of working depth and time). The mechanisms and limitations of current methods were analysed. The effect of various methods on draught forces of subsoiling tools with similar working parameters were quantitatively investigated and compared using statistical analyses. This review also recommends four future research directions about further reduction of TFDS to promote the development of TFDS reduction technology and CTT. • Subsoiling tillage force reduction methods summarized and statistically compared. • Research directions for further reducing subsoiling tillage forces recommended. • Study is useful for subsoiling with lower tillage forces and power consumption. [ABSTRACT FROM AUTHOR]

• Lyophilization can induce the change of lipoprotein structure in egg yolk. • Lipoprotein aggregation results in decreased solubility of egg yolk powder. • The content of active IgY in yolk decreased after lyophilization. • Excipients can improve the properties of lyophilized egg yolk powder. The preparation of egg yolk powder (EYP) with excellent solubility and high retention of active IgY is of great significance for increasing the added value and promoting the application of EYP. A new method of preparing EYP by microwave-assisted freeze-drying (MFD) was researched. Confocal laser scanning microscopy results demonstrated that the supplementation of excipients (sucrose, trehalose, and maltodextrin) could inhibit lipoproteins aggregation in egg yolk induced by freezing. Scanning electron microscopy indicated that drying further damaged the structure of lipoproteins in EYP, leading to lipid separation from it. FTIR and fluorescence spectra confirmed this finding, indicating that excipients enhance protein stability. Compared with conventional freeze-drying (FD), EYP prepared by MFD, particularly that containing excipients, had higher solubility (63 g/100 g), active antibody retention rate and shorter drying time. Therefore, excipients can significantly improve the solubility and stability of EYP and the retention rate of active IgY. [ABSTRACT FROM AUTHOR]

• Hindquarter of cow's back in depth image was located. • 3D structure feature map of point clouds was constructed. • Two-step EfficientNet model was built for BCS. • 91.21% images were scored with the error less than 0.25. Body condition score (BCS) is an important indicator to evaluate the nutritional and health status of perinatal dairy cows. The insufficient accuracy of the automatic body condition score is the core bottleneck restricting the popularization and application of this technology. To further improve the accuracy of automatic scoring of cow body condition, a 3D structure feature map based on convex hull distance of point clouds was constructed and used as the input of a two-level model based on EfficientNet network, which realized the improvement of recognition accuracy within the error of 0.25 in the automatic scoring of cow body condition. First, the depth image of the cow's back was preprocessed to obtain the point cloud from the hook bone to the needle bone region containing the main body condition information. Second, the point cloud was voxelized and convex hulled, and the distance between each peripheral voxel and the nearest convex hull surface was calculated and projected to the X-Y plane to obtain the structural feature map. Then, a two-level classification model based on the EfficientNet network was constructed, and the whale optimization algorithm (WOA) was used to optimize the network scaling coefficient in the model. Finally, 5119 depth images of 77 cows were used to train and test the model, and the dataset ratio was 1:1. The results showed that when the BCS value was in the range of 2.25 to 3.75 and the two-class classification limit was 2.75, the proportions of images with recognition errors less than 0.25 and 0.50 were 91.21% and 97.60%, respectively, and the average recognition speed was 3.446 frames/s. The recognition effect was better than that of the MobileNet-V2, XceptionNet and LeNet-5 network models. This method could realize the noncontact assessment of individual body conditions of dairy cows in large-scale farms and had the characteristics of high accuracy, strong applicability and low cost. [ABSTRACT FROM AUTHOR]

