Abstract:Navigation solutions based on laser simultaneous localization and mapping (SLAM) struggle to overcome the progressive accumulation of positioning errors and localization jumps caused by crop canopy occlusion. This issue was particularly severe in asparagus greenhouse environments, where the occlusion from asparagus plants significantly compromised the robot's accuracy. To address this challenge, an autonomous navigation method for a facility spraying robot was proposed, which integrated an improved Cartographer algorithm with ultrasonic tag positioning technology. The method obtained incremental displacement, velocity, and attitude estimations through inertial measurement unit (IMU) pre-integration and introduced a sliding window optimization strategy to refine state estimations of both current and historical data. A dynamic trigger mechanism was utilized to complete the recording of the navigation path, enabling autonomous operation. Field experiments involving robot localization trajectory mapping and navigation tasks were conducted in a real-world asparagus greenhouse. The results indicated that during positioning tests at speeds of 0.2 m/s, 0.4 m/s, and 0.6 m/s, the mean absolute pose errors between the proposed method's trajectory and the ground truth were 0.249 m, 0.324 m, and 0.408 m, respectively. Compared with the standard Cartographer method, these results represented a reduction in average positioning error by 7.4%, 34.5%, and 30.6%. During navigation tasks at the same speeds, the robot's maximum lateral deviation was 5.3 cm, and at 0.6 m/s, the average lateral positioning error was 2.504 cm. The positioning and navigation accuracy of the robot met the autonomous operational requirements for a facility spraying robot, providing an effective solution for autonomous operations in greenhouse environments.

Abstract:In order to solve the problems of difficult on-site planning of the hybrid cotton picker's operation paths in the cotton field environment in Xinjiang, optimize the operation energy consumption of the hybrid cotton picker under different driving angles in each cotton field, as well as the waste of energy consumption caused by the differentiation of their transfer sequences between cotton fields, a method of operation path planning based on energy consumption model and Harris hawks optimization algorithm was investigated. The cotton field model that met the actual operation requirements was constructed and calibrated by combining the easily accessible high-precision electronic map with the harvesting requirements of the cotton picker at the boundary of the cotton field. The dynamic characteristics and machine parameters of the hybrid cotton picker were analyzed, and the quantitative relationship between the turning mode and energy consumption was considered to construct the operation energy consumption of the hybrid cotton picker in the cotton field with the operation direction angle as the only optimization target. The greedy heuristic strategy based on the distance matrix and the nonlinear energy attenuation factor were introduced to solve the original defects of the Harris hawks optimization algorithm in solving the transfer sequence, and the energy waste caused by the redundant paths in the transfer sequence was reasonably eliminated. Experimental results showed that the hybrid cotton picker's operating energy consumption was reduced by 42.80%, the path was reduced by 51.13%, and the production efficiency was increased by 44.02% after the energy consumption model was added;the improved HHO algorithm reduced the path length of the transition sequence by 6.13%, which verified the good effect of the proposed operation path planning method.

Abstract:Aiming at the problems of low straw clearance rate and poor anti-clogging performance of passive straw cleaning strip tillage machines under corn straw surface mulching conditions, an active straw cleaning strip tillage unit with contour-following functionality was designed and its core component was developed—-the power-driven straw clearance device. Through theoretical analysis and randomized block experiments, the structural and operational parameters affecting the performance of the power-driven straw clearance device were determined. Using the initial cutting angle of the blade, pitch, and cutter roll speed as experimental factors, and straw clearance rate and power consumption as evaluation metrics, a parameter combination optimization was conducted by integrating quadratic regression orthogonal rotation central composite experiments with discrete element simulations. The results indicated that all factors significantly influenced the evaluation metrics. When the initial cutting angle of the blade was 45°~55°, the pitch was 190 mm, 200 mm, 210 mm, and the cutter roll speed was 370 r/min, the straw clearance rate exceeded 88% while the power consumption was below 750 W. Based on the parameter combination optimization results, a prototype was manufactured and field validation tests were conducted. The findings showed that with a pitch of 185~220 mm, an initial cutting angle of 50°, and a cutter roll speed of 370 r/min, the straw clearance rate was no less than 85.2% and the power consumption did not exceed 750 W. The relative error between field validation tests and simulation optimization results remained within the acceptable range. These findings can provide a reference for the design and optimization of active strip tillage machines.

