Abstract:In order to optimize the operation routes of agricultural machinery for cleaning old pineapple seedlings in pineapple orchards and reduce consumption in non-working areas, a path planning method integrating improved genetic particle swarm optimization (GPSO) and Q-Learning(QL) algorithms was proposed to optimize the complete coverage operation path of unmanned machines in multi-field pineapple orchards. Images of the target farmland were obtained by unmanned aerial vehicle and converted into a coordinate map. Each field was traversed at 5° intervals from 0° to 355° to find the optimal travel angle. To address the inherent problem such as slow convergence and getting stuck into a local optimum in traditional GPSO algorithms, an improved GPSO algorithm, namely IHGPSO, was proposed to improve the generation and selection method of initial populations, a multi-objective weighted fitness function was designed, and a dynamic adjustment probability was added to particle exchange mechanism, thereby obtaining the optimal traversal order for multi-field complete coverage operation of pineapple orchard. To address the inherent problem in original QL algorithm, such as low exploration efficiency and slow convergence, an improved QL algorithm, namely order initialization Q-Learning(OI-QL) was proposed, which improved initialization of Q-table, proposed learning rate, and designed a reward function, thereby connecting multi-field complete coverage operation path of pineapple orchard. Simulation results showed that when the sub-area point groups contained 30 points, the IHGPSO algorithm outperformed the GPSO algorithm in solving the problem of the optimal traversal order. The average path length planned by OI-QL algorithm was 28.1% shorter than that planned by QL algorithm, and the average number of convergence iterations and the average convergence time by using OI-QL algorithm was 37.3% and 25.7% less than those by using QL algorithm, respectively. These results indicated that the method can effectively complete the complete coverage operation path planning of pineapple orchard.

Abstract:During lotus seedpod harvesting, efficient and precise detection and localization are essential for improving harvesting efficiency and minimizing the risk of mispicking. However, existing lotus seedpod recognition methods predominantly rely on computationally intensive and structurally complex deep learning models, rendering them impractical for real-time field applications. To address this limitation, a lightweight object detection and localization method optimized for lotus seedpod harvesting scenarios was proposed. The method was based on the lightweight lotus segmentation network (LLSegNet), a lightweight semantic segmentation model that employed MobileNetV2 as the backbone within the DeepLabv3+ framework. To cope with challenges such as multi-scale variation, difficulty in capturing fine details, and background interference during harvesting, key enhancement strategies, including dense atrous spatial pyramid pooling, strip pooling, convolutional block attention, and an efficient channel attention network. These improvements enhanced multi-scale feature extraction and representation while maintaining the lightweight nature of the overall model. Experiments were carried out on an Ubuntu 20.04 platform utilizing the PyTorch 2.3.1 framework for model training and evaluation. The results demonstrated that the LLSegNet model attained an average intersection over union (mIoU) of 86.1% and an average pixel accuracy (mPA) of 92.5%, with a memory footprint of 15.9 MB and a frame rate (FPS) of 73.4 f/s, all of which were superior to mainstream semantic segmentation models. Furthermore, leveraging the high-quality semantic segmentation results produced by the LLSegNet model, a harvesting point localization method that integrated image processing and skeleton analysis was proposed. This method accomplished precise localization of harvesting points through a combination of image preprocessing, skeleton extraction, geometric analysis, and normal vector expansion mapping, achieving a success rate of 88.5%. The findings demonstrated that the proposed method not only improved detection and localization accuracy but also remained computationally efficient, showing strong potential for deployment and further application in resource-constrained agricultural environments.

Abstract:Aiming to enhance the efficiency of mechanical dike construction in paddy fields and reduce labor intensity during operations, a suspended single-side dike construction machine was designed for paddy fields based on a multi-link rotary adjustment mechanism. This machine addressed the inability of traditional single-side dike construction machines to achieve continuous mechanical dike construction at paddy field corners. The study elaborated on the structural design and working principles of key components: the lateral offset mechanism and rotary adjustment mechanism. A kinematic model of the lateral offset mechanism and rotary adjustment mechanism was constructed. Through kinematic analysis, lengths of lateral swing arm Ⅰ, lateral swing arm Ⅱ, and rotary swing arm were determined as 1 260 mm, 672 mm, and 284 mm, respectively, and installation positions of each stepper electric cylinder were identified. Based on kinematic analysis, a multi-link rotary adjustment control system was designed. Performance tests were conducted on the rotary adjustment and field performance of the suspended single-side dike construction machine. Results of the rotary adjustment performance test showed mean square errors between rotational angles of lateral swing arm Ⅰ, lateral swing arm Ⅱ, and rotary swing arm over time and their corresponding theoretical values as 0.45(°)2, 0.49(°)2, and 0.86(°)2, respectively. The mean square error value between rotational angles of all swing arms over time and their corresponding theoretical values throughout entire rotary cycle was 0.64(°)2, indicating high stability during overall rotary operation of the machine. The field performance test results showed that the average soil compaction at each measurement point of the embankments constructed along the straight sections was no less than 1 085 kPa, and the average straightness was no more than 10.4 mm. At the corner sections, the average soil compaction at each measurement point of the embankments was no less than 1 027 kPa, and the average straightness was no more than 9.39 mm. All results met the agronomic requirements for embankment construction in paddy fields.

Abstract:Aiming at the problems such as large turning radius, poor steering stability and difficult to adapt to the working environment of the existing paddy field weeder, a hydraulic steering system for the chassis of paddy field weeder was designed. Firstly, the simulation model of the steering hydraulic system was established by Amesim software, and the corresponding relationship between the displacement of the front and rear steering cylinders under different angle signals was analyzed. Secondly, in Recurdyn, the differential steering was used to simulate the four-wheel steering condition of the rice weeder chassis model, and explore the changes of vertical and lateral centroid acceleration of the chassis in the steering process. Finally, the steering hydraulic system was applied to the chassis of a paddy field weeder, and the minimum turning radius test and steering cylinder displacement test were carried out. The results showed that the minimum turning radius of chassis of paddy field weeder was 3.04 m, which was 1.3% smaller than the theoretical calculation value of 3.08 m, but the error was within the acceptable range, which met the design requirements and effectively reduced the seedling compression rate. Under different angle conditions, the average error of front and rear cylinder displacement was 0.3 mm, and the maximum error was 1.2%, which met the requirements of four-wheel steering, and ensured the stability and operation accuracy of the paddy field weeder in the steering process. The research can provide a feasible scheme for the steering system of the paddy field weeder chassis, and it effectively improved the existing problems of the paddy field weeder.

Abstract:Aiming to improve the performance of the mechanical maize precision seed metering device in high-speed operation, a seed-disturbing mechanism was designed to discretize the originally static accumulation of seeds, increase the mobility of seeds, and improve the precision seeding conditions. The theory analysis and structure design of the disturbing mechanism were carried out. The influence of disturbance teeth shape, disturbance teeth height, and seed pile height on the seeding performance was studied by the discrete element simulation analysis method. The influence of each factor was clarified. To verify the theoretical analysis results and obtain the best performance parameters, the shape of seed-disturbing teeth, the height of seed-disturbing teeth, and the height of the seed pile were used as test factors. The breakage rate, qualified rate, multiple rate, and missing rate were used as test indicators to carry out the three-factor and three-level center orthogonal test. The test data were analyzed by multiple regression analysis and response surface analysis using Design-Expert software. The influence relationship of each factor on the index was obtained. A multi-objective optimization method was used to determine the better parameter combination. When the operation speed was 13 km/h, the shape of seed-disturbing teeth was D, the height of seed-disturbing teeth was 11.20 mm, and the seed layer height was 66.40 mm, the qualified rate was 94.75%, the multiple rate was 2.71%, the missing rate was 2.54%, and the breakage rate was 0.42%, which met the technical requirements of precision seeding under the condition of high speed.

