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• The paper proposes a methodology for a hybrid model that can capture complex non-linear relationships and help understand the interactions between input and output features. • The model overcomes the limitation of traditional neural network models in predicting across different spaces. • The proposed model is an intelligent water quality prediction model that can provide decision support for predicting COD Mn concentration and managing watershed risks in the Changdang Lake basin, with good application prospects and practical value. The organic pollution of lake water can cause a tremendous threat to the water ecosystem and human health. The COD Mn is one of the crucial indicators of lake water quality and is commonly utilized to gauge the extent of organic pollution in lake water. Therefore, this paper selected COD Mn as the research object and used the water quality monitoring data of Changdang Lake in China and its upstream and downstream to predict the COD Mn concentration in the lake. In order to study the spatial relationship between the lake and upstream and downstream water quality, reflect the joint action of multiple water quality factors in prediction and the interaction between different feature factors. This study combined the XGBoost feature filtering algorithm, maximum mutual information coefficient (MIC), and improved recurrent neural network (GRU) and proposes a hybrid model called XGB-MIC-GRU. The model first used XGBoost to screen and extract the relative importance of water quality characteristics and used the Shapley addition extension (SHAP) method to explain XGBoost feature extraction. Then, the correlation between the lake and the upstream and downstream water quality is calculated through MIC analysis. Finally, the selected water quality factor characteristics and spatial characteristics are input into the GRU model for prediction. The experimental results showed that water temperature, total phosphorus, and total nitrogen are the most important to COD Mn , and the upstream US1 and downstream DS1 and DS2 stations are the most closely related to the concentration of COD Mn in the lake. By comparing the prediction effect of the model in different time steps, the best 16-time steps related data were selected to predict the value of the next time. MAE, RMSE, and R2 of the model are 0.10, 0.13, and 0.96, respectively. The model has better prediction accuracy and correlation error than the traditional SVR and GPR. The proposed mixed model can accurately predict the concentration of COD Mn in the lake. It can assist decision-makers in timely implementation of effective measures to safeguard the lake ecosystem. [ABSTRACT FROM AUTHOR]

• The ISP method allows for gathering a large number of historical training samples. • The SRVI values of the same crop planted in neighboring years range from 0.91 and 1.2. • A distinct northward expansion trend is evident for all four major crops, in Heilongjiang Province, China. Mapping agricultural information such as cropping area and type is of great significance for land use and food security and a common method to retrieve such information is satellite imagery classification. The current image classification techniques require a statistically large number of training samples to obtain representative spectral signatures, ideally collected during the satellite overpass time/date. Although this prerequisite sounds simple and easy in principle, it is extremely challenging and sometimes impossible when classifying historical satellite images, as one simply cannot go back in time to collect training datasets or ground truthing. In this paper, we introduce an iterative and subsequent proximation (ISP) method to circumvent this problem for historical image classifications for historical crop mapping. This method assumes that the same crops grown two years apart have similar spectral properties within the same growing season, with limited variation. This allows the use of the crop information classified in any given year (n) to be used as training samples for the next year (n + 1) or the previous year (n-1). Iteratively repeating n-1, n-2... process leads to the mapping of historical cropping information without a priori ground truthing data. To demonstrate the ISP feasibility, we first used the historical Landsat time-series data to map four major stable crops (rice, maize, soybean, and wheat) in Hailun County, Heilongjiang Province, China, and then further expanded the ISP application to the entire Heilongjiang Province to map cropping areas from 1982 to 2020, using the classification and regression trees (CART) algorithm. The results were validated for those years when actual cropping measurement data were available and further verified with statistical data for other years. The results indicated that the proposed ISP method was appropriate for the historical mapping of four major crops, with a mapping accuracy of approximately 80 % when validated with field data, and correlation coefficients of 0.86, 0.85, 0.91, and 0.94, respectively when compared with historical statistical cropping data. The crop mapping results showed distinct trends of northward expansion in each of the four crops in Heilongjiang Province, which agreed well with previous studies. In conclusion, the ISP method is quick, easy, and convenient for historical crop mapping, which is important in understanding the agricultural production history. [ABSTRACT FROM AUTHOR]

[Display omitted] • A wheat plant can be simplified to be composed of a wheat ear and a straw for DEM simulation. • The plant modeling method is a general modeling method that only requires the input of the corresponding parameters of the variety according to the different varieties to build the plant model of that variety. • The plant models built by the modeling method proposed in this paper have flexible characteristics. • The experimental verification and simulation analysis of plant models were carried out. Simulation of the wheat harvesting process using the discrete element method is of significant importance in optimizing the components of wheat harvesting machinery. Establishing a discrete element model that can accurately reflect the biomechanical properties of wheat plants is a crucial foundation for simulating harvesting operations. To address the current issues in the discrete element model (DEM) of wheat plants, a general modeling method for wheat plants was developed in this study. Firstly, the shape and size of 10 representative varieties of wheat plants in the mature period from different regions in mainland China to establish the common correlations between the shape and size of wheat plants. On this basis, a geometrical model of wheat plants was built using the particle arrangement method, and the coordinates of the component particles were solved for. Then, the contact mechanical parameters were measured and calibrated through various tests, including the slope test, single-pendulum test, drop test and stacking angle test. Moreover, the parallel bonding mechanical parameters of wheat plants were measured and analyzed by tensile, compression and shear tests. On this basis, the mechanical model of wheat plants was established based on the Hertz-Mindlin contact model and the parallel bonding model (PBM). The geometrical model together with the mechanical model enabled the construction of a DEM-based model of wheat plants with flexible characteristics. Based on the above work, the models that were proposed in this paper for three varieties of wheat were validated by comparing the experimental data and simulation results in terms of the plant stacking test, vibration screening test and impact threshing test. The results demonstrated that the simulation results for all three varieties were similar to the experimental results, which showed the feasibility and effectiveness of the general modeling method proposed in this paper for wheat plants in the mature period. The results of this study lay a foundation for the analysis of the wheat harvest process by the discrete element method. [ABSTRACT FROM AUTHOR]