Abstract:Aiming to address the problem of plastic-film lifting caused by strong winds after mulching on high ridges in hilly areas, a covering soil on plastic-film device was designed according to the agronomic requirements of high ridge mulching. Based on the generating principle of the Archimedean spiral line on a truncated cone, a spiral blade of the overlying drum was designed. The dynamics of soil movement within the drum were analyzed to determine the main factors affecting the soil pile mass and the soil pile convergence value. Using EDEM, a simulation model of the interaction between the covering device and soil was established. Taking the spiral end angle, forward velocity, and circumferential angle corresponding to the soil discharge port as experimental factors, the soil pile mass and soil convergence value as evaluation indices, single-factor and three-factor three-level orthogonal experiments were carried out to determine the factor ranges and establish a response surface model. The orthogonal test results showed that the spiral end angle and forward velocity had a highly significant effect on the soil pile mass, while the forward velocity and circumferential angle corresponding to the soil discharge port had a highly significant effect on the soil pile convergence value. The optimal operating parameters were determined as follows: spiral end angle was 57.44°, forward velocity was 3.82 km/h, and circumferential angle corresponding to the soil discharge port was 32.02°. A prototype was manufactured based on the optimized parameters, and field experiment was conducted. The field experiment results showed that the soil pile mass on the film was 545.2 g, the lighting-struck ratio of the plastic-film was 72.25%, the soil wide on film side was 90.2 mm, and the soil depth on film side was 35.7 mm, all meeting the agronomic requirements of film mulching and covering soil on plastic-film for high ridge in hilly areas.

Abstract:Considering the problem that the inconsistent furrow depths generated by soil contact parts plugging and double disc openers of existing precision planter for rapeseed in the furrows, resulting in uneven emergence, a pneumatic shooting seeder for rapeseed with seed extraction with negative pressure suction and positive pressure high-speed seeding was designed. The key parameter ranges of nozzle radius, accelerating pipe radius, accelerating pipe length and distance between accelerating pipe and nozzle were determined. The effects of seeder structure and seed velocity on the total pressure loss of flow field were also analyzed. The dynamic model and the corresponding collision equations for the second collision between seeds and seeder was established. The influence of the vortex area in the mixing chamber of the seeder on the trajectory of seeds after their second collision was analyzed. A three-factor and three-level orthogonal experimental study was conducted using CFD-DEM coupled simulation. Regression models were established for the ejection coefficient, seed average shooting velocity, and vortex area ratio in the mixing chamber. It was determined that when the nozzle radius was 6.9 mm, distance between accelerating pipe and nozzle was 13.8 mm, and accelerating pipe radius of 10.36 mm were the optimal structural parameters, The injection coefficient was 0.314, the seed average shooting velocity was 20.92 m/s, and the vortex area ratio in the mixing chamber was 21.15%. It was verified by bench test that there was no seed retention phenomenon under the optimal structural parameters of the seeder. And it also determined the following optimal operating conditions: with soil moisture contents of 30% and 45%, the fan speed was set to be 35 m/s and the height of the seeder above the soil surface was 150 mm;with soil moisture contents of 35% and 40%, the fan speed was set to be 40 m/s and the seeder height was maintained at 150 mm. Field experiment results showed that when the soil water content was 38.5%, the average probability of each line of rapeseed distributed within 120 mm seed row width was 80.12%, and the plant density was 66 plants/m2, which met the requirements of precision sowing of rapeseed.