Abstract:Aiming to address the low precision of traditional seeding equipment in rock wool substrate seedling cultivation of plant factories caused by easy seed bouncing and small seeding area, a needle-suction precision seeding device was designed. A control system was built with a programmable logic controller (PLC) as the core;a curved track for the cylinder-driven component was designed to enable the suction needle to pick and drop seeds vertically. Based on ANSYS Fluent simulation, the optimal parameters of the air chamber and suction needle were obtained (air hole diameter: 8 mm, spacing: 205.3 mm), and experiments were conducted to determine the air chamber's vacuum pressure and suction needle diameters suitable for different seeds. EDEM simulation showed that the seed guide tube combining a parabolic deceleration guide surface and an arc-shaped direction guide surface achieved the best seeding effect. Box-Behnken experiment-based optimization and verification revealed: for Lactuca sativa, the optimal parameters were 5.9 kPa of seed suction negative pressure, 0.6 mm of suction needle diameter, and 44.4 Hz of vibration frequency;for Brassica rapa subsp. chinensis, the optimal parameters were 4.2 kPa of negative pressure, 0.5 mm of suction needle diameter, and 42.5 Hz of frequency. When the productivity was 80 trays/h and 100 trays/h, the single-seed qualification indices of Lactuca sativa were 98.35% and 98.08%, respectively, while those of Brassica rapa subsp. chinensis were 98.29% and 98.11%, respectively. These results can meet the requirements of high-precision operations and provide technical support for high-precision plug seedling seeding equipment for vegetables in plant factories.

Abstract:In response to the problems of low transplanting efficiency, poor opening and closing performance, and poor hole formation in the seedling transplanting process of pepper seedling tray, a chain cam planting mechanism was designed. According to the agronomic requirements of pepper seedling transplantation, the kinematic mechanism of the planting mechanism was analyzed, the basic parameters of the virtual planting mechanism were selected, the three-dimensional modeling was constructed by using Solidworks software, and the kinematic theoretical model was established;the discrete element model of the soil particles was determined, the coupled EDEM-RecurDyn simulation model of the planting mechanism was established, simulation analysis of the planting mechanism was carried out, and the simulation results preliminarily verified preliminary verification of the correctness and rationality of the mechanism. Taking the forward speed of the transplanting machine, characteristic parameter of speed ratio, and height of center axis, as test factors, and the uprightness, missed planting rate, and coefficient of variation of spacing of plants as test indexes, the quadratic rotary orthogonal combination test was conducted to establish the regression model between indexes and factors to determine the better parameter combinations of transplanting operation. The results of validation test showed that under the conditions of parameter forward speed of 0.4 m/s, speed ratio of 1.29, and height of center axis of 336.8 mm, target value of uprightness was 78.2°, the missed planting rate was 1.7%, and the coefficient of variation of spacing of plants was 5.23%, which can meet the actual production requirements of the pepper planting operation.

Abstract:Aiming at the problems of low working efficiency and insufficient depth of buried soil in the existing vine burying machine with one-side round-trip operation, a composite technology of bilateral synchronous operation and conical rotary throwing buried soil was proposed, and accordingly a conical rotary throwing device was designed to realize single-pass completion of buried soil on both sides of vines. The core components of the device included a spiral cutterhead, a retaining cover, and a throwing soil rotary knife. By establishing a mathematical model of the spiral curve, the key parameters of the spiral cutterhead and the retaining cover were optimized to determine the reasonable value range of main parameters affecting the buried soil effect. Based on EDEM discrete element platform and Design-Expert software, three-factor three-level orthogonal simulation test was carried out, and Box-Behnken response surface analysis method was used to verify the rationality of parameter combination. The optimal parameter combination was obtained by numerical optimization module analysis. When the cutter pitch was 23 cm, the opening angle of the retaining cover was 100°, and the spiral length was 125 cm, the corresponding buried depth was 36.39 cm, and the horizontal forward resistance was 1 059.99 N. The field test showed that the measured average buried depth and horizontal forward resistance were 39 cm and 1 149 N, respectively, and the relative errors with the simulation results were 6.7% and 7.7%, respectively. The coefficient of variation of buried depth stability was 1.86%. The research result can provide a theoretical basis and design reference for the in-depth research and development of wine grape bilateral buried equipment.

Abstract:During field operations, tracked combine harvesters often experience longitudinal slip and lateral drift, which cause deviations of the actual trajectory from the planned path and severely affect operational performance. However, conventional path tracking control is still mostly based on the ideal no-slip assumption, which fails to accurately capture the nonlinear interactions caused by slip between the tracks and the ground surface. At present, practical methods that can effectively suppress slip disturbances and ensure continuous and stable path tracking of harvesters remain lacking. To address this issue, a slip-aware model predictive path integral (SLIP-MPPI) path tracking method was proposed for tracked combine harvesters. In the kinematic modeling, the method incorporated an approximate model of longitudinal slip ratio and lateral displacement to correct state propagation. Additionally, a slip penalty term was introduced into the cost function, enabling the sampling-based optimization to actively avoid unstable trajectories with excessive slip. Furthermore, a response surface methodology was employed to perform multi-objective optimization of the cost function weights, yielding more reasonable combinations of weight coefficients. Field experiments demonstrated that SLIP-MPPI achieved significantly lower lateral and heading errors than conventional MPPI under complex steering conditions. In sinusoidal path tests, the maximum lateral error and standard deviation of SLIP-MPPI were reduced to 0.029 m and 0.010 m, respectively, representing decreases of approximately 40.8% and 41.2% compared with MPPI. The maximum heading error and standard deviation were 1.84° and 0.91°, corresponding to reductions of 38.5% and 27.8%. In addition, straight-line harvesting tests showed that the method maintained stable operation, with well-aligned working strip boundaries and without noticeable deviations or serpentine trajectories. These results verified the effectiveness and robustness of the SLIP-MPPI method under complex field conditions. The proposed SLIP-MPPI method provided a feasible solution for achieving high-precision path tracking of tracked combine harvesters under complex soil conditions, contributing to the advancement of agricultural machinery automation and the development of smart agriculture.

Abstract:Accurate recognition of the silage harvester follow-up trailer hopper is the first prerequisite for realizing automatic throwing and loading, for the problem that the complex operational environment of silage harvesting, the variety of trailer types, and existing methods for trailer hopper recognition struggle to apply to all kinds of loading hopper identification, using the technology of image semantic segmentation, a silage harvester follow-up trailer hopper recognition method was proposed with improved DeepLabV3+. The method took the lightweight MobileNetV3 network as the backbone network for feature extraction to reduce the model complexity. In the ASPP module, the original average pooling was replaced by strip pooling to enhance the recognition ability of the hopper, and the original three atrous convolution branches were expanded into four atrous depthwise separable convolution branches to improve the recognition of large targets. Moreover, the multi-scale features in the decoder part were integrated to compensate for the lost hopper boundary detail features. After concatenation and fusion, the C2f-GS module was added to further enhance model's feature fusion capabilities. The ablation tests and comparison tests were conducted on the constructed hopper dataset to verify the effectiveness of the improved model, and the test results showed that the values of the mean intersection over union (mIoU) and mean pixel accuracy (mPA) of the proposed improved DeepLabV3+ model reached 94.25% and 96.62%, which was better than that of the other segmentation models, and the model parameters and FLOPs were significantly lower than that of the other models. Compared with the original DeepLabV3+ model, the mIoU and mPA of the improved model were increased by 4.77 and 3.71 percentage point, model recognition accuracy was effectively enhanced. Using the hopper fitting method based on the RANSAC algorithm, the boundary of the hopper was fitted from the segmented results, fitting experiments conducted under various conditions revealed that the mIoU of the fitted hopper and the original labeled image reached 94.49%, and the deviation of the center-of-mass point position between the two was only 25.47 pixels, which indicated that the fitting result basically could cover the area of the hopper , meeting the operational requirements for silage harvesting in the field.

