Editor’s Choice Articles

Plant litter decomposition and its effect on soil nutrients are important parts of the ecosystem material cycle, and understanding these processes is key for species selection and allocation to promote the effective use of litter in ecological tea gardens. In this study, the in situ litter decomposition method was used to examine the decomposition characteristics of leaf litter of Cinnamomum glanduliferum, Betula luminifera, Cunninghamia lanceolata, Pinus massoniana, and Camellia sinensis prunings in the Jiu’an ecological tea garden in Guizhou and their effects on soil nutrients. The results showed that the litter decomposition rate of broad-leaved tree species was higher than that of coniferous tree species, with a half-life of 1.11–1.75a and a turnover period of 4.79–7.57a. There are two release modes of nutrient release from litter: direct release and leaching–enrichment–release. Different litters make different contributions to soil nutrients; Betula luminifera and Cinnamomum glanduliferum litter increased the contents of soil organic carbon, soil total nitrogen, and soil hydrolyzed nitrogen. Betula luminifera litter increased the content of soil total phosphorus, soil available phosphorus, and soil available potassium, and Pinus massoniana litter increased the content of soil total potassium and soil available potassium; therefore, it is concluded that the decomposition of Betula luminifera litter had a positive effect on soil nutrient content. Thus, Betula luminifera is a good choice for inclusion in ecological tea gardens to increase their nutrient return capacity, maintain fertility, and generally promote the ecological development of tea gardens. Full article

Fall armyworm (FAW), Spodoptera frugiperda (Lepidoptera: Noctuidae), is a highly invasive polyphagous pest that causes great economic losses to agricultural production. Emamectin benzoate (EMB) is one of the most popular biopesticides with high antipest, anti-parasitic and anti-nematode activities and low toxicity. The present study was conducted to determine the lethality of EMB to FAW for 24 h. Sublethal effects of EMB on FAW parental and offspring generations were also assessed. LC₁₀, LC₂₀ and LC₅₀ EMB for 24 h on FAW third instar larvae were 0.0127 mg/L, 0.0589 mg/L, and 0.1062 mg/L, respectively. A low dose of sublethal concentrations of EMB could significantly influence the life cycle of FAW parental and offspring generations. Sublethal concentration (LC₂₀) of EMB significantly prolonged the pupal period of male and increased the pupal weight of male but not of female, and significantly delayed the oviposition period and longevity of adult FAW. In the FAW offspring generation, sublethal concentrations significantly increased the mortality of offspring pupae and pre-adults, and reduced the development time of offspring larvae and pre-adult male and female. Sublethal concentrations (LC₁₀ and LC₂₀) of EMB significantly decreased the FAW oviposition period. However, only LC₁₀ significantly reduced FAW F₁ female fecundity. No significant difference was found in the intrinsic rates of natural increase (r_m), finite rate of population increase (λ), and net reproductive rate (R₀) of FAW offspring exposed to sublethal concentrations. This is the first study to determine the sublethal concentrations of EMB on the life table parameters of two FAW generations. These findings can provide important implications for the rational utilization of FAW insecticides. Full article

Traditional plant disease diagnosis methods are mostly based on expert diagnosis, which easily leads to the backwardness of crop disease control and field management. In this paper, to improve the speed and accuracy of disease classification, a plant disease detection and classification method based on the optimized lightweight YOLOv5 model is proposed. We propose an IASM mechanism to improve the accuracy and efficiency of the model, to achieve model weight reduction through Ghostnet and WBF structure, and to combine BiFPN and fast normalization fusion for weighted feature fusion to speed up the learning efficiency of each feature layer. To verify the effect of the optimized model, we conducted a performance comparison test and ablation test between the optimized model and other mainstream models. The results show that the operation time and accuracy of the optimized model are 11.8% and 3.98% higher than the original model, respectively, while F1 score reaches 92.65%, which highlight statistical metrics better than the current mainstream models. Moreover, the classification accuracy rate on the self-made dataset reaches 92.57%, indicating the effectiveness of the plant disease classification model proposed in this paper, and the transfer learning ability of the model can be used to expand the application scope in the future. Full article

Rice production involves abundant water and fertilizer inputs and is prone to nitrogen (N) loss via surface runoff and leaching, resulting in agricultural diffuse pollution. Based on a two-season paddy field experiment in Jiangsu Province, China, field water and N dynamics and their balances were determined with the well-calibrated HYDRUS-1D model. Then, scenarios of different controlled drainage and N fertilizer applications were simulated using the HYDRUS-1D model to analyze the features and factors of N loss from paddy fields. Evapotranspiration and deep percolation were the two dominant losses of total water input over the two seasons, with an average loss of 50.9% and 38.8%, respectively. Additionally, gaseous loss of N from the whole soil column accounted for more than half of total N input on average, i.e., ammonia volatilization (17.5% on average for two seasons) and denitrification (39.7%), while the N uptake by rice accounted for 37.1% on average. The ratio of N loss via surface runoff to total N input exceeded 20% when the N fertilizer rate reached 300 kg ha⁻¹. More and longer rainwater storage in rice fields under controlled drainage reduced surface runoff losses but increased the risk of groundwater contamination by N leaching. Therefore, compared with raising the maximum ponding rainwater depth for controlled drainage, optimizing N fertilizer inputs may be more beneficial for controlling agricultural diffuse pollution by reducing N loss via surface runoff and leaching. The HYDRUS-1D model provides an approach for the quantitative decision-making process of sustainable agricultural water and N management. Full article

Yield prediction is of great significance in agricultural production. Remote sensing technology based on unmanned aerial vehicles (UAVs) offers the capacity of non-intrusive crop yield prediction with low cost and high throughput. In this study, a winter wheat field experiment with three levels of irrigation (T1 = 240 mm, T2 = 190 mm, T3 = 145 mm) was conducted in Henan province. Multispectral vegetation indices (VIs) and canopy water stress indices (CWSI) were obtained using an UAV equipped with multispectral and thermal infrared cameras. A framework combining a long short-term memory neural network and random forest (LSTM-RF) was proposed for predicting wheat yield using VIs and CWSI from multi-growth stages as predictors. Validation results showed that the R² of 0.61 and the RMSE value of 878.98 kg/ha was achieved in predicting grain yield using LSTM. LSTM-RF model obtained better prediction results compared to the LSTM with n R² of 0.78 and RMSE of 684.1 kg/ha, which is equivalent to a 22% reduction in RMSE. The results showed that LSTM-RF considered both the time-series characteristics of the winter wheat growth process and the non-linear characteristics between remote sensing data and crop yield data, providing an alternative for accurate yield prediction in modern agricultural management. Full article

Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and late crop disease stages include the area of disease and color of disease. This also poses additional difficulties for CNN models. Here, we propose a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases. The main components of GrapeNet are residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules. The residual blocks are used to deepen the network depth and extract rich features. To alleviate the CNN performance degradation associated with a large number of hidden layers, we designed an RFFB module based on the residual block. It fuses the average pooled feature map before the residual block input and the high-dimensional feature maps after the residual block output by a concatenation operation, thereby achieving feature fusion at different depths. In addition, the convolutional block attention module (CBAM) is introduced after each RFFB module to extract valid disease information. The obtained results show that the identification accuracy was determined as 82.99%, 84.01%, 82.74%, 84.77%, 80.96%, 82.74%, 80.96%, 83.76%, and 86.29% for GoogLeNet, Vgg16, ResNet34, DenseNet121, MobileNetV2, MobileNetV3_large, ShuffleNetV2_×1.0, EfficientNetV2_s, and GrapeNet. The GrapeNet model achieved the best classification performance when compared with other classical models. The total number of parameters of the GrapeNet model only included 2.15 million. Compared with DenseNet121, which has the highest accuracy among classical network models, the number of parameters of GrapeNet was reduced by 4.81 million, thereby reducing the training time of GrapeNet by about two times compared with that of DenseNet121. Moreover, the visualization results of Grad-cam indicate that the introduction of CBAM can emphasize disease information and suppress irrelevant information. The overall results suggest that the GrapeNet model is useful for the automatic identification of grape leaf diseases. Full article