Abstract:In response to the demand for dual-channel intelligent screening of sugarcane seeds in the intelligent sugarcane seed preparation production line, an industrial-grade precise sugarcane screening control system was designed. It adopted a parallel architecture to simultaneously achieve real-time visual quality inspection and rejection control of dual-channel sugarcane seeds. Each channel formed feedback by real-time detection of the conveyor belt speed, and the PID algorithm was used to control the stepping motor to achieve closed-loop speed regulation, ensuring the stability of the conveyor belt speed. A delay circuit was constructed by using dual NE555 chips to ensure that the rejection action was precisely synchronized with the position of the inferior species. The system used a 16-channel RS-485 transistor pulse driver as signal driver for 16-channel delay circuit, efficiently connecting the intelligent quality inspection system for sugarcane seeds, the 16-channel delay circuit and rejection actuator. By configuring the station number of 16-channel RS-485 transistor pulse driver, independent addressing can be achieved, featuring good modularization, scalability and engineering applicability. The test results showed that the dual-channel system had high recognition and screening accuracy at different speeds, and its operating efficiency was increased with the increase of speed, verifying its good stability and practicability. In the performance test of the single-channel system, when the conveyor belt speed was 0.20 m/s, the interval between sugarcane seeds was 10 cm, and the proportion of inferior species was 40%, the system screening accuracy rate reached 98%, and filtering took 30 s, which was equivalent to a single-channel screening efficiency of approximately 6,000 segments per hour, with an efficiency increase of 100.5%. The multi-factor orthogonal experiments showed that the average screening accuracy model was extremely significant. Both the conveyor belt speed and the sugarcane seed interval had extremely significant effects on the average screening accuracy, and the proportion of inferior seeds showed a significant influence. After optimizing the experimental parameters, when the conveyor belt speed was 0.20 m/s, the spacing between sugarcane seeds was 11.69 cm, and the proportion of inferior seeds was 24.60%, the screening accuracy rate can reach 98.89%, which can meet the requirements of actual production.

Abstract:Aiming to address the issues of high cost and low operational efficiency associated with manual filling of citrus seedling pots, an automatic bag?opening and pot?filling machine for citrus seedling bags was designed based on multi?mechanism cooperation. The design included vacuum adsorption for automatic bag opening, a manipulator for holding the bag open, and vibration?assisted filling. The structures and key parameters of components such as the bag storage mechanism, bag?taking and opening device, bag?holding manipulator, and filling mechanism were determined. Through orthogonal experiments on bag?taking/opening and filling consistency, the main factors influencing the success rate of bag opening and filling uniformity were identified. The optimal parameter combination was as follows: type?I soft silicone suction cup, adsorption time of 2 s, adsorption pressure of -90 kPa, nutrient soil moisture content of 8%~13%, remaining nutrient soil in the hopper of 40%~70%, and vibrator air pressure of 0.3 MPa. Under these conditions, the average filling amount per seedling bag was 1 800 g, the average time for bag opening and filling was 30 s, and the success rate of bag opening and filling reached 88.5%. This achieved an automatic bag?opening and filling capacity of 120 bags per hour, with high bag?opening efficiency and uniform filling, meeting the requirements for bag?opening and filling operations in citrus container seedling cultivation. The research results can provide theoretical basis and design references for the future development and application of automated machinery for citrus seedling cultivation.

Abstract:Aiming at the agronomic demand for synergistic subsoiling and deep fertilization during the intertillage period in soybean?corn strip intercropping, a subsoiling pneumatic deep fertilization device was designed. By integrating a pneumatic fertilizer delivery system behind the subsoiling shovel, simultaneous soil loosening and deep fertilizer application were achieved. The computational fluid dynamics and discrete element method (CFD?DEM) coupling simulation was used to optimize the structure of air?fertilizer mixing chamber. With the throat contraction depth, throat contraction angle, and transition pipe half?cone angle as influencing factors, and the airflow velocity and fertilizer particle velocity at the fertilizer outlet as test indicators, a regression model between the test indicators and influencing factors was established. The optimized parameters were as follows: throat contraction depth of 28.08 mm, throat contraction angle of 52.1°, and transition pipe half?cone angle of 22.3°. The model predicted an outlet airflow velocity of 16.59 m/s and a fertilizer particle velocity of 2.69 m/s, with a simulation verification error of less than 1.5%. Field test results showed that the designed subsoiling pneumatic deep fertilization device could achieve precise and stable deep fertilizer application, with an average fertilization deviation of 4.63%. The fertilizer discharge consistency among all pipelines was good without clogging. Both subsoiling and fertilization depths met agronomic requirements. The stability coefficients of subsoiling depth for soybean and corn rows were above 96%, and those for fertilization depth were above 95%. The research result can provide an effective equipment solution and theoretical basis for efficient and precise subsoiling and fertilization in soybean?corn intercropping systems.