Abstract:In the pastoral regions of the Qinghai-Tibet Plateau, the traditional method of manually braiding freshly harvested forage for drying is characterized by low efficiency and high labor intensity. To address this challenge and achieve mechanized, continuous, and stable grass braid forming, a vertical green grass braiding device was designed. The device featured an adaptive mechanism that accommodated varying forage feed rates and enabled continuous forced twisting. Based on the mechanical analysis of the twisting process, a quantitative relationship between the feed roller rotational speed and the twisting angular velocity was established. Compression characteristic tests were conducted by using green oat straw, and the relationship between compression force and displacement was accurately modeled by a quartic polynomial equation with a coefficient of determination exceeding 0.99. Utilizing a custom-built experimental braiding platform, a three-factor three-level orthogonal test was performed to investigate the effects of feed thickness, feed pressure, twisting torque, and feed linear speed on the forming quality, with the maximum tensile strength of the formed grass braid as the primary evaluation index. The experimental results indicated that effective twisting was achieved when the feed roller pressure was maintained within the range of 8.26 kg to 17.89 kg and the twisting torque exceeded 12.43 N·m, with higher feed thickness requiring greater applied pressure. Under the optimal combination of operating parameters, specifically a feed-twisting roller linear speed of 0.10 m/s, a twisting speed of 89 r/min, and a feed thickness of 0.10 m, the formed grass braid attained a maximum tensile strength of 120.58 N. The research demonstrated that the proposed vertical single-twist braiding device, operating under adaptive feed conditions, which can successfully achieve continuous forced twisting and produce grass braids with tensile strength sufficient to meet the requirements for subsequent field drying. The findings can provide a theoretical basis and technical reference for the development of mechanized grass braiding equipment for alpine pastoral applications.

Abstract:In response to the problems of low harvesting efficiency and high risk coefficient of local jujube in hilly and mountainous areas, a shaking-type jujube harvesting device was designed, which used an eccentric disk to drive a steel wire rope for traction and shaking the tree body to make the fruits fall smoothly. Based on detailed kinetic analysis, a double-pendulum model of jujube fruit-stalk" was established. Combining with actual measurement data, the natural frequency of the system was obtained as 9.96 Hz, and the average acceleration required for vibrating and detaching jujubes from their stalks was 283.81 m/s2. A flexible model of the jujube tree was established through Hypermesh software. By integrating ADAMS software, a comprehensive rigid-flexible coupling simulation test of the jujube shaking and harvesting process was carried out. Taking the shaking frequency, amplitude and shaking time as influencing factors and the harvesting rate as the evaluation index, an orthogonal test was carried out. The test results showed that the optimal parameter combination was a shaking frequency of 9.835 Hz, an amplitude of 50.602 mm and a shaking time of 5.387s. Under these parameters combination. The field test results showed that the average harvesting rate of jujube fruits was 97. 5%, which provided a valuable design reference for the development of harvesting machinery for small-sized fruits such as jujubes in similar regions.

Abstract:Aiming to address the problem that existing integrated blade-type underground cutting devices for leafy vegetables cannot adapt to the lateral undulations of wide ridges in spinach cultivation, leading to root cutting failures, a split-body shovel into soil copying cutting device was designed to reduce the impact of ridge conditions on spinach root cutting effectiveness. The shovel's motion mode was determined through motion analysis, and the main structural parameters of the shovel were obtained by using discrete element simulation. Based on the shovel's motion requirements, a crank-connecting rod-parallel four-bar linkage transmission mechanism was designed. Through mechanism-soil interaction force analysis, the overall structural parameters of the contour-following mechanism and the compression spring were determined. A mathematical model of the transmission mechanism was constructed by using the complex vector method. Based on the shovel cutting trajectory optimization analysis, the speed ratio coefficient between the forward velocity and crank angular velocity during the up-and-down movement of the cutting component with the ground surface was determined, along with the functional relationship between the longitudinal coordinates of the contour-following rods. Field trials verified the accuracy of the optimized cutting trajectory results. Based on the optimal cutting trajectory, the best operating parameter combination for the split-shovel type soil-entry contour-following device was obtained: crank angular velocity was 18.85 rad/s, speed ratio coefficient was 7.92 mm/rad, at which the average root cutting qualification rate was 95.23%. Under the optimal combination of operating parameters, a performance verification test of the contour-following mechanism was conducted. The results showed that the root length was maintained within the range of 11 mm±3 mm, with an average root length variation coefficient of 37.96%. Comparing the operating effects of existing integrated blade harvesting equipment and split-shovel cutting devices, the results showed that the split-shovel cutting device reduced the root length variation coefficient by an average of approximately 63.42 percentage points and increased the root cutting qualification rate by an average of approximately 17.4 percentage points. The research results can provide a novel cutting method for spinach harvester research and offer component support for the overall machine development.

Abstract:Due to the time-consuming and labor-intensive nature of manual harvesting of leafy vegetables, coupled with insufficient research into the interaction mechanisms between harvesting machinery and vegetables, leading to high plant damage rates caused by the incompatibility of operating parameters in machinery, taking the subterranean-root-cutting crop, Brassica campestris L. ssp. chinensis var. communis, as the research subject, a multi-body coupled motion model for soil-root-cutter interaction and a clamping damage model were established. The dynamic mechanism of plant cutting, clamping, and collection processes was analyzed, revealing that the primary factors influencing the damage rate were the cutter swing frequency, the inclination angle of the clamping conveyor belt, and the ratio of clamping conveyor speed to forward speed. Using discrete element simulation analysis, a soil-root-cutter aggregate model was established, determining the optimal cutter frequency range to be 4~22 Hz. Based on standard and improved parameters, single-factor and three-factor three-level orthogonal optimization experiments were conducted with cutting frequency, clamping conveyor belt inclination angle, and the ratio of clamping conveyor speed to forward speed as experimental factors, and the leafy vegetable damage rate as the experimental indicator. The optimal parameter combination was determined to be a cutting frequency of 7 Hz, a clamping conveyor belt inclination angle of 18°, and a clamping conveyor speed to forward speed ratio of 2.1. The average damage rate in field trials with this optimal parameter combination was 2.12%. The research result can provide a basis for selecting working parameters and a theoretical foundation for the research and development of low-damage harvesting technology for leafy vegetables.

Abstract:Based on the geometric features of the pineapple's outer shape, the easy breakage at the calyx joint and the brittleness of the stem biological characteristics, a breakaway separation type end-effector that mimics the manual breaking method of harvesting was designed. The key components of this harvesting device included a linear guide sleeve and a curved guide sleeve. Mechanical property tests based on the principle of pineapple fruit-stem fracture showed that the maximum breaking torque, maximum breaking angle, and critical damage force were 3.64 N·m, 67.4°and 178.72 N, respectively. A kinematic model of the pineapple clamping mechanism was established, with the goal of achieving a force transmission ratio at least 1.25 and a compact size. Optimizing with Matlab, the length of the linear guide rail in the linear guide sleeve was determined to be 60 mm and the dimensions of each link were obtained. Based on static force analysis, it was found that the driving force magnitude was related to the helix angle of the curved guide rail in the curved guide sleeve. A smaller helix angle resulted in increased overall size and weight, while a larger helix angle caused the thickness of the curved guide sleeve to be less than the diameter of the guide column, making it impossible to break the pineapple. Therefore, the middle value of 45° was chosen as the helix angle, and the driving force was obtained as 90 N. A simulation model was established to analyze the mechanical properties of the pineapple plant during the harvesting process, comparing the peak contact force between the gripper and the fruit with the critical damage force. A prototype was trial-produced and harvesting tests were conducted. The test results showed that the grasping success rate was 100% and the harvesting success rate was 86.7%, which verified the reliability of the end-effector. The research results can provide a reference for the mechanized harvesting of pineapples.

Abstract:Aiming at the problems of high labor intensity of manual feeding, poor uniformity of feed mixing and low feeding accuracy in the process of sea cucumber breeding, an integrated sea cucumber feeding machine for stirring and feeding was designed, which integrated the functions of feed stirring, delivery, and moving. In terms of the optimization of the stirring device, a combined structure of a 45°downward-pushing four-inclined blade stirring paddle and a semi-circular tube deflector plate stirring tank was adopted. The uniformity of the distribution of sea mud particle content was explored through flow field analysis. The test results showed that when the stirring paddle speed was 100 r/min, the sea mud particle content was 51.25%, which was 1.25 percentage points lower than the standard value. The uniformity of the feed was superior to that of manual stirring (the content of sea mud particles was approximately 40%). In terms of the optimization of the placement device, based on hydrodynamic calculations, the performances of conical diffuser nozzles, abrupt conical diffuser nozzles, conical non-diffuser nozzles and conical cylindrical diffuser nozzles were compared and analyzed, and the conical diffuser nozzle was determined to be the optimal structure through orthogonal experiments. The proportion of the optimized nozzle bait coverage area was 90%, the average feeding range reached 4.38 m, and the feeding speed was stable at 0.3 kg/s. Under the condition of a feeding distance of 4 m, the content of sea mud particles in the feed was 48.52%, which was 1.48 percentage points lower than the standard value, meeting the aquaculture requirements for uniform feed mixing and feeding performance. According to the requirements of the stirring device and the feeding device, a traveling device was designed to ensure the normal use of the feeding machine in the breeding area. This sea cucumber feeding machine can effectively replace manual feeding, reduce labor intensity, improve the uniformity of feed mixing and feeding efficiency, which can lay a foundation for the mechanization of aquaculture operations and future intelligent upgrades.