• This paper proposes a multi-dimensional assessment framework for cropland use stability. • Multi-dimensional cropland use characteristics present significant spatial differentiation. • Cropland stability in Northeast China Plain and Loess Plateau is the highest and lowest. • Urbanization will lead to about 9.31% of high-stability cropland losses in 2030. • Integrating landscape stability and national planning can draft a sustainable-oriented strategy. Shifting to more sustainable use can curb cropland resource degradation and improve production resilience. However, most cropland use assessments are unilaterally focused on biophysical levels and productivity, ignoring the multi-dimensional aspects of degraded cropland. This study attempts to describe cropland use stability in China by proposing a multi-dimensional assessment framework (including quantity, quality, management, service, and risk) to provide a reference for designing land degradation-neutral strategies. Results show that multi-dimensional cropland use characteristics present significant spatial heterogeneity. Western regions are reserve resource agglomerations that can replenish cropland quantity. In contrast, main grain-producing areas cover most current resources with preferably cultivation quality, management efficiency, and ecosystem services, while risk resistance is weak (especially in Middle-lower Yangtze Plain). Generally, the stability of cropland use in Northeast Plain is the highest and in Loess Plateau the lowest. Besides, urbanization will occupy about 9.31% of the high-stability cropland in 2030, of which 70.29% will occur in Northeast Plain and Huang-Huai-Hai Plain. The total potential productivity loss in Huang-Huai-Hai Plain alone will reach 100.1 kt, equivalent to supplying the food demand of 199,200 people annually. Therefore, a sustainable-oriented refined cropland management strategy should be designed according to the hierarchical nested spatial layout of comprehensive planning-stability map-multi-dimensional utilization characteristics to ensure the loss-replenishment balance and stable production. [ABSTRACT FROM AUTHOR]

• Sensor technology developments drove advances in biophysical estimation techniques. • CubeSat constellations will increase VHR data accessibility to various users. • Sentinel-2 red-edge bands present prospects for operational crop stress detection. • RF is prominently used in the literature despite new developments in algorithms. • Retrieval models developed in Mediterranean areas do not apply to semi-arid areas. This paper provides an overview of the recent developments in remote sensing technology and machine learning algorithms for estimating important biophysical and biochemical parameters for precision farming. The objectives are (i) to provide an overview of recent advances in remotely sensed retrieval of biophysical and biochemical parameters brought by the developments in sensor technologies and robust machine learning algorithms and (ii) to identify the sources of uncertainty in retrieving biophysical and biochemical parameters and implications for precision agriculture. The review revealed that developments in crop biophysical and biochemical parameters retrieval techniques were mainly driven by announcements and the availability of new sensors. Two ground-breaking events can be identified, i.e., the availability of Sentinel-2 and the SuperDove constellation. The two provide high temporal-high spatial resolution data relevant for site-specific management and super-spectral configuration, enabling retrieval of crop growth and health parameters. The free availability of Sentinel-2 triggered the testing of its spectral configurations and upscaling of retrieval approaches using simulated data from field spectrometers and airborne hyperspectral sensors. SuperDoves will likely reduce the cost of very high-resolution data while providing unprecedented capabilities for detailed, accurate and frequent characterisation of field variability. Studies showed that the red-edge bands and hybrid models coupling Radiative Transfer Model (RTM) and machine learning regression algorithms (MLRA) are promising for operational and accurate monitoring of stress-related crop parameters to aid time-sensitive agronomic decisions. However, such models were tested in Mediterranean climates and performed poorly in African semi-arid areas and China's temperate continental semi-humid monsoon climates. Therefore, locally-calibrated RTM models incorporating crop-type maps and other spatio-temporal constraints may reduce uncertainties when adapted to data-scarce regions. Generally, permanent experimental sites and a lack of systematic calibration data on various crops are some limiting factors to using remote sensing technologies for PA in Sub-Saharan Africa. Other complexities arise from farm configurations, such as small field sizes and mixed cropping practices. Therefore, future studies should develop generic, scalable and transferable models, especially within under-studied areas. [ABSTRACT FROM AUTHOR]

Due to the multifaceted influences of many factors and complex mechanisms, accurate predictions of ocean chemistry data are difficult to achieve. To address this problem, this paper proposes an adaptive hidden Markov model (AHMM) for predicting ocean chemistry data from aquaculture farms in eastern China. The periodic, trend and residual terms of the original sequence are obtained through Seasonal and Trend decomposition using Loess (STL), which effectively reveals the variation trend of the sequence. For the period and trend terms of the sequence, the autonomous search grasshopper optimization algorithm (ASGOA) is employed to train the parameters of the HMM, which avoids the problems of easily falling into local optima and the dependence on the initial value in the parameter training process. To predict ocean chemistry data, a sequence similarity measurement method is proposed to match similar sequences in the historical interval, and a linear regression equation is constructed for sequence similarity and the difference between the predicted value and the value at the previous time based on the HMM. The results have revealed that the MAE value decreases by 25.5%, MSE value by 45.9%, RMSE value by 27.4%, MAPE value by 39.5% and R 2 increases by 6%. Compared with other prediction algorithms under the same conditions, the prediction method proposed in this paper is easily understandable and manages to achieve better prediction performance. This work can serve as a reference for predicting other ocean chemistry data and for the sustainable management of aquaculture ecosystems. • A water quality prediction method based on adaptive hidden Markov model is proposed. • An automatic search grasshopper optimization algorithm (ASGOA) is proposed. • A similarity measurement method for ocean chemistry data prediction is proposed. • AHMM has better prediction performance. [ABSTRACT FROM AUTHOR]

