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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]

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]

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 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]