Abstract:Aiming to address high kernel loss and breakage rates during seed maize husking and insufficient modeling precision, a flexible composite discrete element method (DEM) model for seed maize ears was developed. The intrinsic parameters and contact parameters of different components of seed maize ears were determined through physical experiments. A flexible composite DEM model comprising bract-kernel-cob was constructed based on the Hertz-Mindlin with Bonding V2 contact model. The combination of Plackett-Burman design, steepest ascent method, and Box-Behnken response surface methodology was employed to calibrate and optimize the key bonding parameters of cob shaft and kernels, establishing a DEM model capable of accurately characterizing kernel detachment and breakage during husking. The simulation results showed high calibration accuracy, with relative errors of 3.89% for cob shaft three-point bending, 8.79% for kernel compression, and 7.58% and 6.89% for kernel detachment in axial and tangential directions respectively compared with physical experiments. In husking simulation validation, kernel detachment and breakage rates were 1.774% and 0.457% respectively, compared with bench test results of 1.697% and 0.432%, with relative errors of 4.54% and 5.79%. All results met seed maize husking quality standards, confirming the model realistically reflects kernel detachment and breakage characteristics during husking. The research result can provide reliable reference for refined modeling of seed maize ears and optimization design of husking devices.

Abstract:Aiming to address the problem of random performance fluctuations of centrifugal pump impellers caused by geometric uncertainties during design, manufacturing, and operation, a robust optimization design method of centrifugal pumps considering geometric uncertainties was proposed. A parameterized model of blade geometric uncertainty was constructed based on the Gaussian process-principal component analysis (GP-PCA) method, and the influence of blade geometric deviations on head and efficiency was quantified by using the non-intrusive polynomial chaos (NIPC) method. Combined with the radial basis function (RBF) neural network surrogate model and the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ), a robust optimization design of the centrifugal pump was carried out by taking the inlet blade angle, outlet blade angle, and blade wrap angle as design variables. The results showed that under random geometric deviations of ±3%, both the efficiency and head of the prototype impeller were decreased under design conditions, with the decrease being more pronounced under low flow rate conditions. After robustness optimization, the head and efficiency were significantly improved compared with the prototype pump, and the head variance and efficiency variance were greatly reduced. This improved the impeller's robustness to random geometric deviations while enhancing its hydraulic performance, and had certain guiding significance for the stable and reliable operation of the pump.

Abstract:Accurate acquisition of maize yield information was recognized as critical for formulating agricultural policies and supporting national economic development. Long short-term memory (LSTM) networks and Transformer models were employed for crop yield estimation due to their respective strengths in processing remote sensing time series data. To balance LSTM's capacity for capturing local temporal dependencies with the Transformer's efficiency in modeling global relationships, multi-source remote sensing parameters and maize yield were used as inputs to construct three yield estimation models: Transformer Encoder-LSTM (TFEL), Transformer-LSTM (TFL), and pure Transformer. A Bayesian optimization algorithm was applied to determine the optimal combinations of hidden layer size, learning rate, and other hyperparameters. These models were then used to estimate county-scale maize yields in the Taiyuan and Shangdang Basins of Shanxi Province. The Shapley additive explanations (SHAP) method was employed to quantify the contribution of each remote sensing feature within the hybrid models. The TFEL model demonstrated superior estimation accuracy (R2 was 0.72, P<0.01, RMSE was 756.43 kg/hm2, MAPE was 10.58%, NRMSE was 11.86%) compared with both the TFL model (R2 was 0.62, P<0.01, RMSE was 974.14 kg/hm2, MAPE was 13.50%, NRMSE was 15.14%) and the Transformer model (R2 was 0.53, P<0.01, RMSE was 1 028.76 kg/hm2, MAPE was 19.13%, NRMSE was 16.16%). The spatial distribution of estimated yields showed higher values in the northern and southern regions and lower values in the eastern and western regions. A strong linear relationship was observed between estimated and statistical yields, confirming the TFEL model's generalization capability. Results revealed that the two-band enhanced vegetation index (EVI2) and green chlorophyll vegetation index (GCVI) provided the greatest contributions to maize yield estimation in both the TFEL and TFL frameworks. Compared with the TFL model, the TFEL model focused more effectively on key remote sensing parameters, and consistently identified and quantified their contributions to yield estimation, thereby achieving higher estimation accuracy. In summary, the hybrid model based on Transformer and LSTM showed promising application potential in maize yield estimation, which can provide theoretical and method reference for regional crop yield assessment.

Abstract:In response to the challenges of low harvesting accuracy due to difficulties in the maturity recognition and pose estimation of dragon fruits in complex orchard environments, a lightweight and efficient OWD-YOLO real-time detection model was proposed based on YOLO 11n-pose to achieve precise and efficient harvesting of dragon fruits. Firstly, reparameterized convolution was introduced into the base model, combined with the C3K2 module, to enhance the model's ability to extract multi-scale features and fine-grained pose details of the dragon fruit. Secondly, by incorporating wavelet pooling and large kernel convolution attention mechanisms into the SPPF module, the model reduced interference from environmental factors such as lighting variations and background occlusion, thereby improving detection accuracy. Additionally, a DGECA attention mechanism was introduced into the backbone network to enhance the model's ability to recognize key features such as the fruit skin color and texture, improving the accuracy of maturity classification. Finally, a six-degree-of-freedom robotic arm harvesting platform based on the OWD-YOLO model was deployed in a complex orchard environment, with three-dimensional pose estimation of the dragon fruit achieved via a depth camera. Field experiments demonstrated that the OWD-YOLO achieved an object detection precision of 88.0%, a mean average precision of 92.7%, and a keypoint mean average precision of 93.3%, with absolute improvements of 5.0, 2.4, and 2.0 percentage points over the baseline, respectively. The average frame rate was 58.7 f/s, with a single fruit harvesting success rate of 86.0%, and an average harvesting time of 29.4 s. These results met the requirements for precise mechanized harvesting in complex orchard environments.

Abstract:During the intelligent harvesting of premium tea, existing object detection algorithms face challenges of insufficient detection precision for tender tea shoots and slow inference speed, making deployment on edge computing devices difficult. To address these issues, YOLO-RET, a lightweight tea shoot detection model was proposed based on an improved YOLO v8n architecture. By introducing a redesigned lightweight feature extraction module (RGCSPELAN module) and an enhanced multi-scale feature fusion pyramid structure (EMBSFPN), the model significantly enhanced feature extraction and fusion capabilities while substantially reducing model parameters. Additionally, a novel loss function, Focaler-IoU, was incorporated to address sample distribution imbalance, further improving detection accuracy and robustness. Experimental results demonstrated that compared with YOLO v5n, YOLO v8n, YOLO v8s, and YOLO v10n, YOLO-RET achieved precision improvements of 3.2, 3.0, 0.8, and 4.3 percentage points respectively, with corresponding mAP@0.5 improvements of 2.7, 3.2, 0.4, and 3.1 percentage points. Furthermore, YOLO-RET reduced the parameter count by 43% and computational complexity by 2.2 ×10? compared with that of the original YOLO v8n. The algorithm was successfully deployed on an ATK-DLRK3568 development board with quantization optimization, which not only lowered hardware resource requirements but also maintained high recognition accuracy while enhancing inference efficiency and reducing the computational burden on edge devices. The research result can provide an efficient and accurate solution for real-time object detection deployment on edge computing platforms.