Due to a lack of an accurate model in finite element simulation of mechanized harvesting of wild chrysanthemum, the stem of wild chrysanthemum in the harvesting period is taken as the research object. ANSYS Workbench 19.0 software and LS-DYNA software (LS-PrePOST-4.3-X64) are used to calibrate the finite element simulation model of wild chrysanthemum stem cutting. The stem diameter distribution at the cutting height of the chrysanthemum is obtained. The maximum shear forces at different diameters (7 mm, 8 mm, 9 mm, 10 mm, and 11 mm) within the cutting range are determined as 120.0 N, 159.2 N, 213.8 N, 300.0 N, and 378.2 N, respectively, by using a biomechanical testing machine and a custom-made shear blade. The Plastic_Kinematic failure model is used to simulate the cutting process by the finite element method. The Plackett–Burman test is employed to screen out the test factors that significantly affect the results, namely, the yield stress, failure strain, and strain rate parameter C. The regression model between the shear force and significant parameters is obtained by central composite design experiments. To obtain the model parameters, the measured values are substituted into the regression equation as the simulation target values. In other words, the yield stress is 17.96 MPa, the strain rate parameter C is 87.27, and the failure strain is 0.0387. The maximum shear force simulation test is carried out with the determined parameters. The results showed that the maximum error between the simulated and the actual value of the maximum shear force of wild chrysanthemum stems with different diameters is 7.8%. This indicates that the calibrated parameters of the relevant stem failure model can be used in the finite element method simulation and provide a basis for subsequent simulations. Full article

The optimization of soil sampling is very important in precision agriculture. The main aim of this study was to evaluate the relationships between selected spectral indices (NDWI—normalized difference water index and NDVI—normalized difference vegetation index) and apparent soil electrical conductivity (EC) with soil nutrient content (phosphorus, potassium, and magnesium) and pH. Moreover, the usefulness of these variables for the delineation of within-field management zones was assessed. The study was conducted in 2021 in central Poland at three maize fields with a total area approximately 100 ha. The analyses were performed based on 47 management zones, which were used for soil sampling. Significant positive correlations were observed between the NDVI for the bare soil and all the studied nutrient contents in the soil and pH. A very strong positive correlation was observed between the soil EC and the potassium content and a moderate correlation was found with the magnesium content. A multiple-regression analysis proved that the soil nutrient content, especially potassium and phosphorus, was strongly related to the EC and NDVI. The novelty of this study is that it proves the relationships between soil and the crop attributes, EC and NDVI, which can be measured at field scale relatively simply, and the crucial soil nutrients, phosphorus and potassium. This allows the results to be used for optimized variable-rate fertilization. Full article

The complex backgrounds of crop disease images and the small contrast between the disease area and the background can easily cause confusion, which seriously affects the robustness and accuracy of apple disease- identification models. To solve the above problems, this paper proposes a Vision Transformer-based lightweight apple leaf disease- identification model, ConvViT, to extract effective features of crop disease spots to identify crop diseases. Our ConvViT includes convolutional structures and Transformer structures; the convolutional structure is used to extract the global features of the image, and the Transformer structure is used to obtain the local features of the disease region to help the CNN see better. The patch embedding method is improved to retain more edge information of the image and promote the information exchange between patches in the Transformer. The parameters and FLOPs (Floating Point Operations) of the model are significantly reduced by using depthwise separable convolution and linear-complexity multi-head attention operations. Experimental results on a complex background of a self-built apple leaf disease dataset show that ConvViT achieves comparable identification results (96.85%) with the current performance of the state-of-the-art Swin-Tiny. The parameters and FLOPs are only 32.7% and 21.7% of Swin-Tiny, and significantly ahead of MobilenetV3, Efficientnet-b0, and other models, which indicates that the proposed model is indeed an effective disease-identification model with practical application value. Full article

In order to enable the picking robot to detect and locate apples quickly and accurately in the orchard natural environment, we propose an apple object detection method based on Shufflenetv2-YOLOX. This method takes YOLOX-Tiny as the baseline and uses the lightweight network Shufflenetv2 added with the convolutional block attention module (CBAM) as the backbone. An adaptive spatial feature fusion (ASFF) module is added to the PANet network to improve the detection accuracy, and only two extraction layers are used to simplify the network structure. The average precision (AP), precision, recall, and F1 of the trained network under the verification set are 96.76%, 95.62%, 93.75%, and 0.95, respectively, and the detection speed reaches 65 frames per second (FPS). The test results show that the AP value of Shufflenetv2-YOLOX is increased by 6.24% compared with YOLOX-Tiny, and the detection speed is increased by 18%. At the same time, it has a better detection effect and speed than the advanced lightweight networks YOLOv5-s, Efficientdet-d0, YOLOv4-Tiny, and Mobilenet-YOLOv4-Lite. Meanwhile, the half-precision floating-point (FP16) accuracy model on the embedded device Jetson Nano with TensorRT acceleration can reach 26.3 FPS. This method can provide an effective solution for the vision system of the apple picking robot. Full article

In China, the excessive application and improper disposal of chemical inputs have posed a great threat to the agricultural ecological environment and human health. The key to solve this problem is to promote the sustainable behaviors of farmers’ agricultural green production (AGP). Based on the micro-survey data of 652 farmers, this study adopts the binary probit model to investigate the impacts of risk perception and environmental regulation on the sustainable behaviors of farmers’ AGP. Results show that both risk perception and environmental regulation have significant effects on farmers’ willingness to engage in sustainable behaviors. Moreover, environmental regulation can positively adjust risk perception to improve farmers’ willingness to engage in sustainable behaviors. In terms of the two-dimensional variables, economic risks create the greatest negative impacts, and their marginal effect is 7.3%, while voluntary regulation creates the strongest positive impacts, and its marginal effect is 14.1%. However, both constrained and voluntary regulation have an enhanced moderating effect, where the effects of voluntary regulation are more remarkable. This is mainly because the environmental regulation policy signed by the government and farmers through the letter of commitment can inspire farmers to continue to implement green agricultural production from the deep heart. Therefore, government policies should constantly reduce farmers’ risk perception in terms of economic input, and adopt restrictive behaviors measures, such as regulatory punishment and voluntary contract, to promote their sustainable behaviors of AGP to the maximum extent. Full article

This research was performed to investigate the effects of milk thistle (MT), toxin binder (TB) and marine algae (Spirulina platensis; SP) on the performance, blood indices, humoral immunity and cecal microbiota of broiler chickens exposed to aflatoxin-B₁ (AFB1). A total of 300 one-day-old male chicks were equally divided into five treatments, with six replicates with 10 birds per treatment. Dietary treatments included: (T₁) a control diet (without any feed additive or AFB1); (T₂) control diet + 0.6 mg AFB₁/kg; (T₃) T₂ + 10 g/kg MT; (T₄) T₂ + 1 g/kg TB; and (T₅) T₂ + 10 g/kg SP. BWG and FI were found to be considerably reduced in broilers given AFB1-contaminated diets (p < 0.05). The FCR was negatively influenced in birds fed AFB1-contaminated diets (p < 0.05). MT, TB, and SP powders also reduced the deleterious effects of AFB1 on the growth of chickens (p < 0.05). In comparison with the control birds and the other treatments, broilers given AFB1-contaminated diets had a higher relative weight of abdominal fat (p < 0.05). The feeding of AFB1 resulted in a substantial rise in AST and ALT activity (p < 0.05). MT, TB, and SP powders significantly decreased blood AST and ALT activity in broilers (p < 0.05). The AFB1 and MT groups had the lowest skin thickness (p < 0.05) twenty-four hours after injection. The phytohemagglutinin injection results showed that the TB and SP were more efficient than the other additives in removing toxins from the feed sources (p < 0.05). The antibody titer against sheep red blood cells (SRBCs) was lower in the AFB1 group compared to the control group at 28 days of age (p < 0.05). When comparing AFB1-fed chicks to the control treatment, there was a significant (p < 0.05) concentration of cecal Coliform bacteria. When MT, TB, and SP powders were added to AFB1-contaminated diet, cecal Coliforms were decreased (p < 0.05). When fed AFB1-contaminated diets, it can be concluded that MT, TB, and SP are suitable for supporting growth performance, immunological function, and the serum biochemical parameters of broiler chickens. Full article