Cucumber production in China is declining due to various pathologic diseases, but the technology for plant disease detection is not mature and requires high labor costs. Moreover, since planting sites are typically high-density scenes, most photos are shot from various angles with messy backgrounds, resulting in poor classification reliability. In this paper, batches of cucumber leaf image data are collected from agricultural websites and then preprocessed through the image size normalization. A mobile-based recognition algorithm is proposed to identify cucumber diseases from leaf images in natural scenes, enabling farmers to detect diseases more quickly. The proposed algorithm allows farmers to upload cucumber pictures, and rapidly and accurately classify them with high accuracy. With a improved network based on MobileNet V3, the classification of seven kinds of cucumber leaf diseases can be quickly and accurately completed. The network model is achieved by selecting appropriate parameters, optimizers, and batch capacity using the single-variable method. Additionally, a new training strategy called the flooding method is applied in the model, replacing the traditional strategy that relies solely on loss decline. An accuracy of 83.3% is achieved on our custom dataset. Finally, two public datasets, namely PlantVillage and Apple Disease, are selected for migration experiments. The achieved accuracy rates for these datasets are 99% and 98.1% respectively, demonstrating the universality of the proposed strategy. The code for all the experiments will be made available for reference on the GitHub repository at https://github.com/YiQuanMarx/Agricultural_Diseases_Dentification. • An algorithm to identify seven cucumber diseases in natural scenes is proposed. • Lightweight and fast MobileNetv3 network is achieved by various optimizations. • Replacing the strategy of loss decline, flooding method is applied for high accuracy. • Eight state-of-the-art algorithms are selected for performance comparison. • Performance criteria cover accuracy, loss, parameters, computation time, storage etc. [ABSTRACT FROM AUTHOR]

• Machine learning was applied to quantify the effects of stand, climate and soil on biomass of mixed forests with good accuracy. • Quantifying the importance of various environmental attribute factor to forest biomass depends on the combination of environmental variables and modeling methods. • Environmental variables including degree-days above 18 °C, bulk density and soil pH were most important predictors. Forest biomass is influenced by multiple environmental factors. Multi-source data on site-specific soil, climate, and stand factors are essential for stand biomass prediction. While previous studies concerning planted forest biomass at stand level highlighted the importance of environmental factors, few have been conducted on the effects of climate and soil on stand biomass estimation, especially for natural mixed forest with complicated stand structure. Machine learning (ML) algorithms provide new tools for examining the effects, but the applications are still limited. Therefore, the objectives of this paper were to develop ML models for stand biomass estimation with stand and multiple environmental predictors, and quantify the relative importance of environmental variables. Tree above- and below-ground biomass at stand level was set as output variable with the random forest (RF) and boosted regression tree (BRT) regression methods. The data were from 286 sample plots from the National Forest Inventory (NFI) in Jilin Province, northeast China. The results showed that models with the inclusion of stand, soil, and climate variables explained > 89% of forest biomass variations. BA (stand basal area) and H (stand average height) were the most important stand variables, DD18 (degree-days above 18 °C) was the most important climate variable, and bdod (bulk density) and pH were the most important soil variables. Relative importance of climate and soil factors was 0.5% to 5.1% and 1.2% to 4.1%, respectively, which were weak but important predictors of stand biomass. In the RF model, climate variables were more important than soil variables, but the opposite in the BRT models. We concluded that above forest stand, climate and soil factors were significant input variables for regional forest biomass mapping using ML. [ABSTRACT FROM AUTHOR]

• Spatiotemporal characteristics of trans-regional operations were first revealed. • Around 57.1 % of the harvesters completed 80.09 % of the total harvested area. • Flow and reliance of each region on harvesters were analyzed and described. • 6 provinces, 82 cities and 588 counties showed a high reliance on nonlocal harvesters. Trans-regional operation of agricultural machinery is an essential socialized service that can enhance the utilization rate of agricultural machinery and accelerate the progress of grain harvesting. In this paper, we quantitatively analyze the trans-regional operations of the harvesters using massive GNSS (Global Navigation Satellite System) data in China and reveal their spatiotemporal characteristics from the province, city and district perspectives. Trajectory data were collected from 25,763 harvesters during wheat harvesting season between May and June 2021 using the agricultural machinery operation big data system. Using the field road segmentation method for GNSS trajectory and the trans-regional operation judgment method, we calculated the quantity, driving distance, harvesting duration, and harvested area. Following this, we obtained the corresponding figures for each region, including the quantity, harvested area, harvesting duration, and distance of harvesters. To further analyze the flow and reliance on harvesters, we examined these factors at the province, city, and district scales for each respective region.. Our analysis revealed that approximately 57.1% of the harvesters were involved in trans-district operation, covering approximately 80.09 % of the total harvested area by all harvesters. Furthermore, we identified 6 provinces, 82 cities and 588 districts with a significant reliance on nonlocal harvesters. The findings provide valuable insights for the government, agricultural machinery enterprises, and agricultural machinery operators to optimize decision-making and configuration schemes to enhance the overall operational efficiency of harvesters. [ABSTRACT FROM AUTHOR]