Abstract:With the promotion of smart agriculture technology, there are issues such as low detection accuracy and resource limitations of real-time detection devices due to the small size of litchi fruits, severe occlusion, and complex background in natural environments. A lightweight litchi fruit detection method was proposed based on the improved YOLO v8s. Firstly, a lightweight network, MobileNetV3, was adopted as the backbone network to reduce the model parameters. On this basis, the convolutional local attention module (CLAM) was introduced to enhance the model's feature extraction ability for litchi fruits in natural environments both in channels and space. The concept of residual learning was also introduced to fuse the features before and after the attention module through weighted addition, ensuring the model's feature learning from the original image and improving the detection stability in complex environments. Secondly, some convolutional layers were replaced with depthwise separable convolutions, and multi-scale feature fusion was performed. Finally, to address the issues of small fruit size and severe occlusion, the αEIoU was used as the loss function to accelerate the convergence speed of the aspect ratio of the detection bounding box and reduce the missed detection rate of overlapping fruits. Experimental results indicated that the improved YOLO v8s achieved an accuracy rate of 91.75% and a detection precision of 79.07% on the experimental dataset, which were 17.29 and 14.75 percentage points higher than that of the original model, respectively. At the same time, the number of parameters was significantly reduced to 5.488×10?, a decrease of 50.7% compared with that of the original model. Compared with mainstream lightweight models such as YOLO v5s, EfficientNetV2, YOLO v7-Tiny, YOLO v9s, YOLO v10s, and YOLO 11s, this model demonstrated advantages in terms of precision, recall, and detection accuracy, providing a technical reference for litchi detection and yield estimation in modern orchard environments using mobile devices.

Abstract:Aiming to address the critical bottleneck whereby high-performance convolutional neural networks for apple leaf disease diagnosis are difficult to deploy efficiently on resource-constrained edge devices due to high computational complexity, a novel lesion-aware progressive compression framework (L-EAFP) was proposed. The methodology began by adapting a robust high-performance cross-domain model, EAFP-Med ST, to function as a teacher network. To facilitate high-fidelity knowledge migration to an ultra-lightweight ShuffleNetV2 student model, the framework introduced a lesion-weighted pruning algorithm that prioritized the preservation of pathological feature channels while rigorously eliminating redundancy. Concurrently, a multi-objective lesion feature anchoring distillation technique was applied to strictly align the student model's attentional focus on disease regions. Finally, an adaptive quantization mechanism was incorporated to further compress the model bit-width. Experimental validations performed on a comprehensive dataset of 9 263 apple leaf images demonstrated the framework's effectiveness. The results revealed that L-EAFP reduced the model parameter count from 3.157×10? to 3.500×10?, achieving a remarkable compression rate of 98.9%. Crucially, despite this drastic reduction, the compressed model retained a diagnostic accuracy of 98.58%. These findings indicated that the proposed lesion-aware strategy successfully achieved an optimal balance between model lightweighting and diagnostic performance fidelity, providing a highly effective technical solution for the deployment of advanced agricultural AI models on mobile and embedded edge terminals.

Abstract:An improved MobileNet-V2 convolutional neural network model was proposed, aiming at addressing the balance between computational resource consumption and recognition accuracy in apple leaf disease identification. Firstly, data augmentation techniques were employed to expand the diversity of image samples, thereby enhancing the model's generalization ability. Next, a transfer learning strategy was introduced, where pretraining and freezing some layer parameters reduced training time and computational resource consumption. The model further optimized feature extraction capability by incorporating group convolutions, a squeeze-and-excitation (SE) module from the channel attention mechanism, and weight pruning strategies, which enhanced both computational efficiency and accuracy. Experimental results demonstrated that, after optimization, the model achieved an accuracy of 99.1%, surpassing the value of the original MobileNet-V2 model by 5.9 percentage points, and outperforming traditional convolutional neural network models, like ResNet50, VGG16, and Xception by 4.6, 9.4, and 4.0 percentage points, respectively. The model's average precision was 98.9%, average recall was 98.75%, and F1 score was 98.82%, with only 4.06×10? parameters. In practical deployment, the model achieved fast and accurate disease detection, with an average inference time of 507 ms per image, demonstrating its practical value and potential for widespread application, offering insights for the development of smart agriculture.

Abstract:Aiming to address the challenge of accurately assessing the body condition of dairy cows in environments with motion blur, an integrated method for body condition scoring that incorporated an improved noise adaptive flow network model was proposed for the restoration of motion-blurred images. The simple channel attention module in NAFNet was replaced with a channel prior convolutional attention module, which dynamically allocated attention weights in both channel and spatial dimensions, refining and enhancing features to generate high-quality body condition feature maps. Additionally, the inverted residual mobile block module was embedded within the C2f module of the YOLO v8 model, maintaining the model's lightweight nature while aiding in the extraction of spatial relationships among body condition features. Furthermore, the large separable kernel attention module was integrated into the spatial pyramid pooling fusion (SPPF) module to achieve a larger effective receptive field, enhancing the model's ability to extract global features for body condition scoring. It can effectively reduce the probability of false detection and missing detection, and improve the detection accuracy. The proposed NAF-YOLO v8 method achieved precision, recall, and mean average precision of 80.7%, 75.3%, and 80.1%, respectively, on the motion-blurred test set, representing improvements of 8.2, 4.7, and 8.0 percentage points compared with models without image restoration. This integrated approach effectively reduced the impact of motion blur on the accuracy of body condition scoring, providing significant support for the intelligent management of dairy cattle.

Abstract:Aiming at the problems of poor sensitivity of feature learning, serious interference of complex redundant information in pasture background and difficulty in distinguishing high similarity individuals in cattle recognition task, a cattle face recognition method based on element-by-element dynamic fusion and adaptive loss function was proposed. Firstly, the StarNet architecture was used to redesign the feature extraction network, and an efficient network integrating multi-level features was constructed to extract key visual information and enhance the sensitivity of the model to subtle differences. Secondly, an element-by-element feature fusion module was proposed to screen low-dimensional features and perform weighted fusion on different feature maps to ensure that important features are retained, while irrelevant or redundant features were suppressed and removed. Finally, a dynamic adaptive ArcFace Loss was designed to adaptively adjust the angle boundary of ArcFace Loss, so as to enhance the model's ability to distinguish high similarity samples, and adaptively balance the feature distribution between different categories, which significantly improved the overall recognition performance. The algorithm was verified on the self-built data set and the public data set of 300 cattle. The experimental accuracy reached 88.42% and 86.67%, respectively, indicating the effectiveness and superiority of the algorithm. The running speed reached 15 f/s, and it was applied to complex environment testing. Both of them achieved high accuracy, which proved that the algorithm had high stability and robustness. Compared with other algorithms, the proposed algorithm had better recognition effect on cattle face recognition.

Abstract:Body measurement of goats is a fundamental aspect of livestock management, serving as a core indicator for quantifying production performance, analyzing genetic traits, improving the accuracy of breeding selection, and monitoring health status. It plays an irreplaceable role in enhancing economic efficiency and optimizing genetic breeding strategies. To address the challenges of measuring goat body dimensions under dynamic and multi-posture conditions, a non-contact, rapid, and accurate measurement method was proposed. An improved YOLO v8n-Pose deep learning model, named YOLO v8n-SK, was developed for keypoint detection of goats in the channel environment. This model specifically focused on accurately detecting the knee joints of goats to minimize the interference of movement on height measurements during motion. The model introduced a spatial-channel collaborative reconstruction convolution (ScConv) module to optimize the convolution-to-feature (C2f) structure, reducing parameter redundancy while enhancing feature extraction capability. CBAM attention mechanism was integrated to improve the model's robustness to interference, and an EIoU loss function was adopted to further optimize the training process and improve bounding box localization accuracy. From the extracted keypoints, body measurement-related features were obtained and combined with monocular depth estimation data. A nonlinear regression model was then employed to predict the goat body dimensions. Experimental results demonstrated that the proposed model exhibited excellent performance in detecting trunk and back keypoints, achieving a precision of 98.0%, recall of 98.1%, and mAP@50-95 of 95.8%. Meanwhile, the model complexity was significantly reduced, with parameters and FLOPs controlled at 6.7×10? and 7.9×10?, respectively. In the prediction of body measurement parameters, the mean absolute percentage errors (MAPE) for height, chest girth, abdominal girth, and body diagonal length were 2.408%, 1.731%, 1.340% and 2.519% respectively, demonstrating high measurement accuracy. Compared with traditional manual methods, the proposed approach significantly improved measurement efficiency and avoided stress responses in goats, enabling fast and accurate body measurement without disturbing the animals.