Due to the diversity and the different distribution conditions of agricultural products, split delivery plays an important role in the last mile distribution of agricultural products distribution. The time-dependent split delivery green vehicle routing problem with multiple time windows (TDSDGVRPMTW) is studied by considering both economic cost and customer satisfaction. A calculation method for road travel time across time periods was designed. A satisfaction measure function based on a time window and a measure function of the economic cost was employed by considering time-varying vehicle speeds, fuel consumption, carbon emissions and customers’ time windows. The object of the TDSDGVRPMTW model is to minimize the sum of the economic cost and maximize average customer satisfaction. According to the characteristics of the model, a variable neighborhood search combined with a non-dominated sorting genetic algorithm II (VNS-NSGA-II) was designed. Finally, the experimental data show that the proposed approaches effectively reduce total distribution costs and promote energy conservation and customer satisfaction. Full article

The water resources carrying capacity (WRCC) strongly determines the agricultural development in arid areas. Evaluation of WRCC is important in balancing the availability of water resources with society’s economic and environmental demands. Given the demand for sustainable utilization of agricultural water resources, we combine the water stress index and comprehensive index of WRCC and use multi-source data to evaluate agricultural WRCC and its influencing factors at the township scale. It makes up for the deficiencies of current research, such as the existence of single-index evaluation systems, limited calibration data, and a lack of a sub-watershed (i.e., township) scale. By applying multi-source data, this study expands the spatial scale of WRCC assessment and establishes a multidimensional evaluation framework for the water resources in dryland agriculture. The results indicate water stress index ranges from 0.52 to 1.67, and the comprehensive index of WRCC ranges from 0.25 to 0.70, which are significantly different in different types of irrigation areas and townships. Water quantity and water management are key factors influencing WRCC, the water ecosystem is an area requiring improvement, and the water environment is not a current constraint. Different irrigation areas and different types of townships should implement targeted measures to improve WRCC. Full article

The issue of food quality and safety is a major concern. Rice is considered one of the three staple foods. Rice quality and safety problems have occurred frequently, which seriously affect human health. The rice supply chain is characterized by complex links, discrete data, and numerous types of hazardous substances. Strengthening the information management and control capabilities of the rice supply chain is an important means to ensure the quality and safety of rice. Based on multi-chain collaboration, we have conducted research on information management and control of the rice supply chain. First, a multi-chain collaborative model of “blockchain + sub-chain” is designed. Based on this model, the following four mechanisms are designed: a trusted chain mechanism, a multi-level sub-chain encryption mechanism, a trusted supervision mechanism, and a hierarchical consensus mechanism. These mechanisms jointly serve the multi-chain collaborative management and control of the rice supply chain information. Secondly, smart contracts and operating procedures are designed, and a comparative analysis of them is executed. Finally, the design and implementation of the prototype system is carried out, and an example is verified and analyzed in a grain enterprise. Results show that this model serves the information supervision of the rice supply chain by studying the multi-chain collaboration. The study solves the real-time data interaction problem between each link of the rice supply chain. The credible management of information and control of the rice supply chain is accomplished. This study applies new information technology to the coordination and resource sharing of the food supply chain and provides ideas for the digital transformation of the food industry. Full article

The aim of this paper is to optimize the working parameters of the arc-shaped nail-tooth roller-type recovery machine for sowing layer residual film. Firstly, the tooth roller device of the residual film recovery machine is designed, and the main working parameters affecting the operation of the machine and the value range of each parameter are determined through the analysis of the operation process. Secondly, virtual simulation technology is used to establish a virtual simulation model of the interaction process between the tooth roller device and soil. At the same time, taking the soil-hilling quantity as the index, we build a quadratic regression mathematical model with three factors—the forward speed, rotation speed, and working depth—using the Box–Behnken method. Consequently, the analysis of the simulation results show that the order of the most significant factors is working depth, rotation speed, and forward speed. The optimal combination of working parameters are as follows: a forward speed of 4.5 km/h, a rotation speed of 43.2 r/min, and a working depth of 100.0 mm. Meanwhile, the predicted value of the soil-hilling quantity is 23.1 kg. Finally, we carried out field tests using the optimal combination parameters; the results show that the normal residual film collection rate is 66.8%, the soil-hilling quantity is 24.2 kg, and the relative error between the test value and the predicted value is 4.8%. This indicates that the devised DEM simulation model can be used to predict the operational performance of the tooth roller device in the working process. This study provides a reference that can be used in the planning and boundary enhancement of agricultural machinery and equipment. Full article

Sorghum bicolor (L.) Moench, one of the most important dryland cereal crops, is moderately tolerant of soil salinity, a rapidly increasing agricultural problem due to inappropriate irrigation management and salt water intrusion into crop lands as a result of climate change. The mechanisms for sorghum’s tolerance of high soil salinity have not been elucidated. This study tested whether sorghum plants adapt to salinity stress via stomatal regulation or osmotic adjustment. Sorghum plants were treated with one of seven concentrations of NaCl (0, 20, 40, 60, 80, or 100 mM). Leaf gas exchange (net CO₂ assimilation (A), transpiration (Tr); stomatal conductance of water vapor (gs), intrinsic water use efficiency (WUE)), and water (Ψw), osmotic (Ψo), and turgor Ψt potentials were evaluated at 40 days after the imposition of salinity treatments. Plants exhibited decreased A, gs, and Tr with increasing salinity, whereas WUE was not affected by NaCl treatment. Additionally, plants exhibited osmotic adjustment to increasing salinity. Thus, sorghum appears to adapt to high soil salinity via both osmotic adjustment and stomatal regulation. Full article

Renewable energy generation has attracted growing interest globally. The agro-photovoltaic (APV) system is a new alternative to conventional photovoltaic power plants, which can simultaneously generate renewable energy and increase agricultural productivity by the use of solar panels on the same farmland. The optimization of crop yields and assessment of their environmental sensitivity under the solar panels have not yet been evaluated with various crop species. This study aimed to evaluate the agronomic performances and crop yields under the APV system and the open field with crop species such as rice, onion, garlic, rye, soybean, adzuki bean, monocropping corn, and mixed planting of corn with soybean in South Korea. The results indicated that there was statistically no negative impact of the APV system on the forage yield of rye and corn over two years, suggesting that forage crops under the APV system were suitable to producing forage yield for livestock. In addition, the measured forage quality of rye was not significantly different between the open field and the APV system. However, rice yield was statistically reduced under the APV system. The yield of legume crops and vegetables in this study did not show consistent statistical results in two years. For further study, crop yield trials will still be required for rice, soybean, adzuki bean, onion, and garlic for multiple years under the APV system. Full article

The working environment of a picking robot is complex, and the motion-planning algorithm of the picking manipulator will directly affect the obstacle avoidance effect and picking efficiency of the manipulator. In this study, a time-optimal rapidly-exploring random tree (TO-RRT) algorithm is proposed. First, this algorithm controls the target offset probability of the random tree through the potential field and introduces a node-first search strategy to make the random tree quickly escape from the repulsive potential field. Second, an attractive step size and a “step-size dichotomy” are proposed to improve the directional search ability of the random tree outside the repulsive potential field and solve the problem of an excessively large step size in extreme cases. Finally, a regression superposition algorithm is used to enhance the ability of the random tree to explore unknown space in the repulsive potential field. In this paper, independent experiments were carried out in MATLAB, MoveIt!, and real environments. The path-planning speed was increased by $99.73\%$, the path length was decreased by $17.88\%$, and the number of collision detections was reduced by $99.08\%$. The TO-RRT algorithm can be used to provide key technical support for the subsequent design of picking robots. Full article

To improve the speed regulating characteristics of continuously variable transmission for agricultural machinery, in order to meet the engineering and technical requirements of precision agriculture and intelligent agriculture, the paper researches and proposes a method combining the analysis of speed regulating characteristics, regression-based correction, and the improved particle swarm optimization (I-PSO) algorithm. First, the paper analyzes the degree of deviation between the linearization degree and the theoretical value of the speed regulating characteristics of the variable-pump constant-motor system of agricultural machinery according to the measurement results of the bench test. Next, the paper corrects the speed regulating characteristics and compares the regression results based on four models. Finally, the paper proposes a design method for the expected speed regulating characteristics of agricultural machinery and it completes the optimization of speed regulating characteristics and the matching of transmission parameters with the I-PSO algorithm. Results indicate that the speed regulating characteristics of the variable-pump constant-motor system show high linearization (with a coefficient of determination of 0.9775). The theoretical and measured values of the speed regulating characteristics have a certain deviation (with a coefficient of determination of 0.8934). Therefore, correcting the speed regulating characteristics of the variable-pimp constant-motor system is highly necessary. In addition, the second reciprocal function model proposed has the highest correction precision (with a coefficient of determination of 0.9978). The I-PSO algorithm is applicable to the design and application of hydro-mechanical continuously variable transmission (HMCVT) for agricultural machinery. The new method proposed can improve the HMCVT’s speed regulating characteristics efficiently and quickly. It also ensures that the speed regulating characteristics are highly consistent with the expected design characteristics (with a mean error of 1.73%). Thus, the research offers a theoretical direction and design basis for the research and development of continuously variable transmission units in agricultural machinery. Full article