Abstract:During the flow of granular fertilizers, they tended to obstruct each other, and the external wheel?type fertilizer distributor further caused uneven distribution, making it challenging to accurately measure the fertilizer flow. To address this issue, an airflow?assisted photoelectric detection method that enabled more accurate measurement of granular fertilizer flow was proposed. An airflow?assisted photoelectric granular fertilizer flow sensor was designed, utilizing positive pressure airflow to assist in the reconstruction of the fertilizer flow pattern. A detection model was developed by integrating simulation and static experiments. The model incorporated fertilizer flow volume as an intermediate variable and calculated the flow rate through an area element integration algorithm. Using diammonium phosphate as the test material, static experiments revealed a significant linear relationship between the equivalent diameter of the fertilizer flow and the sensor?s response voltage. The correction coefficient?s multiple regression model was determined through calibration experiments, thus constructing the fertilizer flow detection model. To validate the effectiveness of the method, a dedicated test platform was set up for verification experiments. The test results showed that under airflow?assisted conditions, the U?V?Q detection model achieved an average absolute percentage error (MAPE) of no more than 4.55%, and a root mean square error (RMSE) of no more than 3.82 g/s. This demonstrated that the airflow?assisted photoelectric detection method for granular fertilizer flow had high detection accuracy and good stability. Compared with the U?V?Q detection model without the airflow assistance device, the MAPE was reduced by 2.44%, and the MSD was reduced by 0.74%. Furthermore, with the airflow assistance device, the MAPE of the U?V?Q detection model was 9.03% lower than that of the U?Q detection model. The research results indicated that using airflow assistance alone or the U?V?Q detection model alone did not achieve optimal detection performance. However, combining airflow assistance technology with the U?V?Q detection model significantly improved both the accuracy and stability of granular fertilizer flow detection, providing insights for real?time flow detection in fertilization machines, which was of great significance for the closed?loop control of precision variable?rate fertilization systems.

Abstract:Aiming to address issues such as low efficiency, high costs, and the lack of grafting equipment meeting domestic production requirements in the market, a fully automatic seedling?off?tray grafting machine was designed. It can automatically complete operations, including seedling loading, scion pre?cutting, seedling clamping, simultaneous multi?seedling cutting, scion alignment and clamping, and seedling replanting. The structure and working principle of the grafting machine were detailed, with key mechanisms and operational parameters designed for seedling loading, transportation, pre?cutting, clamping, cutting, and clamp application. To determine optimal parameters and enhance grafting success rates, tomato, peppers, and eggplants as test subjects. The factors selected for investigation were the height of the rootstock after pre?cutting, the descent height of the scion during alignment, the height of the scion, and the descent speed of the scion during alignment. Grafting success rate served as the evaluation metric. A four?factor, three?level response surface experiment was conducted, revealing that the descent height and descent speed of the scion during alignment were significant factors affecting grafting success rate. Design?Expert 13 software was employed for variance analysis, response surface analysis, parameter optimization, and experimental validation to obtain the optimal parameter combination. Experimental results indicated that when the pre?cut rootstock height was 70~80 mm, scion descent height was 75 mm, scion height was 140~160 mm, and scion descent speed was 0.2 m/s, the average grafting success rate reached 94.6% with an average grafting efficiency of 1 110 plants per hour, meeting the demands for large?scale grafting operations of solanaceous vegetable seedlings.

Abstract:Aiming to address typical operational failures such as belt breakage and pickup tooth detachment in film?recovery machines, a fault monitoring system for the pickup mechanism was designed. This system employed an STM32 microcontroller as the data acquisition core and utilized the Qt framework to develop the upper?level monitoring interface. To enhance field interference resistance, RS485 bus communication was adopted for data transmission. Considering the circumferential arrangement of the pick?up teeth, the film?stripping roller speed and the time interval between adjacent teeth passing sensors were selected as key monitoring parameters. Through screening sample data under normal operating conditions and comparing polynomial, exponential, and other function fits, an optimal second?order exponential decay function model was established for the relationship between rotational speed and theoretical time interval values under normal conditions. The natural exponential function was introduced to calculate the relative deviation between monitored and theoretical values, enhancing data feature differentiation. Normalized rotational speed, time interval values, and their relative deviations were used as feature vectors input into a hyperparameter?optimized multilayer perceptron (MLP) for classification. The Softmax function outputted fault states, achieving an overall model training recognition accuracy of 98.65%. Model comparison and bench validation tests demonstrated that the MLP outperformed support vector machines and random forest models. Bench validation achieved an average system recognition accuracy of 96.13%. These findings can provide theoretical and technical references for developing fault monitoring systems for gear?belt residual film recovery machines.