• A defencing algorithm based on deep learning was proposed. • The algorithm based on U-Net and Pix2pixHD. • Chicken contours occlude by the wire mesh can be recovered. • The accuracy of caged chicken detection is improved after defencing. Cage farming is the mainstream farming mode in China. Accurate individual identification and behavioral detection of caged chickens can provide managers with a better understanding of chicken status. However, for image detection of caged chickens, the cage may affect the accuracy of the detection algorithm. For this reason, CCD (caged chicken defencing), a defencing algorithm based on U-Net and pix2pixHD, was proposed to improve caged chickens' detection accuracy. The proposed defencing algorithm can accurately identify the cage wire mesh and recover the chicken contours completely. In the test set, the detection accuracy of the cage wire mesh was 94.71%, while a structural similarity (SSIM) of 90.04% and a peak signal-to-noise ratio (PSNR) of 25.24 dB were obtained in the image recovery. To verify the practicality of the method proposed in this paper, we analyzed the performance of the object detection algorithm before and after defencing from the perspective of the most basic individual detection in the caged chicken detection task. We validated the defencing algorithm with different YOLOv5 detection algorithms, including YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. The experimental results showed that the defencing algorithm improved the detection precision of caged chickens by 16.1%, 12.1%, 7.3%, and 5.4%, respectively, compared with before defencing. The recall improvement was 29.1%, 16.4%, 8.5%, and 6.8%. To our knowledge, this is the first time that a deep learning-based defencing algorithm has been applied to caged chickens, and the detection accuracy can be significantly improved. The method proposed in this paper can remove cage wire mesh greatly and provide a technical reference for subsequent poultry researchers. [ABSTRACT FROM AUTHOR]

• A multi-view field-road classification method was proposed for GNSS trajectories. • Trajectories were converted to trajectory images to encode rich motion information. • Pixel-level visual features were extracted by a specific image segmentation model. The understanding of agricultural machinery, whether it is used "in field" or "on road", plays an essential role in optimizing the efficiency of cross-regional agricultural mechanization services that have been available in China for decades. With the widespread availability of Global Navigation Satellite System (GNSS)-enabled devices, developing a method for automatically identifying the activity associated with each point in GNSS recorded trajectories can significantly enhance the optimization of these services. In this paper, we propose a novel field-road classification method that utilizes two feature vectors to represent each point in a GNSS trajectory. The first feature vector is a statistical feature vector extracted with existing methods, while the second feature vector is a visual feature vector obtained through an image segmentation model. To extract visual features, we first convert each GNSS trajectory into a trajectory image that encodes the motion information (e.g., speed, direction) of each point and the spatiotemporal relationship between points. Then, an image segmentation model specifically for field-road classification was developed, which can effectively extract a visual feature vector for each pixel in a trajectory image, corresponding to each point in the GNSS trajectory. The image segmentation model used in our approach has a robust feature extraction capability that enhances the statistical feature representation of each point by adding pixel-level visual features. We evaluated the effectiveness of our multi-view field-road classification method using trajectories of corn, wheat, and paddy harvesters. The results demonstrated that our method achieved accuracy scores of 94.17%, 90.93%, and 83.43% for respective datasets. Our method outperformed state-of-the-art field-road classification methods, especially for trajectories with high-frequency GNSS acquisition. [ABSTRACT FROM AUTHOR]

• An algorithm (IDE) was proposed to design a new bionic subsoiling mechanism (BSM). • It is proved that IDE has better solution accuracy and speed. • Proposed a new method for establishing the soil model. • It was proved that the BSM has improved tillage performance based on DEM. • The driving parameters combination for the BSM was optimized. Because of the poor subsoiling effects and high tillage resistances of small subsoiling machines when used on red soil in southern China, a new subsoiling mechanism based on functional bionics and an optimization method that employed an improved differential evolution (IDE) algorithm are proposed in this paper. The tillage performance was improved by causing the subsoiler to mimic the digging motion of a mole forefoot. The path synthesis of the subsoiling mechanism was solved by the IDE, and the tillage performance of the four-bar bionic subsoiling mechanism was evaluated by the discrete element method. First, the digging trajectory of the mole forefoot was mapped as the desired subsoiling trajectory. An optimization model for the path synthesis was established to minimize the position error between the desired subsoiling trajectory and the coupler curve of the mechanism; it was solved by the differential evolution algorithm with improvements in the mutation and crossover operations. It was found that the IDE could more rapidly search for a more accurate solution. The optimization results were used to design a bionic subsoiling mechanism that could mimic the digging trajectory of a mole forefoot. Second, the microscopic contact parameters among the soil particles were calibrated by soil accumulation and direct shear tests. In addition, a discrete element soil model was established that included dual coupling of variable contact models for the tillage layer and the plow pan. The tillage processes of an existing common subsoiling mechanism (CSM), the bionic subsoiling mechanism equipped with a common subsoiler (BSM&CS), and the bionic subsoiling mechanism equipped with a bionic subsoiler (BSM&BS) were analyzed by a joint ADAMS and EDEM simulation. The simulation results were analyzed according to established evaluation indexes for tillage performance. The comprehensive soil disturbance performance of the BSM&CS improved by 257.03% and its drag reduced by 10.87% from the CSM values, while the comprehensive soil disturbance performance of the BSM&BS improved by 312.78% and its drag reduced by 18.40% from the CSM values. Finally, the driving parameter combination for the BSM was optimized using the central composite design method. [ABSTRACT FROM AUTHOR]