To solve the problems of poor working conditions and high labor intensity for artificially pruning jujube trees, a pruning scheme using a manipulator is put forward in the present paper. A pruning manipulator with five degrees of freedom for jujube trees is designed. The key components of the manipulator are designed and the dimension parameters of each joint component are determined. The homogeneous transformation of the DH parameter method is used to solve the kinematic equation of the jujube pruning manipulator, and the kinematic theoretical model of the manipulator is established. Finally, the relative position and attitude relationship among the coordinate systems is obtained. A three-dimensional mathematical simulation model of the jujube pruning manipulator is established, based on MATLAB Robotics Toolbox. The Monte Carlo method is used to carry out the manipulator workspace simulation, and the results of the simulation analysis show that the working space of the manipulator is −600~800 mm, −800~800 mm, and −200~1800 mm in the X, Y, and Z direction, respectively. It can be concluded that the geometric size of the jujube pruning manipulator meets the needs of jujube pruning in a dwarf and densely planted jujube garden. Then, based on the high-speed camera technology, the performance test of the manipulator is carried out. The results show that the positioning error of the manipulator at different pruning points of jujube trees is less than 10 mm, and the pruning success rate of a single jujube tree is higher than 85.16%. This study provides a theoretical basis and technical support for the intelligent pruning of jujube trees in an orchard. Full article

Physiological maturity of bananas is of vital importance in determination of their quality and marketability. This study assessed, with the use of a Vis/NIR hyperspectral imaging (400–1000 nm), the feasibility in differentiating six maturity levels (maturity level 2, 4, and 6 to 9) of green dwarf banana and characterizing their quality changes during maturation. Spectra were extracted from three zones (pedicel, middle and apex zone) of each banana finger, respectively. Based on spectra of each zone, maturity identification models with high accuracy (all over 91.53% in validation set) were established by partial least squares discrimination analysis (PLSDA) method with raw spectra. A further generic PLSDA model with an accuracy of 94.35% for validation was created by the three zones’ spectra pooled to omit the effect of spectra acquisition position. Additionally, a spectral interval was selected to simplify the generic PLSDA model, and an interval PLSDA model was built with an accuracy of 85.31% in the validation set. For characterizing some main quality parameters (soluble solid content, SSC; total acid content, TA; chlorophyll content and total chromatism, ΔE*) of banana, full-spectra partial least squares (PLS) models and interval PLS models were, respectively, developed to correlate those parameters with spectral data. In full-spectra PLS models, high coefficients of determination (R²) were 0.74 for SSC, 0.68 for TA, and fair of 0.42 as well as 0.44 for chlorophyll and ΔE*. The performance of interval PLS models was slightly inferior to that of the full-spectra PLS models. Results suggested that models for SSC and TA had an acceptable predictive ability (R² = 0.64 and 0.59); and models for chlorophyll and ΔE* (R² = 0.34 and 0.30) could just be used for sample screening. Visualization maps of those quality parameters were also created by applying the interval PLS models on each pixel of the hyperspectral image, the distribution of quality parameters in which were basically consistent with the actual measurement. This study proved that the hyperspectral imaging is a useful tool to assess the maturity level and quality of dwarf bananas. Full article

In order to solve the problems of blocking the drainage ditch and reducing the soil flatness caused by soil accumulation when using compound planter with plowshare to ditch, a spiral soil separation mechanism (SSSM) is designed. The SSSM is analyzed. In order to obtain the optimal parameters of the SSSM, based on the discrete element method, the multifactor test is carried out with the embedded depth, pitch, and rotation speed of the spiral blade as the test factors and the soil separation distance and uniformity as the evaluation index. The optimal parameters are the embedded depth 49 mm, pitch 331 mm, and rotation speed of the spiral blade 318 r min⁻¹. The field experiment is carried out with these parameters, with soil separation distance 900 mm and standard deviation of soil height 7.8 mm, which is consistent with the simulation results. No blockage of drainage ditch was found, which shows that this device can effectively solve the problem. This study can provide a reference for the design of soil separation equipment using spiral soil separation device. Full article

Rising temperatures in most viticultural regions are associated with a higher incidence of drastic weather circumstances such as heatwaves. The consequences are reflected in qualitative and quantitative white grapes characteristics. In fact, there is an enhancement in alcohol content and a jeopardized reduction in the aromatic potential. We performed a scientific test to assuage the bump of heatwaves and exposure of grapes on Vitis vinifera cv. “Sauvignon Blanc” with exposed vines (untreated) or with kaolin foliar treatment or with partial fruit-zone shading (shading net 30 and 70%). This work aimed to evaluate the effects of shading net (SD-30% and SD-70%) and foliar kaolin (K) treatment on physiology, technological maturity, and thiolic precursors in Italy during the 2020–2021 seasons. For this purpose, four treatments were established: SD-30% (green artificial shading net at 30%), SD-70% (green artificial shading net at 70%), K (foliar kaolin), and CTRL (no application). During the two vintages, single-leaf gas exchange appraisal, leaf temperature, berry temperature, chlorophyll fluorescence, pre-dawn, and leaf water potential were measured. Moreover, berry weight, pH, °Brix, acidity (technological maturity specifications), and the following thiolic precursors were analyzed: 3-S-glutathionylhexan-1-ol (Glut-3MH), S-4-(4-methylpentan-2-one)-L-cysteine (Cys-4MMP), and 3-S-cysteinylhexan-1-ol (Cys-3MH). SD-70% and K denoted less negative water potential, a lower berry temperature, and a higher level of all precursors than the other treatments. Acidity and sugar parameters indicated significant differences among treatments. The lower berry weight and the lower tartaric acidity were found in the CTRL treatment. In comparison, SD-70% and K showed lower and more balanced sugar contents. As a result of global warming, color shading net and kaolin have been demonstrated to be good practices to counterpoise the divergence between aromatic and technological maturity in Sauvignon Blanc grapevines. Full article

With the development of advanced information and intelligence technologies, precision agriculture has become an effective solution to monitor and prevent crop pests and diseases. However, pest and disease recognition in precision agriculture applications is essentially the fine-grained image classification task, which aims to learn effective discriminative features that can identify the subtle differences among similar visual samples. It is still challenging to solve for existing standard models troubled by oversized parameters and low accuracy performance. Therefore, in this paper, we propose a feature-enhanced attention neural network (Fe-Net) to handle the fine-grained image recognition of crop pests and diseases in innovative agronomy practices. This model is established based on an improved CSP-stage backbone network, which offers massive channel-shuffled features in various dimensions and sizes. Then, a spatial feature-enhanced attention module is added to exploit the spatial interrelationship between different semantic regions. Finally, the proposed Fe-Net employs a higher-order pooling module to mine more highly representative features by computing the square root of the covariance matrix of elements. The whole architecture is efficiently trained in an end-to-end way without additional manipulation. With comparative experiments on the CropDP-181 Dataset, the proposed Fe-Net achieves Top-1 Accuracy up to 85.29% with an average recognition time of only 71 ms, outperforming other existing methods. More experimental evidence demonstrates that our approach obtains a balance between the model’s performance and parameters, which is suitable for its practical deployment in precision agriculture art applications. Full article