Abstract:Rice pest and disease information mostly originates from unstructured text. These texts contain densely nested entities, lengthy sentences, and complex grammatical structures. Because of this, current named entity recognition (NER) methods struggle to fully identify the relevant entities. To solve this problem, the AgriRoBERTa-BiLSTM-Man-CRF model was proposed. Firstly, a pre-trained corpus in the agricultural domain and a labeled dataset for rice pest and disease named entity recognition were constructed. This provided high-quality data for model training. Secondly, pre-training RoBERTa on agricultural texts was continued by using whole-word masking. This approach enabled the model to focus on the complete meaning of Chinese words and learn the specific language patterns found in texts about rice diseases and pests. Finally, Manhattan attention mechanism was introduced to capture sparse features in high-dimensional space by using L1-distance. This approach quantified feature differences while precisely focusing on critical contextual information, so as to improve the accuracy of entity boundary recognition. Experimental results showed that the proposed algorithm achieved an F1 score of 90.69%, with a precision of 87.87% and a recall of 93.71% for entity recognition. The F1 score was 7.8, 9.99, 1.8, 15.9 percentage points higher than that of four conventional models: BiLSTM-CRF, BiLSTM-Attention-CRF, BERT-BiLSTM-CRF and IDCNN-CRF. This enhanced performance enabled more effective recognition of diverse entities in rice pest and disease texts. This significant improvement indicated that the model can recognize various entities in rice pest and disease texts more effectively.

Abstract:Water content detection is essential for preventing mold growth and ensuring the proper storage and transportation of crops. However, existing portable detection methods often suffer from low accuracy and efficiency. To meet the demand for rapid crop water content measurement, a fast detection platform was proposed based on near-infrared (NIR) spectroscopy and a gradient boosting decision tree (GBDT) model. An off-axis Czerny-Turner (CT) optical structure was optimized by using Zemax, and a ZYNQ chip was employed to implement embedded decoding algorithms for a linear array CCD, with the system connected to a mobile phone. Savitzky-Golay (SG) smoothing and a piecewise direct standardization (PDS) data transfer algorithm were implemented on the mobile device. After spectral acquisition, the GBDT model running on the mobile phone was used to predict crop water content. Zemax simulations demonstrated that the off-axis optical design outperformed traditional spherical designs in terms of volume reduction and aberration control, maintaining a spectral resolution of 2 nm at the 950 nm edge of the image plane. SG smoothing improved the signal-to-noise ratio (SNR) from 18.11 dB to 35.10 dB. The GBDT-based prediction model achieved a coefficient of determination (R2) of 0.877 7 and a root mean square error of prediction (RMSEP) of 0.135 4% on the validation set. On the test set, the model attained an R2 of 0.861 7 and an RMSEP of 0.121 4%, with prediction absolute errors within 0.4%, meeting the requirements for rapid and efficient crop water content detection.

Abstract:Soil organic carbon (SOC) plays a crucial role in the global carbon cycle, and with the influence of global climate change and human activities, soil organic carbon density is constantly changing. A soil organic carbon density (SOCD) estimation method was proposed based on climate regionalization and a random forest model. It also developed a SOCD product with a long time series from the 1980s to 2020s and a spatial resolution of 1 km. The spatial heterogeneity and evolution patterns of SOCD in China from the 1980s to the 2020s were analyzed. Using Landsat series satellite images, elevation data, meteorological data, and measured SOCD data, a digital soil mapping method based on the random forest model was constructed to estimate the spatio-temporal distribution of 0~20 cm surface SOCD in China. The results showed that the prediction accuracy of the model considering climate zoning (R2=0.55, RMSE was 2.19 kg/m2) was better than that of the global model (R2=0.46, RMSE was 2.36 kg/m2). Meteorological factors had a significant impact on SOCD. Increasing temperature would accelerate the metabolic rate of microorganisms, promote the decomposition of soil organic matter, and lead to the increase of soil organic carbon release. Precipitation had a direct effect on soil water status, and suitable soil water content was conducive to SOC accumulation. At the same time, through verification with the measured data of the Heihe River basin, a high consistency was achieved between the model estimation results and the measured data (R2=0.69, RMSE was 2.01 kg/m2). The research result can provide a scientific basis for the accurate estimation and analysis of SOCD in China and it had important guiding significance for optimizing agricultural practice, improving soil carbon sink function, and realizing the national “double carbon” goal, which was conducive to promoting sustainable agricultural development and ecological environmental protection.

Abstract:Against the backdrop of intensifying global climate change and escalating food security challenges, accurately and timely estimating crop yields is important. Traditional vegetation indices based on reflectance spectra are difficult to capture the photosynthetic physiological state of crops in real time, while single?model approaches like Transformers and bi?directional long short?term memory network (BiLSTM) also exhibit limitations in extracting yield?related temporal features. Therefore, a hybrid deep learning yield estimation model that integrated data such as solar?induced chlorophyll fluorescence (SIF), actual evapotranspiration (Aet), precipitation (Ppt), and Palmer drought severity index (PDSI) was proposed. By leveraging the advantages of Transformer in extracting global dependencies and the BiLSTM in capturing local detail changes, a Transformer?BiLSTM wheat yield estimation model was constructed. The generalization ability and feature contribution of the model were also evaluated. Results indicated that the Transformer?BiLSTM hybrid model demonstrated superior fitting performance on the 2013—2019 county?level sample test dataset from Henan Province (R2=0.89, NRMSE was 8.18%, RPD was 2.90). All metrics outperformed those of both the single Transformer and BiLSTM models (R2 was increased by 0.04, NRMSE was decreased by 1.46 and 1.22 percentage points respectively, and RPD was improved from 2.46 and 2.53 to 2.90). In the 2020—2022 cross?temporal experiment of county?level data in Henan Province, the Transformer?BiLSTM hybrid model maintained high accuracy (R2=0.89, NRMSE was 8.44%, RPD was 2.77). Compared with single Transformer and BiLSTM models, R2 was improved by 0.05 and 0.07, respectively, while NRMSE was decreased by 1.92 and 1.56 percentage points. The RPD was risen from 2.25 and 2.33 to 2.77, demonstrating the model's robust temporal generalization capability. Further application of this hybrid model to Anhui Province, where yield distributions were more complex, exhibited robust performance (R2=0.87, NRMSE was 11.07%, RPD was 2.73). The R2 values were increased by 0.07 and 0.08, respectively, while the NRMSE was decreased by 2.33 and 3.10 percentage points. The RPD was improved from 2.25 and 2.13 to 2.73, confirming the strong regional generalization capability of the Transformer?BiLSTM hybrid model. Furthermore, aggregating the high?resolution yield distribution maps generated at the pixel scale to the county level for validation demonstrated high consistency with statistical yields (R2>0.8). Based on Shapley additive explanations (SHAP) feature importance analysis, the minimum temperatures from January to February and the SIF from March to June contributed most significantly to the model outputs, with SIF maintaining consistently high importance throughout the entire time series. Concurrently, meteorological factors such as PDSI, Ppt, and Aet during the winter wheat jointing to grain filling stage also exerted significant influence on yield prediction, indicating the model's ability to effectively capture synergistic interactions between crop growth processes and environmental factors.