• Detection of cropland abandonment based on phenological information. • Summarized the inter-annual phenological changes of abandoned cropland. • Detecting cropland abandonment information at the sub-pixel scale. • Spatiotemporal patterns of abandoned cropland in this paper were revealed. Cropland abandonment is a common land-use change with mixed impacts on the environment and rural economic development. Prevalent small family farms and excessive land fragmentation result in cropland abandonment processes that are often gradual and spatially dispersed. These difficulties limit the ability to apply previous remote sensing mapping methods of cropland abandonment to areas with complex underlying surfaces, where mixed pixels and spectral aliasing problems arise. To meet the demands of broad-scale and high-accuracy abandoned cropland mapping, we developed the P C R R S B S - C V model by combining the phenology-based cropland retirement remote sensing (PCRRS) model with the coefficient of variation (CV) and tilled soil fraction (BS). This method combines information regarding crop phenology with Moderate Resolution Imaging Spectroradiometer (MODIS) time-series images. The PCRRS model was used to estimate changes in spectral metrics parameters during the process of cropland abandonment. Additionally, the dead fuel index and normalized difference vegetation index (NDVI) were employed to estimate the BS in mixed pixels (according to crop type). We predicted that use of the BS as the weighting coefficient for the PCRRS model would reduce interference from the mixed pixel problem, while spatial heterogeneity could be reduced by dividing the research area into regional units. Use of the CV of the NDVI time series with phenological information highlights the volatility of crop growth periods and helps to eliminate disturbances associated with woodlands and grasslands. Finally, we demonstrated this method in the Loess Plateau of China and a portion of central Europe; we verified its accuracy using high-resolution images from Google Earth. Our algorithm demonstrated overall accuracy of 82.2 % and can extract cropland abandonment information as little as 20 % of a pixel. The overall root mean square error (RMSE) was controlled below 15 %. In summary, the high accuracy achieved by this method enables the monitoring of cropland abandonment dynamics on a large scale. [ABSTRACT FROM AUTHOR]

• Constructing a three-layer large system for irrigation water allocation. • Balancing water supply and demand between farmland, irrigation areas and regions. • Farmer income, allocation equity, engineering and regional economy coordination. • Optimal water allocations for crops, canals and large irrigation areas. • Fluctuations in water supply affect water distribution of different scales. Reasonable and optimal allocation of irrigation water is of great significance to improve agricultural water use efficiency for large-area agricultural irrigation systems. To solve the problems of large agricultural production scale, unreasonable water distribution structure, many rivers in the region and large changes in runoff volume, this paper takes the Sanjiang Plain in Heilongjiang Province, China, as the research object to develop an efficient and accurate water optimal allocation method. First, the runoff volume of many rivers in the region was obtained through the ARIMA-SVM-BP combined runoff prediction model, based on which a 'regional-irrigation district-farmland' multilayer multiobjective water resource cooperative deployment system model was established to achieve the synergistic enhancement of farmers' interests and the economic-social and regional economic-engineering volume of the irrigation district. The joint optimization method, fuzzy mathematical planning, and large system decomposition-coordination coupling were used to solve the multilayer multiobjective regulation system. The results show that the optimal agricultural water allocation volumes for the current year (2020) and the forecast year (2025) of 37 irrigation districts in the Sanjiang Plain are 3.61 × 109 m3 and 3.89 × 109 m3, saving 17% of water and resulting in a 24% decrease in the agricultural water use ratio compared with the actual situation, respectively. In the process of repeated coordination of the large system, the water distribution error is controlled within 1%. The fairness of water distribution in the multilayer system was improved by 34.7% while obtaining high economic benefits. The method proposed in this study helps the coordinated regulation and planning of multiple water sources in large agricultural irrigation areas, improves the ability of water distribution in irrigation areas to cope with different levels of incoming water, and promotes the sustainable development of regional irrigated agriculture. [ABSTRACT FROM AUTHOR]

• The monitoring and estimation method of Winter Wheat Soil Moisture Based on multi-source sensing data and CNNR model is studied in this paper. • The proposed CNNR model has the best prediction accuracy. With the decrease of the SNR of radar echo, CNNR always has higher R2 and RPD, which indicates that the CNNR model can better predict the change trend close to the real value. Therefore, CNNR model has the best noise robustness among the three models. Rapid and accurate estimation of soil moisture content (SMC) is an important part of precision agriculture, and it is also one of the key problems to be solved in field real-time monitoring and precision irrigation. Most of the existing studies are limited to SMC monitoring of bare soil, which can be obtained by optical remote sensing (e.g., multispectral, hyperspectral) or Synthetic Aperture Radar (SAR). However, for the soil covered by vegetation, such as farmland, there are some theoretical defects with only one of the measuring methods. Meanwhile, in order to break through the limitations of low spatial and temporal resolutions of satellite remote sensing, it is of great significance to study SMC retrieval based on multi-source remote sensing data for the near earth UAV remote sensing systems. Based on this, this paper, taking the winter wheat planting area in Guanzhong plain of China as the research area, combines the advantages of ultra-wideband (UWB) radar, and multispectral remote sensing data, to reduce the influences of vegetation coverage on the estimation accuracy. A one-dimensional regression convolution neural network model is constructed to realize the quantitative prediction and estimation of SMC in farmland. The carried out experiments show that the proposed CNNR model has a better performance than traditional SVR and GRNN models and the R2, RMSE and RPD are 0.7453, 0.0140 cm3/cm3 and 1.9246, respectively. After introducing NDVI, MSAVI and DVI vegetation indices generated from multispectral images, the accuracy of the three models increased significantly. Among the three models, the constructed CNNR model has the best performance, and its R2, RMSE and RPD reach 0.9168, 0.0089 cm3/cm3, and 3.0201. Furthermore, after adding different levels of Gaussian noise to the original radar echoes, the CNNR model constructed in this paper still has the highest prediction accuracy and the strongest noise robustness. [ABSTRACT FROM AUTHOR]