Due to the projected increased frequency of occurrence of extreme flood events, it is becoming increasingly important to pay attention to agricultural flood management. The middle and lower reaches of the Yangtze River Basin (MLYRB), as one of the most important agricultural areas in the world, frequently suffer from the ravages of long-duration extreme flood events. Comprehensive flood risk evaluation can provide important support for effective management strategies by focusing on the combination of flood hazard and the consequences of flooding in areas exposed to the inundation. Previous satellite-based flood disturbance detection methods intended for use in single-cropping agricultural systems cannot be applied to the MLYRB with multi-cropping practices and long-duration flood events. Additionally, comprehensive agricultural flood risk evaluations traditionally neglect the characteristics of the impact of flooding with strong spatial and temporal variability. Thus, in this research, an integrated disturbance index (IDI) was developed to detect the impact of flood disturbance on crop growth, aiming to acquire a map of crop damage condition for a multi-cropping agricultural system with long-duration flood events that is spatially explicit and has a sufficiently high spatial resolution. A coupled hydrological and 2D hydraulic model parallelized using the GPU approach was employed to simulate flood flows, aiming at deriving sufficient meaningful detail at the local scale in terms of flood inundation patterns and processes over the whole natural watershed. Additionally, a spatial map of the combined effects of flood hazard and the consequences of flooding was used to investigate the relationship between flood characteristics and associated loss extent with the random forest model. The comprehensive evaluation framework was applied for the 2010 flood event in the MLYRB. The evaluation results indicate that the detection results based on IDI are consistent with the governmental statistics, the most hard-hit areas in related reports, and the spatial characteristics of river floods. The coupled hydrological–hydraulic model offers a clear picture of the flood characteristics over the whole basin, while simultaneously ensuring a sufficiently high spatial resolution. Our findings show that flood duration is the most important predictor in predicting crop damage extent. Full article

The behavior of cage-reared ducks is an important index to judge the health status of laying ducks. For the automatic recognition task of cage-reared duck behavior based on machine vision, by comparing the detection performance of YoloV4 (you only look once), YoloV5, and Faster-RCNN, this work selected the YoloV5 target detection network with the best performance to identify the three behaviors related to avoidance after a cage-reared duck emergency. The recognition average precision was 98.2% (neck extension), 98.5% (trample), and 98.6% (spreading wings), respectively, and the detection speed was 20.7 FPS. Based on this model, in this work, 10 duck cages were randomly selected, and each duck cage recorded video for 3 min when there were breeders walking in the duck house and no one was walking for more than 20 min. By identifying the generation time and frequency of neck extension out of the cage, trample, and wing spread, it was concluded that the neck extension, trampling, and wing spread behaviors of laying ducks increase significantly when they feel panic and fear. The research provides an efficient, intelligent monitoring method for the behavior analysis of cage-rearing of ducks and provides a basis for the health status judgment and behavior analysis of unmonitored laying ducks in the future. Full article

The superfamily cytochrome P450s is involved in the evolution of insecticide resistance. However, whether CYP4G68, a differentially expressed gene identified from our transcriptomics analysis, confers resistance to the world’s heavily used insecticide class neonicotinoids is unknown. Hence, we explored the role of CYP4G68 in conferring imidacloprid and thiamethoxam resistance in Bemisia tabaci. The species B. tabaci MED developed low-to-high resistance to imidacloprid and thiamethoxam. Exposure to imidacloprid and thiamethoxam significantly increased the expression of CYP4G68. Moreover, quantitative real-time PCR analysis demonstrated that CYP4G68 was remarkably overexpressed in imidacloprid-resistant and thiamethoxam-resistant strains compared to susceptible strains. Further correlation analysis showed that CYP4G68 expression was significantly positively correlated with the associated resistance level in various strains of B. tabaci. These results suggest that the enhanced expression of CYP4G68 appears to mediate imidacloprid and thiamethoxam resistance in B. tabaci. Additionally, silencing CYP4G68 via RNA interference strongly increased the susceptibility of B. tabaci MED to imidacloprid and thiamethoxam. Collectively, this work revealed that CYP4G68 plays a vital role in imidacloprid and thiamethoxam resistance in B. tabaci MED. These findings will not only advance our understanding of the role of P450s in insecticide resistance but also provide a great potential target for the sustainable control of destructive insect pests such as whiteflies. Full article

The intensive use of mineral (M) fertilizers may cause harm the environment via leaching or greenhouse gas emissions, destroy soil fertility as a consequence of loss of soil organic matter, and, due to their high price, they are economically unviable for producers. It is widely accepted that organic (O) fertilizers may deal with pressing challenges facing modern agriculture, even if farmers need to improve their knowledge for applying in fertilization programs. A meta-analysis approach has been adopted to evaluate the effects on soil organic carbon (SOC) and crop yield of O fertilizers, applied alone or in combination with mineral fertilizers (MO) under conventional (CT), reduced (RT), and no-tillage (NT) regimes. The analysis was performed in different climatic conditions, soil properties, crop species, and irrigation management. Organic fertilizers have a positive influence in increasing SOC compared with M (on average 12.9%), even if high values were observed under NT (20.6%). The results highlighted the need for flexible and environment-specific systems when considering organic fertilization subjected to different tillage regimes. Similarly, MO application showed a better crop yield response in CT and RT under coarse soils when compared with M fertilizer applied alone (on average 13.4 and 12.7%, respectively), while in medium-textured soils, CT and RT yielded better than NT under O fertilizers (9.5 and 11.2 vs. 2.5%, respectively). Among the crop species, legumes performed better when O fertilizers were adopted than M fertilizers (on average 15.2%), while among the other crop species, few differences were detected among the fertilization programs. Under irrigated systems, RT and NT led to higher productivity than CT, especially under MO treatments (on average 9.2 vs. 3.4%, respectively). The results highlighted the importance of the environmental and agronomical factors and how their understanding could affect the impact of these conservation farming practices on crop productivity to improve the sustainability of the farming system in a specific region. Full article

Temperature is a critical environmental factor regulating plant growth and yield. Corn is a major agronomic crop produced globally over a vast geographic region, and highly variable climatic conditions occur spatially and temporally throughout these regions. Current literature lacks a comprehensive study comparing the effects of temperature on above versus below-ground growth and development and biomass partitioning of corn measured over time. An experiment was conducted to quantify the impact of temperature on corn’s early vegetative growth and development. Cardinal temperatures (T_min, T_opt, and T_max) were estimated for different aspects of above- and below-ground growth processes. Plants were subjected to five differing day/night temperature treatments of 20/12, 25/17, 30/22, 35/27, and 40/32 °C using sun-lit controlled environment growth chambers for four weeks post-emergence. Corn plant height, leaves, leaf area, root length, surface area, volume, numbers of tips and forks, and plant component part dry weights were measured weekly. Cardinal temperatures were estimated, and the relationships between parameters and temperature within these cardinal limits were estimated using a modified beta function model. Cardinal temperature limits for whole plant dry weight production were 13.5 °C (T_min), 30.5 °C (T_opt), and 38 °C (T_max). Biomass resources were prioritized to the root system at low temperatures and leaves at high temperatures. Root growth displayed the lowest optimum temperature compared to root development, shoot growth, and shoot development. The estimated cardinal temperatures and functional algorithms produced in this study, which include both above and below-ground aspects of plant growth, could be helpful to update crop models and could be beneficial to estimate corn growth under varying temperature conditions. These results could also be applicable when considering management decisions for maximizing field production and implementing emerging precision agriculture technology. Full article

The farmland residual film pollution caused by traditional PE film has an adverse impact on crops and the environment. Polypropylene carbonate (PPC) film is a fully biodegradable film that can alleviate “white pollution”. In this study, the soil physicochemical properties and the composition and function of the soil community of FM (PPC film mulching) and CK (no film) treatments were determined to explore the effect of PPC film mulching on soil and the soil microbial community. Furthermore, the microorganisms at different time periods during the degradation of PPC mulch film were also analyzed. The results showed that film mulching increased soil pH but decreased the contents of EC and SOC, and there was no significant difference in the contents of AP and AK. The relative abundance of the phyla Acidobacteria was increased with film mulching, and the relative abundance of the genera MB_A2_108 also increased in the film mulched soil. Among the soil physicochemical properties, pH and SOC were the most important factors leading to changes in the composition of the bacterial and fungal communities. PPC film mulching had no significant effect on soil microbial community abundance and diversity. In addition, Pseudomonas, Flavobacterium, and Rhizobacter were dominant in the degradation of PPC film. Our research results provide a scientific theoretical basis for soil safety and the large-scale use of PPC biodegradable mulching films and a research foundation for the degradation of PPC plastics. Full article