Abstract:Under climate change scenarios, optimizing water and fertilizer management measures to mitigate the negative impacts of climate change on rice production in Southwest China can provide a theoretical basis for the sustainability of rice production in this region. The APSIM?ORYZA model was used to simulate and analyze the impacts of different irrigation modes (conventional flooding irrigation (CK) and controlled irrigation (I1, I2, I3, with lower limits of 50%, 70%, and 90% of the soil available moisture content and upper limits of the field capacity)) and combined application of organic and inorganic fertilizers (20% (F1), 40% (F2), 60% (F3) organic fertilizer substitution) on rice yield, soil organic carbon, and total nitrogen in Southwest China under the future SSP2?4.5 and SSP5?8.5 climate scenarios. The results showed that different future climate change scenarios had negative impacts on rice yield in Southwest China, but the yield reduction rate was decreased with the increase in irrigation water volume. Compared with the historical baseline period, in the 2050s under the SSP2?4.5 scenario, the rice yields of the I1, I2, I3, and CK treatments were decreased by 6.9%, 5.8%, 5.7%, and 5.3%, respectively;by the 2080s, the yield reduction rate of the CK treatment was increased to 8.7%, and those of the I1~I3 treatments were increased to 10.1%, 9.1%, and 8.9%, respectively. In addition, the rice yields of the controlled irrigation treatments were generally decreased in the order of I3, I2, I1, all of which were lower than that of the CK, and different irrigation modes had little impact on soil organic carbon. However, in the 2080s under the SSP5?8.5 scenario, the rice yield of the I3 treatment in Yunnan Province was equal to that of the flooding irrigation. With the increase in the organic fertilizer substitution ratio, the rice yield was firstly increased and then decreased. Compared with the conventional fertilization rate, under the SSP2?4.5 scenario, the rice yields of the F1 and F2 treatments were increased by 91 kg/hm2 and 27 kg/hm2, respectively, while that of the F3 treatment was decreased by 232 kg/hm2;under the SSP5?8.5 scenario, the rice yields of the F1 and F2 treatments were increased by 97 kg/hm2 and 43 kg/hm2, respectively, while that of the F3 treatment was decreased by 155 kg/hm2. Moreover, under different future climate change scenarios, organic fertilizer substitution could significantly increase the soil organic carbon and total nitrogen contents of rice in Southwest China. Under the future SSP2?4.5 (SSP5?8.5) climate scenario, the soil organic carbon contents of the F1, F2, and F3 treatments in the 2050s were 209 (213) kg/hm2, 347 (336) kg/hm2, 346 (355) kg/hm2 respectively, which were increased by 71 (80) kg/hm2, 209 (203) kg/hm2, 208 (222) kg/hm2, respectively compared with that of CK;the soil organic carbon contents of the F1, F2, and F3 treatments in the 2080s were 318 (323) kg/hm2, 554 (533) kg/hm2, 523 (541) kg/hm2, respectively, which were increased by 159 (167) kg/hm2, 395 (376) kg/hm2, 364 (385) kg/hm2, respectively compared with that of CK. Under the water?fertilizer coupling treatment, the F1I3 treatment had the best yield?increasing effect. Compared with the conventional water and fertilizer management, this combination increased the yield by 45~186 kg/hm2 under the SSP2?4.5 scenario and by 38~185 kg/hm2 under the SSP5?8.5 scenario. The research result showed that moderate coordinated water and fertilizer management can effectively increase rice yield and improve soil fertility in Southwest China under the background of future climate warming, promoting the sustainable development of agricultural production.

Abstract:The estimation of crop leaf nutrient content through hyperspectral remote sensing necessitates the identification of wavelength bands sensitive to nutrient deficiencies. Previous studies focused on correlating spectral data with nutrient content, neglecting the relationship between sensitive bands and phenotypic changes under nutrient stress, leading to less explainable band selection. Here, a spectral feature selection method was proposed, integrating a radiative transfer model with a post?explanation algorithm, specifically using the extreme gradient boosting model combined with Shapley additive explanations (SHAP) to enhance the interpretability of sensitive phenotypic selection. By linking leaf spectra with phenotypic changes, spectral features sensitive to nitrogen (N), phosphorus (P), and potassium (K) deficiencies in winter oilseed rape (Brassica napus L.) were identified. These selected spectral features were subsequently used to estimate leaf nitrogen concentration (LNC), leaf phosphorus concentration (LPC), and leaf potassium concentration (LKC), thereby validating the effectiveness of the band selection process. The results revealed that protein, with the highest SHAP value, was the key phenotypic indicator for N deficiency, followed by anthocyanin and carotenoid, therefore, prompting the selection of 43 bands between 400~720 nm and 1 140~2 250 nm for estimating LNC. For P deficiency, anthocyanin, with the highest SHAP value, was the most critical phenotype, followed by protein and chlorophyll, resulting in the selection of 50 bands between 400~810 nm and 1 140~2 250 nm for estimating LPC. Furthermore, carotenoid was identified as the most sensitive phenotype to K deficiency, with chlorophyll and equivalent water thickness also showing high SHAP values, leading to the selection of 32 bands between 450~810 nm and 1 140~2 480 nm for estimating LKC. The selected bands were used to construct random forest models to estimate LNC, LPC, and LKC. These models demonstrated high accuracy on the validation dataset. The highest performance for LNC, with a coefficient of determination (R2) of 0.86, a root mean square error (RMSE) of 0.37%, and a normalized root mean square error (NRMSE) of 0.11, followed by LPC (R2 of 0.83, RMSE of 0.04%, and NRMSE of 0.10), and LKC (R2 of 0.79, RMSE of 0.35%, and NRMSE of 0.12). Results underscored the significance of selecting sensitive wavelength bands for estimating leaf nutrient content and illuminated their relationship with phenotypes under N, P, and K deficiencies.

Abstract:Accurate soil moisture data are essential for regional water resource management and agricultural production. Thus, a comprehensive accuracy analysis and assessment is necessary to identify the soil moisture product with the optimal overall performance in the target region before applying the existing products, which can then serve as the foundational dataset for subsequent research. The accuracy of four soil moisture products (ERA5, GLDAS, SMCI, and SoMo) was evaluated based on the soil moisture data obtained from different observation sites across Loess Plateau of China. The evaluation included spatial accuracy assessment across different regions, quantitative performance estimation at annual and seasonal (dry/wet) scales, and accuracy appraisal under various land cover types. Based on the evaluation results above, a fusion of multiple products was conducted to enhance the overall accuracy of soil moisture estimation on Loess Plateau. The results showed that ERA5, GLDAS, and SoMo products tended to overestimate the actual soil moisture on the Loess Plateau, with Bias values between 0 m^3/m^3 and 0.3 m^3/m^3 at about 85% of the total sites investigated. The GLDAS product had the highest consistency with the observed multi-year average soil moisture, with Bias values of -0.15 m^3/m^3~0.15 m^3/m^3 at more than 96.5% of the total sites and with a median Bias of 0 m^3/m^3. At annual scale and during both dry and wet seasons, the GLDAS product showed the best overall accuracy, while ERA5 performed the worst. The SMCI and SoMo products had comparable performances. The GLDAS product performed better under the land cover types of grassland, forest, and irrigated cropland, while SoMo product performed best under non-irrigated cropland. Through combining the GLDAS and SoMo products, an integrated optimal soil moisture product was obtained for Loess Plateau, which could enhance the dataset's ability to reflect the dynamic changes in soil moisture on Loess Plateau while maintained a small error in values of soil moisture. The median values of the correlation coefficient (R), root mean square error (RMSE), bias (Bias), and unbiased root mean square error (ubRMSE) between the integrated soil moisture product and site measurements on Loess Plateau were 0.66, 0.06 m^3/m^3, 0.02 m^3/m^3, and 0.04 m^3/m^3, respectively. In general, the GLDAS product was recommended as the optimal soil moisture product for ecological, agricultural, and hydrological studies on Loess Plateau.

Abstract:Aiming at the selection of independent variables in geographically weighted regression (GWR) analysis, the construction of GWR model was explored by using soil heavy metal Cu content as the dependent variable and soil hyperspectral data as the independent variables. The best spectral transformation that characterized the relationship between the soil heavy metal Cu content and the spectral reflectance in the study area was determined, throughing the correlation analysis of the spectral reflectance of the original soil and its eight transformation methods. The candidate variable subsets was established by using the ordinary least squares method, and the GWR model was constructed by using the regression analysis method. The advantages of the GWR model, as well as its prediction accuracy, were compared with that of the GWR model constructed by traditional methods. The results showed that the correlation coefficient between the logarithmic differential transformation spectrum and the soil heavy metal Cu content was from -0.626 to 0.618, which could best represent the relationship characteristics between the soil spectrum and the soil heavy metal Cu content in the study area, and it was the best spectral transformation method for the study area. The results of the regression analysis indicated that the optimal GWR prediction model for the soil heavy metal Cu content in the study area had four independent variables combined of 2 308 nm, 1 400 nm, 2 197 nm, and 2 138 nm wavelengths, which had the smallest Akaike information criterion (AICc), the largest determination coefficient (R^2) and adjust coefficient of determination (R^2_adj). Compared with the GWR model constructed by the traditional method, the GWR model constructed by the regression analysis method was more concise with stronger model robustness and higher prediction accuracy.