• A detection approach for late-autumn shoots of litchi tree based on UAV remote sensing is provided. • A remote sensing dataset of late-autumn shoots of litchi tree is constructed. • A deep learning model based on the combination of YOLOv5 and Swin-transformer is proposed. • The detection accuracy of late-autumn shoots in remote sensing images is improved. • It will provide technical support for other researchers on late-autumn shoots and orchards manager. Litchi is one of the most common economic fruits in southern China, however, the growth of late-autumn shoots of litchi hinders flower bud differentiation and reduces yield of fruit. The early identification of the late-autumn shoots is of great significance for orchard management to control shoots and then increase fruit yield. At present, the identification of late-autumn shoots still relies on manual methods, which is not suitable for smart orchard management in a large area due to low recognition efficiency and high subjectivity. Therefore, a convenient, fast and cost-effective method is urgently needed. In response to this problem, the paper proposes a method based on the combination of unmanned aerial vehicle (UAV) remote sensing and object detection algorithm to detect late-autumn shoots. For this purpose, a remote sensing dataset of late-autumn shoots of litchi is first constructed by UAV. An improved YOLOv5 algorithm called YOLOv5-SBiC is then developed for late-autumn shoots identification. In the YOLOv5-SBiC algorithm, the transformer module is introduced to speed up the convergence of the network and improve detection accuracy, the attention mechanism module is employed to help the model extracting details, and BiFPN is used to better solve the multi-scale problem in detecting and then improve the recognition effect of small-sized objects. In addition, CIOU is selected as the loss function of bounding boxes regression to achieve high-precision localization of the boxes. The test results demonstrate that the recognition accuracy of YOLOv5-SBiC reaches a relatively high value of 79.6%, which is 4.0% higher than that (75.6%) of the original YOLOv5 algorithm and 15.9% higher than that (63.7%) of the pure transformer algorithm. It's also demonstrated that YOLOv5-SBiC is more competitive than the mainstream target detection algorithms in the dataset of late-autumn shoots. [ABSTRACT FROM AUTHOR]

• Forecast crop yield accurately by using dataset expanded and corrected by yield data compensation method. • Calculate the effects of different environmental factors on yield of different crop varieties and identify the key growth periods affecting crop yield. • Promote crop varieties according to comprehensive analysis of the promoted variety yield, preferences for different meteorological factors and the climatic characteristics of the target ecological regions. Forecasting crop variety yield, selecting suitable ecological regions for designated crop varieties, and finding high-yield crop varieties for designated ecological regions are all critical aspects of crop variety popularization. A crop variety yield prediction system based on variety yield data compensation (CVYPS-VYDC) is described in this paper. This novel system is used to predict the yield of designated crop varieties using the large amount of breeding data stored in the Golden Seed Breeding Cloud Platform and meteorological data from the National Meteorological Science Data Center. The prediction system was devised to (a) forecast the yield of designated crop varieties in the target ecological regions, (b) calculate the effects of different environmental factors on the yield of different crop varieties, (c) identify the key growth periods affecting crop yield, and (d) promote crop varieties according to a comprehensive analysis of the promoted variety yield, preferences for different meteorological factors, and climatic characteristics of the target ecological regions. In addition to meteorological factors, soil factors affect crop yield. However, large-scale measurements of soil composition require a large number of sensors and considerable human resources. By using the yield data compensation method, the yield data of predicted varieties across different regions were expanded and corrected, which reduces the yield prediction error caused by soil differences among regions to some extent. In addition, the coefficient of determination (R 2) between actual and predicted yield was improved to 0.82. In addition to predicting the yield of 90 maize varieties in the Huang-Huai-Hai area of China, the preferences of different maize varieties for different environmental factors were analyzed at the same time. CVYPS-VYDC identified the key environmental factors greatly impacting the maize yield, and the proportion by which different factors affected the yield prediction of different maize varieties was determined. Moreover, the growth periods that have great impact on maize yield were identified by analyzing 39 cultivation sites of various maize varieties in the Huang-Huai-Hai area of China. [ABSTRACT FROM AUTHOR]

• LRSMS based on dynamic model library is designed and implemented. • DML with three modules is developed and used for locust monitoring. • Two cases form China are tested and well verify the advantages of the LRSMS. Locusts (insect pest) often cause serious loss to crops world-wide. Monitoring the occurrence of locust plagues through remote sensing technology has become a common method. However, with the abundance of remote sensing data resources, the corresponding locust remote sensing monitoring models are also diversified, and the selection of model parameters are more complex. How to manage these models scientifically for better reuse is an important problem that needs to be addressed. In order to solve these problems, we designed and implemented a locust remote sensing monitoring system (LRSMS) based on dynamic model library (DML), which is composed of model file library, model dictionary library, and model library management module. In terms of data to match the remote sensing monitoring models, there is more integration of locust habitat data in LRSMS, such as plantation, soil, which are closely related to the locust plague breeding environment. In addition, to improve the scalability of the system, a data interface is designed for the interaction between the DML and the Structured Query Language (SQL) server database. At the end of the paper, two application cases from China are given, and the results show that the LRSMS can well integrate multi-source data sets and carry out locust plague monitoring research through the dynamic model library. The approaches and methodologies presented in this paper can serve as a reference for those who are interested in remote sensing monitoring applications. [ABSTRACT FROM AUTHOR]

• A more effective extraction method of time factor for soil type mapping based on remote sensing images was determined. • The method of principal component extraction result based on the tasseled cap transformation was more effective. • The fusion of multi-temporal features and texture features could improve the accuracies of soil type mapping. • The best texture feature for soil mapping was correlation parameter, which produced the largest overall accuracy of 86.81%. With the rapid development of remote sensing (RS) technology, remote sensing images provide an important data basis for soil type mapping. In remote sensing images, temporal factor is difficult to obtain directly, and the rich geometric features are not used adequately. Multi-temporal remote sensing data could effectively reflect the temporal variation of ground objects, while how to extract multi-temporal image features more effectively for soil type interpretation needs to be studied. Moreover, it is not clear whether multi-temporal features and texture features can be effectively integrated to improve mapping accuracies. Therefore, taking five soil types of Laoshan County, Shandong Peninsula, China as the subject investigated and six remote sensing images as data sources, this paper explored and compared two extraction methods of multi-temporal features from remote sensing images. The effects of the eight different texture features fused the multi-temporal features on digital soil mapping were also analyzed. The results showed that the principal component extraction result based on the tasseled cap transformation was better than based on the spectral band synthesis, increasing the overall accuracy by 3.83–11.41% and the kappa index by 0.03–0.13. The fusion of multi-temporal features and texture features can effectively improve accuracies of soil type mapping. After the addition of correlation texture feature parameter, the overall accuracy (86.81%) and Kappa index (0.81) were increased by 11.92% and 0.16, respectively. The research results showed that multi-temporal features in remote sensing images had great advantages in digital soil mapping, and the effective fusion with texture features provided a new idea for improving the accuracy of digital soil mapping. [ABSTRACT FROM AUTHOR]