Mycotoxin incidence in forage may heavily affect the amount of toxins consumed by cattle. However, many studies have focused on mycotoxin occurrence in cereals and there are less studies of forages, particularly of grass silages. For determining the occurrence of mycotoxins in farm silages under Northern European conditions in Finland, 37 grass silage and 6 whole-crop cereal silage batches were analysed separately for surface, core and, if present, visibly mouldy spots. Mycotoxins were found in 92% of the samples. All mouldy samples contained mycotoxins. Beauvericin was the most common mycotoxin in grass silages and roquefortine C in whole-crop cereal silages. In mouldy samples, beauvericin, mycophenolic acid and roquefortine C were the most common mycotoxins in the grass silage and mycophenolic acid in the whole-crop cereal silage. Aflatoxins were not found in any of the samples. On average, all samples contained more than one type of mycotoxin. Concentrations of mycotoxins varied considerably from very low to very high values. The results of this survey indicate that silage-fed ruminants can be exposed to a broad range of mycotoxins. The absence of visible moulds does not always indicate mycotoxin-free feed. All moulded samples contained mycotoxins and some at very high concentrations, and they contained more different types of mycotoxins than samples without visible mould. Thus, feeding mouldy feeds to animals should be avoided. Full article

Contemporary agriculture is increasingly oriented toward the synergistic adoption of technologies such as the Internet of Things, Internet of Farming, big data analytics, and blockchain to combine resource protection and economic, social, and environmental sustainability. In Italy, the market growth potential of “Agriculture 4.0” and “Farming 4.0” solutions is very high, but the adoption of the related technological innovations is still low. Italian companies are increasingly aware of the opportunities offered by the 4.0 paradigm, but there are still cultural and technological limits to the full development of the phenomenon. This research aims to contribute to knowledge that will improve the propensity of agricultural operators to adopt the digital solutions of “Agriculture 4.0” by demonstrating its potential, along with its limits. To this end, an integrated methodological approach was adopted, built with focus groups and multicriteria analysis, to define and assess the possible future scenarios resulting from the implementation of digital transformation. The results show an increased focus on solutions that allow the integration of new tools to support those already used in the business organization and at a sustainable cost. To enable the development of “Agriculture 4.0”, we propose that it is necessary to invest in training operators in the supply chain, and above all, raising awareness among farmers, who it is essential fully appreciate the potential benefits of the 4.0 revolution. Full article

Leaf spot disease caused by Lasiodiplodia theobromae is one of the most serious fungal diseases of kiwifruit production. In this work, the co-application of isopyrazam·azoxystrobin and chitosan against leaf spot disease in kiwifruit and its effects on disease resistance, photosynthesis, yield, quality, and amino acids of kiwifruit were investigated. The results show that isopyrazam·azoxystrobin exhibited a superior bioactivity against L. theobromae with an EC₅₀ value of 0.1826 mg kg⁻¹. The foliar application of chitosan could effectively enhance isopyrazam·azoxystrobin against leaf spot disease with a field control efficacy of 86.83% by spraying 29% isopyrazam·azoxystrobin suspension concentrate (SC) 1500 time + chitosan 100-time liquid, which was significantly (p < 0.05) higher than 78.70% of 29% isopyrazam·azoxystrobin SC 1000-time liquid. The co-application of isopyrazam·azoxystrobin and chitosan effectively enhanced soluble protein, resistance enzymes’ activity in kiwifruit leaves, and reduced their malonaldehyde (MDA), as well as reliably improved their photosynthetic characteristics. Simultaneously, their co-application was more effective in promoting growth, quality, and amino acids of kiwifruit fruits compared to isopyrazam·azoxystrobin or chitosan alone. This study highlights that the co-application of isopyrazam·azoxystrobin and chitosan can be used as a green, safe, and efficient approach for controlling leaf spot disease of kiwifruit and reducing the application of chemical fungicides. Full article

The pattern of international agricultural trade is undergoing profound changes. The influence of country risks on the international agricultural trade pattern is prominent. In this paper, we comprehensively analyze the international agricultural trade patterns and explore the influence of country risks on them. Specifically, we first construct an international agricultural trade network (IATN) based on complex network theory. Second, we analyze each country’s diversity of import sources and the position of countries in the IATN using the Herfindahl–Hirschman Index (HHI) and network indicators, such as in-degree, out-degree, weighted in-degree, weighted out-degree, and betweenness centrality. Third, this paper explores the influence of different types of country risks, including economic risk and political risk, on international agricultural trade patterns using the panel regression method. The results show that countries played different roles and occupied different positions in the international agricultural trade pattern; notably, the United States occupied a core position, while Japan and Mexico had insufficient diversity in import sources. Moreover, based on the panel regression method, we find that political risks have a positive impact on the agricultural trade pattern, while an unstable economic environment could inhibit the agricultural trade pattern in various countries. This study could provide references for countries to implement agricultural trade policies regarding country risks to ensure stable agricultural trade relations and national food security. Full article

To build a more accurate motor efficiency model with a strong generalization ability in order to evaluate and improve the efficiency characteristics of electric vehicles, this paper researches motor efficiency modeling based on the bench tests of two motor efficiencies with differently rated powers. This paper compares and analyzes three motor efficiency modeling methods and finds that, when the measured values in motor efficiency tests are insufficient, the bilinear interpolation method and radial basis kernel function neural networks have poor generalization abilities in full working conditions, and the precision of polynomial regression is limited. On this basis, this paper proposes a new modeling method combining correlation analysis, polynomial regression, and an improved simulated annealing (I-SA) algorithm. Using the mean and the standard deviation of the mean absolute percentage error of the 5-fold Cross Validation (CV) of 100 random tests as the evaluation indices of the precision of the motor efficiency model, and based on the motor efficiency models with verified precision, this paper makes a comparative analysis on the full vehicle efficiency of electric tractors of three types of drive in five working conditions. Research results show that the proposed novel method has a high modeling precision of motor efficiency; tractors with a dual motor coupling drive system have optimal economic performance. Full article

This work aimed to determine and compare the effect of elevation and season on the productivity and the nutritive value of pastures in the Azores (Terceira Island). Forage was collected and analysed for dry matter (DM), crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF), acid detergent lignin (ADL), ether extract (EE), mineral ash (Ash), dry matter digestibility (DMD) and organic matter digestibility (OMD). The net productivity (NP) was higher in the low elevation pasture A (1.80 g m⁻²), lower in pasture B (0.98 g m⁻²) and peaked in the winter in both pastures A (3.57 g m⁻²) and B (2.33 g m⁻²) and during the summer in the high elevation pasture C (2.15 g m⁻²). The soil chemical proprieties varied significantly among the three pastures. The highest soil pH, available P, K, Ca and Mg were recorded in pasture A. Positive correlations were observed between all soil parameters analysed and NP, except for the OM content. The DM, PB and EE changed significantly with elevation, while all nutritive parameters (except CP, EE and Ash) increased significantly along the growth season. Environmental factors influenced the nutritive parameters and productivity, suggesting that climate change might have significant impacts on forage production and quality. Full article

Soil moisture is the major factor influencing microbial properties and nitrous oxide (N₂O) production. Agricultural soils can be probed under wetting, wet/dry alternating, and constant moisture conditions to evaluate the combined effects of early (previous) and immediate (current) moisture on N₂O emission and nitrification/denitrification. In view of the water history of upland black soil, five moisture regimes comprising different antecedent and present water holding capacity (WHC) levels were set up in the microcosm study. The 20% WHC was adopted as the initial legacy moisture, while three immediate water statuses include constant WHC, dry-wet cycle, and incremental moisture. Quantitative PCR and 16S rRNA amplicon sequencing were used to assess the impact of current and previous moisture on the bacterial community composition and abundance of nitrification/denitrification genes (amoA, nirS, and nosZ); the soil physicochemical properties, and N₂O emission were monitored. The N₂O production and nitrifying-denitrifying microbial communities were influenced by the antecedent moisture and pattern of the dry-wet cycle. The nitrifying-denitrifying microbial communities, especially members of β-/γ-Proteobacteria, Bacteroidetes and Gemmatimonadetes, in black soil were important in explaining the variation of N₂O production. The key taxonomic groups in response to the moisture alteration, e.g., Acidobacteria, Sphingobacteriia, Deltaproteobacteria, Methylobacterium, Gemmatimonas and Pseudarthrobacter, etc., were also highlighted. The soil nitrate, ammonium nitrogen, N₂O emission, nitrification/denitrification and mineralization were profoundly impacted by water regimes and showed statistically significant correlation with specific bacterial genera; the nitrite/nitrate reduction to ammonium could be boosted by high moisture. Both nitrifier denitrification and heterotrophic denitrification could be enhanced substantially when the black soil moisture was increased to above 60% WHC. These findings help evaluate the effects of the water mode on the N₂O emission from black soil, as well as the associated impacts on both soil fertility and the global environment. Full article