Abstract:The increasing demand for inspection and precise operations in livestock housing has imposed high requirements on navigation methods for autonomous mobile robots. However, existing navigation methods are often sensitive to lighting conditions, susceptible to environmental disturbances, and expensive, limiting their applicability. To address this challenge, a multimodal sensor fusion framework for robotic navigation was proposed which fused a near-infrared camera and a 2D LiDAR. By jointly optimizing the geometric features of LiDAR and the semantic landmark information from the near-infrared camera, the framework overcame navigation bottlenecks in complex environments. The system leveraged the wide-range robust localization capability of LiDAR and the high-precision assistance of the near-infrared camera to achieve efficient navigation. The system leveraged LiDAR to provide robust localization in large-scale environments and utilized a near-infrared camera to enhance navigation accuracy. By identifying and extracting feeder rib landmarks from near-infrared images, the system fused this landmark information with data from wheel odometry, LiDAR, and an inertial measurement unit (IMU). The Cartographer algorithm was employed to construct environmental maps, enabling high-precision localization. The pose information of target locations was systematically recorded to support global path planning, while local path planning incorporated the Trajectory Rollout and Dynamic Window Approaches algorithms to further ensure autonomous navigation precision. Field tests were conducted in stacked cage poultry houses. Results demonstrated that the standard deviation of landmark positioning accuracy was less than 2 cm. In the hall, at speeds below 0.5 m/s, the standard deviation of single-point positioning error was less than 2 cm, and the standard deviation of yaw angle error was less than 1°. In the aisles, at speeds below 0.3 m/s, the standard deviation of target point positioning error was no more than 5.04 cm, and the standard deviation of yaw angle error was approximately 1°. Field test results demonstrated that the system achieved centimeter-level positioning accuracy in complex livestock housing environments, validating its applicability for inspection and precision operation tasks.

Abstract:The online detection technology for rumen carbon emissions of individual dairy cows is of great significance for promoting emissions reduction and carbon mitigation in animal husbandry, and achieving sustainable high-quality development. In order to solve the problems of cumbersome, inefficient and a lack of suitable equipment for detecting individual rumen carbon emissions in China's dairy farming, an online detecting device was designed. Additionally, an SVM-based model for predicting daily methane emissions from individual cows was proposed. The equipment was mainly controlled by STM32 microprocessor, integrating RFID/methane/carbon dioxide sensors to collect data, and using LoRa/4G dual-mode communication technology to transmit data. Based on Qt Creator software/cloud platform, data was analyzed and displayed to achieve local/remote integrated monitoring, transmission, analysis and visualization of individual carbon emissions data of cows. To obtain the daily carbon emissions of individual cows, an SVM-based prediction model was constructed by using the background concentration deduction and time point alignment reconstruction methods. Field tests were conducted on 25 Holstein cows at a certain ranch in Beijing. Compared with the CO2 balance method, the SVM regression prediction model showed MAE averages of 511.13 g/d, 58.16 g/d, and 2 202.10 g/d, and MRE averages of 3.78%, 14.34%, and 8.55% for the average cattle emissions of carbon dioxide, methane, and carbon, respectively. Moreover, when the fan was turned on, the SVM model predicted that the average carbon dioxide emissions, methane emissions, and carbon emission fluctuations of cattle individuals were 10.85 g/d, 8.15 g/d, and 255.35 g/d, respectively. These fluctuations were significantly smaller than those derived from the carbon dioxide balance method. This study can achieve real-time, rapid, and accurate measurement of methane and carbon dioxide emissions from individual cow rumens. It can provide technical support for the precise accounting of carbon emissions in the agricultural and livestock sectors.

Abstract:It was designed to clarify the mechanical behaviour and crack propagation mechanisms of deep-ridged walnuts under compression shell-breaking, and improve kernel integrity rate by directly observing and quantifying crack evolution. A dual-view synchronous high-speed imaging platform integrating force-displacement acquisition was constructed, and a pixel-level crack segmentation pipeline based on YOLO v8m-seg was developed. In comparison with YOLO v8n-seg and YOLO 11n-seg, the proposed model was found to achieve a precision of 86.3% and an mAP50 of 82.3% for the crack class. An annotated dataset comprising 3,600 pairs of front- and back-view images was established, and an imaging magnification calibration coefficient was introduced to unify crack measurements between the two views;on this basis, total crack number, total crack area ratio, and crack quadrant count were defined as three reproducible crack descriptors to describe the spatiotemporal evolution of cracks. A two-factor full factorial design was adopted, with equivalent diameter (30~42 mm, six levels) and compression direction (transverse, ridge, longitudinal) as independent variables, and compression strokes were set to 6,7,8 mm according to walnut size. Two-way ANOVA results showed that equivalent diameter and compression direction had highly significant effects (p≤0.01) on mechanical response, crack features, and kernel integrity rate. Typical force-displacement curves were observed to be divided into three stages, consisting of an elastic rising stage, a stage in which primary cracks were initiated and were accompanied by a sharp force drop, and a subsequent stage of stable crack growth. For all test groups, the maximum compression force was found to range from 190 N to 280 N and the total work was found to range from 620 J to 1,000 J;transverse compression was associated with lower energy and more stable shell opening than the other directions. During mid-stage loading, the crack number was observed to reach 4~7 and then to merge into 2~3 cracks toward the end of compression, while the final crack area ratio was found to stabilize at 10%~20%. When the crack area ratio reached a peak of 29% and then was reduced to 18% as compression progressed, this range was shown to coincide with the best kernel integrity rate performance. Walnuts with an equivalent diameter of 34~36 mm compressed along the transverse direction were demonstrated to perform best, combining lower energy consumption, a stable shell-breaking mode, and a maximum kernel integrity rate of 73%. The proposed vision-based segmentation framework and crack descriptors were shown to provide an effective basis for in-situ fracture analysis of biological shells under load and to offer practical guidance for the design and optimization of low-damage, controllable shell-breaking equipment.

Abstract:Aiming to address the problems of low energy replenishment efficiency and insufficient braking reliability in monorail transport systems for mountainous orchards, an electric monorail transporter integrating autonomous power pickup and high-reliability braking was proposed. Based on typical orchard operating conditions, an overall system configuration was developed, including a wedge-type power pickup device, a positioning power supply unit, a dual drum brake, and a turnout mechanism. The cooperative working principles of the key components were analyzed. Multibody dynamic models of the dual drum brake and a conventional single drum brake were established in ADAMS to evaluate emergency braking performance, and a thermo-structural coupled analysis was conducted in ANSYS to investigate temperature and stress under emergency and continuous braking conditions. Simulation results indicated that the dual drum brake achieved a full stop within 1.3 s, significantly shorter than that of the single drum brake. Experimental results showed that the transporter can stably park on a 45° half-slope, with an average turnout switching time of 9.327 s. Autonomous charging achieved high positioning accuracy, with fine positioning errors below 2 mm. The charging station exhibited a charging efficiency of 92% and stable output performance. The proposed system effectively improved both energy replenishment efficiency and braking safety, providing technical support for practical applications.

Abstract:A rigid flexible hybrid cable driven parallel mechanism (CDPM) constrained by 3 RPS branches was proposed to overcome the limitation of unidirectional force characteristic in traditional cabledriven parallel mechanisms. In this configuration, the direction of cable branches can be flexibly adjusted according to the desired workspace requirements. The motion principle of the mechanism was firstly described, followed by the design of its three-dimensional structure. Both kinematic and static models were then systematically established. The correctness of the driving cable force was confirmed through comparative analysis between theoretical computations and simulation outcomes. Subsequently, based on the developed mechanical model, the force closure reachable workspace of the mechanism was calculated, and the main structural parameters that affected the workspace were investigated in depth.Furthermore, the Jacobian matrix of the mechanism was derived. And the performance indicators such as singular configuration, dexterity, and stiffness of the mechanism were analyzed by using the determinant of the Jacobian matrix, condition number, and two norm of the stiffness matrix as indicators. The findings established a solid theoretical basis for further development and practical application of the proposed mechanism.