Abstract:Aiming to address the issues of low operational success rate, high ginger tuber damage rate, and frequent clogging at the gathering and cutting points in the clamping conveying devices of existing ginger combine harvesters, an adaptive flexible clamping conveying device was designed. Using Anqiu organic ginger as the research subject, the mechanical parameters of the ginger stems were firstly determined through physical property tests. A stem rheological model was constructed based on the Burgers viscoelastic model, and creep curves under different loads and at different stem locations were fitted, clarifying the intrinsic relationships among clamping force, conveyance loss, and ginger tuber damage. Design?Expert software was employed for experimental design and data analysis of key operational parameters (elastic coefficient of the floating spring support, inclination angle of the conveying device, chain speed). The response surface methodology was used to reveal the influence patterns of various factors and their interactions on harvesting success rate and damage rate. Combined with ginger cultivation agronomy, the key components of the device were structurally optimized. Field validation test results showed that when the elastic coefficient of the floating spring support was 9.08 N/mm, the inclination angle was 29.95°, and the chain speed was 200.5 mm/s, the device achieved a harvesting success rate of 94.80% and a ginger tuber damage rate of 4.9%. The optimized parameter combination was highly consistent with the regression model predictions, significantly improving the reliability and operational quality of the clamping and conveying process. The research result can provide a theoretical basis and practical reference for the design and optimization of key components in ginger combine harvesters.

Abstract:The current method for loading material onto vehicles in silage harvesters, which primarily involves manually controlling the rotation of the throwing arm, is labor-intensive and has high operational requirements. This method not only affects the harvesting efficiency but also easily causes losses of silage. A detection method for determining whether silage fell into the trailer hopper was proposed. Firstly, an improved YOLO v8n-pose model was constructed. By introducing the lightweight coordinate attention (LCA), DynamicConv, and Deformable Convolution v4 (DCNv4), the detection accuracy of the trailer hopper corners and the falling points of silage was improved. After coordinate transformation, combined with the convex hull algorithm and cross product method, it was determined whether the silage fell into the trailer hopper. Experiments proved that the improved YOLO v8n-Pose model achieved a mAP50:95 of 95.1% in the loose state of the silage flow, an increase of 5.9 percentage points compared with that of the original model, and a mAP50:95 of 95.0% in the normal state of the silage flow, an increase of 3.3 percentage points compared with that of the original model. The improved model demonstrated higher detection accuracy and stability in different states of the silage flow, with a significant enhancement in adaptability to abnormal working conditions with drastic shape changes, which provided a solid visual foundation for future adaptive throwing control.

Abstract:The hammer is a key component and vulnerable part of the forage crusher. Wear and blunting of the hammer not only reduce the operational efficiency of the crusher, increasing power consumption, but also degrade the processing quality of the forage. Moreover, uneven wear of the hammer can affect the balance and vibration of the crusher rotor, thereby impacting the service life of the entire machine. The mole claw toe was selected as the bionic prototype to design a novel bionic hammer aimed at improving wear resistance without compromising material crushing performance. Based on the contour equation of the third toe tip of the mole claw toe, the contour curve was fitted, and the bionic hammer was designed using the principle of geometric similarity. A bonded particle model (BPM) was established for material crushing, and the CFD-DEM coupling method was employed to simulate the motion patterns of airflow and material in the crushing chamber during the crushing process. Meanwhile, the Archard wear model was introduced to calculate the wear amount of the hammer. The results showed that within the same duration, the maximum wear amount of the original rectangular hammer was 1.02×10^{-5} mm, while that of the mole claw toe bionic hammer was 9.51×10^{-6} mm, representing a 6.76% reduction in maximum wear compared with the prototype hammer. Additionally, the number of bond breakages in the material after crushing by the bionic hammer was increased by 11.43% compared with that of the rectangular hammer. The results demonstrated that the mole claw toe bionic hammer exhibited improvements in both wear resistance and crushing performance. The research result can provide a methodological reference for the design and performance enhancement of hammer-type forage processing machinery.