Smart agriculture today uses a wide range of wireless communication technologies. Low Power Consumption Embedded Devices (LPCED), such as the Internet of Things (IoT) and Wireless Sensor Networks, make it possible to work over great distances at a reduced cost but with limited transferable data volumes. However, data management (DM) in intelligent agriculture is still not well understood due to the fact that there are not enough scientific publications available on this. Though data management (DM) benefits are factual and substantial, many challenges must be addressed in order to fully realize the DM’s potential. The main difficulties are data integration complexities, the lack of skilled personnel and sufficient resources, inadequate infrastructure, and insignificant data warehouse architecture. This work proposes a comprehensive architecture that includes big data technologies, IoT components, and knowledge-based systems. We proposed an AI-based architecture for smart farming. This architecture called, Smart Farming Oriented Big-Data Architecture (SFOBA), is designed to guarantee the system’s durability and the data modeling in order to transform the business needs for smart farming into analytics. Furthermore, the proposed solution is built on a pre-defined big data architecture that includes an abstraction layer of the data lake that handles data quality, following a data migration strategy in order to ensure the data’s insights. Full article

This study aimed to understand green finance’s impact on fertilizer use and agricultural carbon emissions. We selected the macro panel data of 30 provinces (cities) in China from 2000 to 2019. The main research methods are standardized test framework (cross-sectional dependence, unit root and cointegration test), the latest causal test, impulse response, and variance decomposition analysis. Examined the long-term equilibrium relationship between green finance, fertilizer use, and agricultural carbon emissions. The results show: fertilizer consumption and agricultural carbon emissions have a positive correlation. However, green finance can significantly reduce agricultural carbon emissions. The causal test confirmed the bidirectional causal relationship between agricultural carbon emissions and fertilizer use. At the same time, verified one-way causality from green finance to both of them. Interpret the results of impulse response and variance decomposition analysis: among the changes in agricultural carbon emissions, chemical fertilizers contributed 2.45%, green finance contributed 4.34%. In addition, the contribution rate of green finance to chemical fertilizer changes reached 11.37%. Green finance will make a huge contribution to reducing fertilizer use and agricultural carbon emissions within a decade. The research conclusions provide an important scientific basis for China’s provinces (cities) to formulate carbon emission reduction policies. China has initially formed a policy system and market environment to support the development of green finance, in 2020, the “dual carbon” goal was formally proposed. In 2021, the national “14th Five-Year Plan” and the 2035 Vision Goals emphasized the importance of green finance. It plays an important supporting role in carbon emission reduction goals, and green finance has become an important pillar of national strategic goals. Full article

As pesticide abuse becomes increasingly serious worldwide, it is necessary to pay attention to the biopesticide adoption behavior of agricultural producers. It is worth verifying whether agricultural cooperatives, as training organizations sharing the same social network with farmers, can promote the adoption of biopesticides through their technology diffusion function. Therefore, based on survey data of 837 citrus producers in Sichuan Province, China, the IV-probit regression model and a mediation effects model were used to empirically test the impact of technical training on farmers’ adoption of biopesticides in addition to its mechanism, considering the farmers’ perception of technology as the mediation variable. The results show that (a) participation in technical training can significantly enhance the probability of the adoption of biopesticides; (b) farmers’ perceptions of biopesticides’ economic and health benefits play a partial mediating role in the relationship; and (c) technical training has more significant effects on biopesticides adoption behavior for a household with higher-educated household heads, lower household total income, and smaller household size, relative to their counterparts. This study provides evidence for establishing relevant policy to encourage the full adoption of the technical training function of agricultural cooperatives and popularize the use of biopesticides. Full article

The conversion of grasslands to croplands is common in the agro-pastoral ecotone and brings potential risks to soil health and environmental safety. As the forming unit of soil structure, the status of soil aggregates determines soil health and is affected by multiple factors. This study investigated the changes in soil aggregate and main related factors in conversion grasslands with different managed years. Grassland conversion ages were selected as experimental treatments, which included unmanaged grassland, 3 years, 10 years, 30 years, and 50 years since grassland conversion. After grassland conversion, the proportion of large macro-aggregates with a particle size of >2 mm in the 0–10 cm soil layer decreased, small macro-aggregates with a particle size of 2–0.25 mm and micro-aggregates with a particle size of 0.25–0.053 mm increased, while aggregates with a particle size of <0.053 mm had no significant change. Soil chemical properties, most microorganisms and the soil aggregate stability indices MWD and GMD decreased at the early stage (<30 years) of the managed grasslands. After about 50 years of cultivation, soil chemical properties and microorganisms returned to equal or higher levels compared to unmanaged grasslands. However, the stability of aggregates (mean weight diameter (MWD) and geometric mean diameter (GMD)) did not recover to the initial state. MWD and GMD were positively correlated with most bacterial factors (total phospholipid fatty acids (PLFAs), bacteria, Gram-positive bacteria, Gram-negative bacteria, actinomycetes and arbuscular mycorrhizal fungi (AMF)) and some soil chemical properties (carbon, nitrogen and polysaccharides). According to the partial least square structural equation model, soil organic carbon, total nitrogen and phosphorus in the 0–10 cm soil layer explained 33.0% of the variance in MWD by influencing microorganisms. These results indicated that the stability of aggregates was directly driven by microorganisms and indirectly affected by soil organic carbon, total nitrogen and phosphorus. Full article

Plant diseases are a major concern in the agricultural sector; accordingly, it is very important to identify them automatically. In this study, we propose an improved deep learning-based multi-plant disease recognition method that combines deep features extracted by deep convolutional neural networks and k-nearest neighbors to output similar disease images via query image. Powerful, deep features were leveraged by applying fine-tuning, an existing method. We used 14,304 in-field images with six diseases occurring in apples and pears. As a result of the experiment, the proposed method had a 14.98% higher average similarity accuracy than the baseline method. Furthermore, the deep feature dimensions were reduced, and the image processing time was shorter (0.071–0.077 s) using the proposed 128-sized deep feature-based model, which processes images faster, even for large-scale datasets. These results confirm that the proposed deep learning-based multi-plant disease recognition method improves both the accuracy and speed when compared to the baseline method. Full article

Plum fruit and kernels offer bioactive material for industrial production. The promising procedure for distinguishing plum kernel cultivars used in this study comprised two stages: image analysis to compute the texture parameters of plum kernels belonging to three cultivars ‘Emper’, ‘Kalipso’, and ‘Polinka’, and discriminant analysis using machine learning algorithms to classify plum kernel cultivars based on selected textures with the highest discriminative power. The discriminative models built separately for sets of textures selected from all color channels L, a, b, R, G, B, U, V, S, X, Y, Z, color space Lab and color channel b using the KStar (Lazy), PART (Rules), and LMT (Trees) classifiers provided the highest average accuracies reaching 98% in the case of the color space Lab and the KStar classifier. In this case, individual cultivars were discriminated with the accuracies of 97% for ‘Emper’ and ‘Kalipso’ to 99% for ‘Polinka’. The values of other performance metrics were also satisfactory, higher than 0.95. The ROC curves were quite smooth and steady with the most satisfactory curve for the ‘Kalipso’ kernels. The present study sheds light on an objective, non-destructive, and inexpensive procedure for cultivar discrimination of plum kernels. Full article

Agricultural mechanization is essential to increase farmers’ income in modern agriculture. However, the use of machinery for crop production in China is quite inefficient. To understand the obstacles limiting farmers’ use of machinery, we conducted face-to-face interview surveys with 1023 farmers (including cooperative directors, machine operators, and farmers without machines) in two major cereal-producing regions with large differences in farming scale: the North China Plain (2.7 ha per capita) and the Northeast China Plain (12.8 ha per capita). The results revealed that farmers in both regions had strong will to use machines. The obstacle preventing farmers from buying machines was the lack of machinery training in the Northeast China Plain and land fragmentation in the North China Plain. Among different farmer groups, land fragmentation was the main barrier for cooperative directors. Farmers without machines thought that there was lack of machinery training and that the cost of machinery purchase was high. Machine operators believed that machine maintenance was too expensive. The income and age also had an effect on the different groups of farmer. It is concluded that, to improve mechanization efficiency and stimulate farmers’ intention to use machinery, the government should make policies to encourage the merge of fragmented farmlands, provide targeted subsidies for agricultural machinery, and organize machinery training in an efficient way. Full article