• Correcting the agricultural machine body posture using the attitude of implement. • Establishment of kinematics model of agricultural machinery implement. • The Model Predictive Control path tracking control method is designed. The annual rice planting area in China is approximately 30 million ha. With continuous reductions in the rural labour force and increasing production costs, it is urgent to develop unmanned technology for paddy field agricultural machinery to solve the problem of "who will farm the land". In paddy field environments with slippery mud and uneven hard underlying, side slip and slip of paddy farm machinery can easily occur. Due to the relative movement and attitude changes between paddy farm machinery and implements and the inconsistent driving track of the farm machinery and implements, it is difficult to track and control the path of unmanned paddy farm machinery. In view of the above problems, this paper takes the paddy field agricultural machinery body as the control object and the agricultural machinery pose as the observation quantity and establishes an agricultural machinery kinematics model based on agricultural machinery pose correction. Based on the model and model predictive control (MPC), the linear model, objective function and constraint function of paddy field agricultural machinery are designed, an MPC path tracking control method is established based on the pose of the agricultural machinery, and field experiments are conducted. The results show that the average root mean square error of the three-line straight-line path tracking is 0.043 m, the average absolute error is 0.033 m, and the effect is favourable. The MPC path tracking method based on the pose correction of agricultural machinery can effectively suppress abrupt lateral position deviations caused by the relative position and attitude changes of the machine, can improve the control accuracy, and can meet the control accuracy requirements of unmanned paddy field agricultural machinery operations. [ABSTRACT FROM AUTHOR]

• Graphical user interfaces offering an accessible way to see and understand trends, patterns, and connotations in breeding data. • Statistical tools providing convenient experimental design and data analysis support which may reduce the professional demand for statistics skills of breeders. • Intelligent decision support tools for heterosis prediction and cultivar evaluation improving the decision-making ability with multi-dimensional breeding data. Plant breeding is an effective guarantee of agriculture production and food security, whereas information technologies are effective ways to promote plant variety improvement. Although breeding information technologies provide a convenient and scientific means for breeding, it also leads to the problem of massive data processing with multidimensional breeding data in multiple granularities across generations. Therefore, decision support tools that help breeders extract relevant and valuable information are necessary and important. This paper introduces the decision support tools of the golden seed breeding cloud platform at three levels: (i) graphical user interfaces; (ii) statistical tools; and (iii) intelligent decision support tools. The advantages have effectively improved the breeding efficiency as well as the data management and application capabilities in more than 150 breeding institutions in China. The successful application of our system confirms the effectiveness of our work and provides data and support for future research. [ABSTRACT FROM AUTHOR]

• The residual structure is introduced into the Inception structure as a convolutional block, and a plant disease classification network is established. • An improved Convolutional Block Attention Module combing channel attention and spatial attention is embedded in the classification network. • Corn, potato and tomato are identified as a whole and individually by using a classification network. • Designed and developed a web-based disease detection system, which can complete real-time identification of plant diseases on mobile phones and computers. In this paper, we proposed a convolutional neural network based on Inception and residual structure with an embedded modified convolutional block attention module (CBAM), aiming to improve the classification of plant leaf diseases. Corn, potatoes and tomatoes are the most cultivated grains in southern China. The leaves of the three crops are very fragile and sensitive and are susceptible to leaf diseases, such as leaf blight of corn, late blight of potato and mosaic virus of tomato. These diseases cannot be identified at early stages. Therefore, an efficient solution is proposed by deep learning techniques to detect the disease categories of crops, which can effectively prevent the spread of diseases and ensure the normal growth of plants. In this experiment, our model achieved an overall accuracy of 99.55% for the identification of the three diseases of corn, potato and tomato. In addition, we tested the three plants individually. The classification accuracy of our model on corn, potato and tomato was 98.44%, 99.43% and 95.20%, respectively. We have also developed a web-based real-time plant disease classification system and deployed our model. The system had good performance in time and accuracy evaluation metrics. The results of this study showed that our model had fewer parameters, shorter training time, and higher recognition accuracy compared to existing image classification models. [ABSTRACT FROM AUTHOR]

• Estimate soil moisture content on the spatial scale of the catchment region. • The synergic application of Sentinel-1 radar backscattering coefficients and Sentinel-2 optical images to monitor soil moisture content. • The revision of the water cloud model localization. • Inversion of vegetation moisture content by combining NDVI from Sentinel-2 and on-site samplings. Remote sensing techniques, which provide timely and accurate soil moisture information to improve food production, are becoming increasingly popular among agricultural production in China and many other regions. It also shows its importance in monitoring agricultural basins where the meteorological and topographic conditions vary greatly. In this case, the reliability of soil moisture content (SMC) monitoring for theses basins could be promised. The main objective of this paper is to develop a synergic method of radar and optical data, as well as an on-site sampling scheme, to accurately estimate SMC in the basin. The reason is that in the winter wheat production area of Dawen River basin in China, there are few conventional SMC testing equipment. Considering the characteristics of different crops and the conditions of crops in different growth periods, the VV polarization mode of Sentinel-1 C-band Synthetic Aperture Radar (SAR) images was used to retrieve SMC. Combined with the Normalized difference vegetation index (NDVI) extracted from Sentinel-2 MSI optical images and the optimized Water Cloud Model (WCM), the surface backscattering coefficient was obtained by eliminating vegetation moisture content (VMC), and the SMC model was established. The results showed that from March to May in 2020, the coefficient of determination (R2) between the estimated VMC and the field observation was 0.655, and the Root Mean Square Error (RMSE) between the two was 0.334. In the inversion algorithm model, the R2 between the retrieved SMC and the field measurement was 0.506, and the RMSE was 3.725. [ABSTRACT FROM AUTHOR]