• The edge recognition technology of seedlings is combined with the end effector of the transplantation manipulator to plan the path, thereby reducing the transplantation loss. • During the pipeline operation of the seedling transplanting, only the time for the first seedling edge recognition is increased. Seedling transplantation is the key link in the automation of protected horticulture, and the transplantation quality will directly affect the crop yield. In the previous research, our research group developed a prototype of a low-loss transplantation robot for plug seedlings based on machine vision. On this basis, a method of planning seedling path by integrating seedling edge recognition technology and end effector of transplanting manipulator is proposed to reduce the loss of seedling transplantation. Firstly, the RGB image and depth image of the whole row of seedlings were obtained from the side of the plug seedling with Intel RealSense D415 depth camera. Then, the extreme point coordinates E (X cb , Y ca) of seedling edge were obtained by edge recognition algorithm, and the coordinate value of z-axis was known. Taking point E as the starting point the transplanting manipulator picked up seedlings from the E point in an "L" path, which can reduce the damage of transplanting manipulator to seedling stems and leaves during seedling taking, thereby reducing the transplanting loss. Through three-factor four-level orthogonal test, it was determined that the distance between the depth camera and the plug tray was 600 mm, the height from the horizontal plane was 135 mm, and the light intensity was level 7. Then, a calibration test of extreme points on the edge of plug seedlings was carried out, and the calibration success rate was 98.4%. The deviation of X coordinate was within 5 mm, and the average ratio of deviation was 12.8%. The deviation of Y coordinate was within 4 mm, and the average ratio of deviation was 3%. The quality of image and the accuracy of depth information were the main causes of the deviation. Then, the comparative experiments of routine transplantation group, fixed transplantation group and machine vision transplantation group were carried out. The results showed that compared with the conventional transplantation group, the injury rate of machine vision group decreased by 11.11%, and the average time of single transplantation increased by 0.029 s. Compared with the fixed transplantation group, the injury rate increased by 0.46%, and the average transplantation time decreased by 0.238 s. Because the edge recognition time and the transplantation time intersected and did not interfere, only the time for the first seedling edge recognition was increased in the pipeline operation. Therefore, this method can reduce the damage rate while ensuring the efficiency. This study can provide a reference for reducing the injury rate of leafy vegetable seedlings transplantation. [ABSTRACT FROM AUTHOR]

• Intelligent transplanting machine hydraulic control system is designed to transplant seedlings. • Operation parameters can be adjusted in real time through fuzzy PID control system. • The intelligent control system can improve the dynamic response speed of the hydraulic system. At present, there is a problem that the output accuracy of the transplanting manipulator is low due to interference from external factors. To solve the problem of low output accuracy of the transplanting manipulator caused by the interference of external factors to the displacement tracking of the hydraulic seedling picking-up system, a control method of the transplanting manipulator based on the fuzzy PID control strategy is proposed. First of all, the hydraulic seedling taking system was modeled and analyzed, and the total output displacement of hydraulic cylinder piston when the valve core input displacement and external load force were acted on simultaneously was given. Then the PID control method of the hydraulic seedling picking-up system was designed, and by combining the fuzzy control theory with PID control theory, a fuzzy PID controller was designed, which was capable of on-line adjusting the PID parameters. MATLAB/Simulink was used to simulate the control system, and the control effects of conventional PID control and fuzzy PID control on the hydraulic seedling picking-up system were compared. The results showed that when k p value took 4, k i value took 10 and k d value took 0.001, the output response curve of the traditional PID control reaches a stable state in 5 s, while the fuzzy PID control only takes 2.2 s under the same conditions, successfully increasing the step response signal time of the system by 56%. The results showed that fuzzy PID control introduced helped the hydraulic seedling picking-up system to overcome its disadvantages of non-linearity and low control accuracy, and improved the stability and dynamic performance of the system. [ABSTRACT FROM AUTHOR]

In order to improve the automation and reliability of the mechanized transplanting, a vegetable seedling automatic conveying system was designed. The functional execution component of the system consists of a tank-wheel storage seedling mechanism, a seedling tray conveyor, and a hanging cup circulating conveying mechanism. Combined with the communication technology between host computer and programmable logic controller (PLC), the user interface software of automatic conveying system for potted vegetable was developed by using Visual Studio to control and monitor the transmission progress. The PVDF pressure sensor was used to detect the pressure change of the seedling storage disk to obtain the information of the loading seedling tray. The limit switch and stroke switch were combined to obtain the information of the push rod and feed plate. The timing of each component is controlled by the PLC controller combined with the feedback information of each sensor. The test results show the effect of taking an individual seedling is excellent, the error rate of the pushing mechanism is about 2.08%, and the success rate was 97.91%. [ABSTRACT FROM AUTHOR]