To simulate the interactions between a pneumatic cotton precision seed-metering device and coated delinted cotton seeds accurately, physical and simulation experiments based on a rotating drum apparatus were combined to calibrate the discrete element simulation parameters of E`kangmian-10 cotton seeds. Firstly, the contact parameters and the dynamic repose angle of the cotton seeds were measured through physical tests. Based on the particle size requirement of the Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD) coupling simulation and the reverse engineering technology, the cotton seed discrete element bonded-particle model (BPM) was established. Secondly, taking the contact parameters as calibration objects and the simulated dynamic repose angle as the evaluation index, a Plackett–Burman (PB) test was designed for significance screening. The results of the screening test showed that the static friction coefficient of cotton seed–tough photosensitive resin, the impact recovery coefficient of cotton seed–cotton seed, and the static friction coefficient of cotton seed–cotton seed had a highly significant effect on the simulated dynamic repose angle. Next, a Box–Behnken Design (BBD) test was adopted to establish the quadratic regression model between significant parameters and the simulated dynamic repose angle, and then the multi-factor optimization solution was carried out to obtain the optimal combination of parameters: the static friction coefficient of cotton seed–tough photosensitive resin and the impact recovery coefficient and static friction coefficient of cotton seed–cotton seed were 0.33, 0.06 and 0.10, respectively. Lastly, verification tests on the rotating drum apparatus and the seed-metering device were performed, and their relative errors were less than 2%, which indicated that the discrete element models and the contact parameters of the coated delinted cotton seeds were reliable. This study provides a reference for the selection of the discrete element parameters of coated delinted cotton seeds for DEM-CFD coupling simulation and the optimal design of precision seed-metering device for cotton. Full article

Rhizoctonia cerealis is a major fungal pathogen of wheat that causes great yield losses in all wheat-growing regions of the world. The biocontrol agent Bacillus subtilis XZ18-3 was investigated for inhibiting R. cerealis growth in wheat. The results of the mycelial growth test showed that the sterile filtrate of B. subtilis XZ18-3 could significantly inhibit the mycelial growth of R. cerealis and cause swelling and rupture of the mycelium. Observation by transmission electron microscopy indicated that the sterile filtrate could penetrate the cellular membrane of Rhizoctoniacerealis, resulting in organelle destruction. The effect of the sterile filtrates on the pathogen cells, shown through fluorescent microscopy using different stains, revealed the mechanism by which the sterile filtrate caused DNA fragmentation, accumulation of ROS and changes in cell membrane permeability. To reach a better treatment of the soil-borne fungi, the components of a wettable powder were screened and an optimised formula determined (30.0% kaolin, 4.0% polyvinyl alcohol, 8.0% Tween-80, 2.0% polyethylene glycol and 100% fermentation broth). A quality index analysis revealed that the wetting powder reached acceptable biological pesticide standards. Pot control experiments showed that the wettable powder of B. subtilis XZ18-3 effectively controlled the pathogens with an efficacy of 88.28%. This study has provided the potential biocontrol agents (BCAs) for wheat sharp eyespot disease. Full article

Fluctuations in agricultural commodity prices affect the supply and demand of agricultural commodities and have a significant impact on consumers. Accurate prediction of agricultural commodity prices would facilitate the reduction of risk caused by price fluctuations. This paper proposes a model called the dual input attention long short-term memory (DIA-LSTM) for the efficient prediction of agricultural commodity prices. DIA-LSTM is trained using various variables that affect the price of agricultural commodities, such as meteorological data, and trading volume data, and can identify the feature correlation and temporal relationships of multivariate time series input data. Further, whereas conventional models predominantly focus on the static main production area (which is selected for each agricultural commodity beforehand based on statistical data), DIA-LSTM utilizes the dynamic main production area (which is selected based on the production of agricultural commodities in each region). To evaluate DIA-LSTM, it was applied to the monthly price prediction of cabbage and radish in the South Korean market. Using meteorological information for the dynamic main production area, it achieved 2.8% to 5.5% lower mean absolute percentage error (MAPE) than that of the conventional model that uses meteorological information for the static main production area. Furthermore, it achieved 1.41% to 4.26% lower MAPE than that of benchmark models. Thus, it provides a new idea for agricultural commodity price forecasting and has the potential to stabilize the supply and demand of agricultural products. Full article

The conventional method for crop insect detection based on visual judgment of the field is time-consuming, laborious, subjective, and error prone. The early detection and accurate localization of agricultural insect pests can significantly improve the effectiveness of pest control as well as reduce the costs, which has become an urgent demand for crop production. Maize Spodoptera frugiperda is a migratory agricultural pest that has severely decreased the yield of maize, rice, and other kinds of crops worldwide. To monitor the occurrences of maize Spodoptera frugiperda in a timely manner, an end-to-end Spodoptera frugiperda detection model termed the Pest Region-CNN (Pest R-CNN) was proposed based on the Faster Region-CNN (Faster R-CNN) model. Pest R-CNN was carried out according to the feeding traces of maize leaves by Spodoptera frugiperda. The proposed model was trained and validated using high-spatial-resolution red–green–blue (RGB) ortho-images acquired by an unmanned aerial vehicle (UAV). On the basis of the severity of feeding, the degree of Spodoptera frugiperda invasion severity was classified into the four classes of juvenile, minor, moderate, and severe. The degree of severity and specific feed location of S. frugiperda infestation can be determined and depicted in the frame forms using the proposed model. A mean average precision (mAP) of 43.6% was achieved by the proposed model on the test dataset, showing the great potential of deep learning object detection in pest monitoring. Compared with the Faster R-CNN and YOLOv5 model, the detection accuracy of the proposed model increased by 12% and 19%, respectively. Further ablation studies showed the effectives of channel and spatial attention, group convolution, deformable convolution, and the multi-scale aggregation strategy in the aspect of improving the accuracy of detection. The design methods of the object detection architecture could provide reference for other research. This is the first step in applying deep-learning object detection to S. frugiperda feeding trace, enabling the application of high-spatial-resolution RGB images obtained by UAVs to S. frugiperda-infested object detection. The proposed model will be beneficial with respect to S. frugiperda pest stress monitoring to realize precision pest control. Full article

Soil shrink–swell behavior is a common phenomenon in farmland, which usually alters the process of water and solute migration in soil. In this paper, we report on a phenomenological investigation aimed at exploring the impact of drying–wetting cycles on the shrink–swell behavior of soil in farmland. Samples were prepared using clay loam collected from farmland and subjected to four drying–wetting cycles. The vertical deformation of soil was measured by a vernier caliper, and the horizontal deformation was captured by a digital camera and then calculated via an image processing technique. The results showed that the height, equivalent diameter, volume and shrinkage-swelling potential of the soil decreased with the repeated cycles. Irreversible deformation (shrinkage accumulation) was observed during cycles, suggesting that soil cracks might form owing to previous drying rather than current drying. The vertical shrinkage process consisted of two stages: a declining stage and a residual stage, while the horizontal shrinkage process had one more stage, a constant stage at the initial time of drying. The VG-Peng model fit the soil shrinkage curves very well, and all shrinkage curves had four complete shrinkage zones. Drying–wetting cycles had a substantial impact on the soil shrinkage curves, causing significant changes in the distribution of void ratio and moisture ratio in the four zones. However, the impact weakened as the number of cycles increased because the soil structure became more stable. Vertical shrinkage dominated soil deformation at the early stage of drying owing to the effect of gravity, while nearly isotropic shrinkage occurred after entering residual shrinkage. Our study revealed the irreversible deformation and deformation anisotropy of clay loam collected from farmland during drying–wetting cycles and analyzed the shrink–swell behavior during cycles from both macroscopic and microscopic points of view. The results are expected to improve the understanding of the shrink–swell behavior of clay loam and the development of soil desiccation cracks, which will be benefit research on water and solute migration in farmland. Full article