Abstract:Aiming at the problems of insufficient control accuracy and poor stability of the orchard sprayer in the small flow spraying interval, a variable control system for precision spraying of fruit trees was designed based on the improved fuzzy proportional-integral-derivative (PID) algorithm. The system controlled the rotational speed of the DC brushless water pump through the pulse width modulation (PWM) signal to control the flow rate accurately. The flow rate relationship of the DC brushless water pump under different duty cycles was obtained through the test, and the segment fitting was performed according to the trend of the data to obtain the accurate flow rate fitting formula. Then the fuzzy control was combined with the traditional PID control algorithm, and the Gaussian function and asymmetric trigonometric function were used to design the affiliation function, which achieved the improvement of the fuzzy PID algorithm. In order to verify the effectiveness of the algorithm, the modelling simulation comparison between the traditional PID, fuzzy control and the present algorithm was carried out, and the experimental results showed that the present algorithm was better than the other algorithms in terms of rise time, response overshooting amount, and steady state error. At the same time, the comparison experiments of flow control accuracy in non-motion state, dynamic flow following in motion state and the test of droplet deposition rate were set up. The experimental results showed that the spray volume error of this algorithm was controlled within 2%;when the spray volume changed dynamically in the range of 0.5~3 L/min, the average overshooting amount was 2.43%, the average adjustment time was 0.52 s, and the average droplet deposition rate was 76.89%. The research result can provide technical support for the development and application of smart orchards.

Abstract:Sandalwood seedlings rely on companion plants during their growth, which are often interplanted with other crops, resulting in a complex growth environment that makes accurate detection challenging, thereby affecting the effective assessment of sandalwood productivity. To address this issue, a lightweight detection algorithm, (YOLO v8n improved for sandalwood plant seedlings detection,YOLO?SPS), was proposed based on multispectral image fusion and an improved YOLO v8n. The SwinFusion model was utilized to fuse near?infrared and visible light remote sensing images of sandalwood seedlings, enhancing their texture and color features. The cross stage partial network fusion_spatial and channel reconstruction convolution module, which combined spatial and channel reconstruction, was introduced into the backbone network of the YOLO v8n model to improve feature extraction capabilities. The partial self?attention (PSA) mechanism was incorporated into the neck network to optimize global perception and reduce computational costs. Finally, Shape?IoU was adopted as the bounding box loss function to further enhance detection accuracy. Experimental results demonstrated that the YOLO?SPS model achieved precision, recall, and mean average precision of 92.8%, 93.2%, and 95.9%, respectively, significantly outperforming the original YOLO v8n model and surpassing five mainstream detection models, including YOLO v5s, YOLO v6n, YOLO v7?tiny, YOLO v9?t, and YOLO v10n. The research result can provide effective technical support for the precise monitoring of sandalwood seedlings during their early growth stages.

Abstract:Mountainous terrain shadows in remote sensing satellite imagery typically exhibit irregular morphologies and complex boundaries, which pose significant challenges to accurate segmentation using conventional methods. To address this issue, an improved VGG16?UNet semantic segmentation model that integrated deformable convolution and a coordinate attention mechanism was proposed, aiming to improve the recognition and localization of shadow regions. The model employed VGG16 as the backbone encoder, where deformable convolution was introduced to dynamically adjust sampling locations, thereby effectively capturing features within irregular shadow neighborhoods. Simultaneously, a coordinate attention mechanism was embedded to enhance the synergistic representation of spatial positional information and channel?wise features, optimizing detail recovery and structural consistency. Validation experiments conducted on a self?built dataset and domestic GaoFen?7 satellite imagery showed that the proposed method achieved mean intersection over union (mIoU), mean recall (mRecall), and overall accuracy (OA) scores of 94.77%, 97.28%, and 97.52%, respectively. These results represented improvements of 0.62 percentage points, 0.41 percentage points, and 0.30 percentage points over the baseline VGG16?UNet model. Furthermore, tests across diverse mountainous scenarios confirmed that the method possessed stable and reliable shadow detection capabilities, along with strong generalization performance and robustness. This work can provide a reliable technical pathway for the automated extraction of high?precision terrain shadows.