• We propose a target-free rough registration method for TLS-ULS point cloud data. • We propose a fast fine registration method to improve the result. • We explore the influence of the change of search window on the final accuracy. Modern laser scanning techniques have been commonly used for forest land studies. Complementary scanning capabilities can be particularly obtained using unmanned aerial vehicle laser scanning (ULS) and terrestrial laser scanning (TLS) technologies. The registration of the ULS and TLS data can thus lead to more comprehensive data acquisition and information extraction in forest lands. However, this registration process is typically hindered by problems of data density and inconsistency of the scanned forest canopy shape. In this paper, we propose a tree-height registration (TR) method for ULS-TLS point-cloud registration, and apply this method to the alpine forest land of the Shangri-La City of the Northwestern Yunnan Province in China. The tree height points are obtained from a digital surface model (DSM), which contains isosurface points of the forest structures. Rotation and translation matrices are then calculated through singular value decomposition (SVD), and rough registration is completed. Finally, fine registration is achieved through nearest-neighbor iterative SVD. The results show that the proposed method can effectively register ULS and TLS forest data samples, with an average accuracy of 0.43 m. The method has an average running time of less than 21 s through 3m-by-3m window size to search tree height point for registration, and also shows good applicability in forest lands with slopes in the range of 2-18°. Moreover, the best outcomes were obtained for a 3m-by-3m window size. [ABSTRACT FROM AUTHOR]

• A novel method for matching creative agri-product scheme was proposed. • Demand documents and scheme documents were structured through MGSSA algorithm. • The numerous demanders were clustered through the CDF-based K-means algorithm. • The matching pairs were generated through a multi-objective optimization model. • The concerned case problem was solved based on this study. Transformation and upgrading are crucial for the development of agri-products, whereas an appropriate creative agri-product scheme is the key. How to match the demanders and suppliers of creative agri-product schemes based on the demand analysis of demanders and the expected implement effect analysis of creative schemes provided by suppliers is of great significance, while research concerning this issue is still relatively scarce, especially in how to organize and mine the demand information and expected implement effect information. The purpose of this paper is to develop a novel sentiment analysis-based method for matching creative agri-product scheme demanders and suppliers by taking a case study from China. The procedure of this method includes five parts: first, structured processing of demand documents of different demanders; second, formation of creative agri-product scheme demand matrix; third, formation of creative agri-product scheme supply matrix; fourth, measurement of matching degrees of creative agri-product scheme supply and demand; fifth, construction and solution of a multi-objective optimization model for the matching problem. To illustrate the use of the proposed method, an empirical analysis of generating suitable creative agri-product scheme demand and supply matching pair for nut agri-products in XS village is given. The obtained computational process and results clearly demonstrate how to generate the optimized matching pairs for creative agri-product scheme between demanders and suppliers. [ABSTRACT FROM AUTHOR]

• An improved adaptive updating ant colony optimization algorithm (AU-ACO) is proposed. • Feature bands extracted by AU-ACO is taken as input to reduce the complexity of model. • A new hybrid prediction model (ELM-P) combining ELM with PLSR is proposed. • This model can estimate rice chlorophyll content with extracted feature band. • The propose method has better superiority compared with other modeling methods. Using spectral information to detect chlorophyll content in rice canopy leaves quickly, non-destructively and accurately has a great practical significance for rice growth evaluation, precise fertilization and scientific management. In this paper, Japonica rice in northeast China is taken as the research object, and rice canopy hyperspectral data of key growth stages are obtained through plot experiments. Firstly, the standard normal variate (SNV) and SG smoothing method is used to preprocess the hyperspectral data, and based on the processed spectral data and ant colony optimization algorithm (ACO), an improved adaptive updating ant colony optimization algorithm (AU-ACO) is proposed to select feature bands of chlorophyll content by introducing an adaptive adjustment strategy of volatilization coefficient and optimal pheromone updating strategy in different stages, and compared with the standard ACO algorithm and full-band modeling method. Then, taking the extracted feature band and full-band as input, considering the advantages of linear model and nonlinear model, a hybrid prediction model (ELM-P) combining extreme learning machine (ELM) with partial least squares regression (PLSR) is proposed. In this model, PLSR is used to obtain the preliminary prediction of the chlorophyll content in rice, and the linear trend is obtained, and then the ELM with strong nonlinear approximation ability is used to predict the deviation of PLSR model, the final prediction value is obtained by superposition of the two outputs. In order to verify the superiority of the proposed model, PLSR and ELM prediction models are also established by taking the full-band and the feature bands by different extraction methods as input. The simulation experiment results show that under the same prediction model conditions, the feature bands extracted by proposed AU-ACO algorithm as input can reduce the complexity of the model and improve the prediction performance of the model. The determination coefficient (R 2- P) of testing set and the determination coefficient (R 2- C) of training set for each model are greater than 0.6785, among them, the ELM-P model with the feature band extracted by AU-ACO algorithm as input has the highest prediction accuracy, R 2- C and R 2- P are 0.7969 and 0.7918 respectively, RMSE- C and RMSE- P are 1.2969 and 1.1293 mg/L respectively, which can provide valuable reference for detecting and evaluating chlorophyll content in Japonica rice. [ABSTRACT FROM AUTHOR]