Abstract:Obstacle detection is one of the key technologies for enabling autonomous operation of unmanned agricultural machinery. To address obstacle detection in the complex environments of unstructured farmland in southern China, a dataset of obstacles in such farmlands was collected and annotated，and data diversity was enhanced through data augmentation techniques. Based on the YOLO v8 model, the Shuffle Attention mechanism was introduced into the C2f module, proposing an improved C2f?ATT module to enhance feature representation. Simultaneously, a convolutional neural network building block was used to replace some conventional modules, forming the YO?STNet model. The enhanced intersection over union (EIoU) loss function was adopted to replace the original loss function, balancing the decreased convergence speed caused by increased model complexity. Experimental results showed that the proposed model exhibited significant advantages in detection accuracy, particularly in small object detection and blurred object recognition, while network convergence speed was also markedly accelerated. Compared with the original YOLO v8 model, the improved model achieved a 3.67 percentage points increase in average detection accuracy. Comparative results with models such as YOLO v5, YOLO v7, YOLO v10, Faster R?CNN, and SSD demonstrated that the proposed model offered superior performance and higher precision. The findings can provide important technical reference and practical support for autonomous obstacle avoidance of unmanned agricultural machinery in complex farmland environments.

Abstract:Parsing of main stem phenotype is of great significance for understanding plant growth patterns, optimizing planting density and improving crop yield. In this study, one soybean main stem node detection model YOLO?SN based on improved YOLO v8n was proposed for multiple breeding scenarios, and the phenotypic parameters of main stem node number, internode length and main stem length were analyzed. Firstly, aiming at the problems of poor model adaptability and high missed detection rate caused by significant differences in the characteristics of soybean main stem nodes in three typical breeding scenarios, including open field, indoor and artificial climate chamber, respectively. The DAF (Deformable attention fusion module) module is constructed in the backbone to enhance the model?s ability to pay attention to node regions in multiple scenarios;Secondly, for the problem that the small pixel share of main stem nodes leads to recognition difficulties, the DDH (Dynamic decouple?head) was designed in the head to improve the model?s ability to perceive the main stem nodes of soybeans;Finally, the dynamic non?monotonic focusing mechanism WIoU (Wise intersection over union, WIoU) was designed for further improving the convergence speed and generalization of original network YOLO v8n. The experimental results show that the main stem node detection accuracy, recall, average precision mean and F1?score are significantly improved in a single scene, and the comprehensive performance in multiple scenes reaches 90.6%, 85.1%, 90.0% and 87.8%, respectively, which are all better than the mainstream target detection models. The absolute error and coefficient of determination of the number of main stem nodes, the distance between nodes and the length of main stem were 0.42, 12.6 pixels, 17.4 pixels and 0.88, 0.80, 0.82, respectively. This study provide an effective method for the accurate parsing of soybean plant phenotypes in typical breeding environments, as well as offering technical support for intelligent breeding of soybeans and other legumes.

Abstract:Cucumber downy mildew significantly impacts yield and quality. Quantitative characterization of the infection structures of the cucumber downy mildew pathogen in microscopic images is crucial for assessing the degree of pathogen infection and analyzing infection behavior. To address challenges such as varying sizes and overlapping of different infection structures, the quantitative characterization of cucumber downy mildew pathogen infection structures was implemented based on an instance segmentation network. Firstly, an in?situ stained microscopic image dataset of the cucumber downy mildew pathogen was constructed. Secondly, an instance segmentation model for microscopic images of the cucumber downy mildew pathogen, DS?YOLO v8s, was developed, significantly improving the detection and segmentation capabilities of infection structures. Thirdly, a quantitative characterization system for the infection structures of the cucumber downy mildew pathogen was established by using morphological analysis methods. Finally, a quantitative characterization system for the infection structures of the cucumber downy mildew pathogen was designed and implemented. Experimental results demonstrated that the proposed DS?YOLO v8s model achieved mAP_box@.5 of 89.5% and mAP_mask@.5 of 80.1%. In terms of morphological distribution, the perimeters of spores, sporangia, branching structures, and hyphae were concentrated in 40~90 pixels, 200~350 pixels, 0~1 500 pixels, and 0~800 pixels, respectively;their areas were concentrated in 100~400 square pixels, 3 000~6 000 square pixels, 0~20 000 square pixels, and 0~6 000 square pixels, respectively. The circularity of spores and sporangia was concentrated in 0.68~0.78 and 0.70~0.85, respectively, and the hyphal length was concentrated in 0~300 pixels. The quantitative characterization system for cucumber downy mildew pathogen infection structures implemented functions such as user registration and login, image segmentation, and quantitative characterization. The research result can provide a reusable technical pathway for the early precise monitoring and intelligent control of cucumber downy mildew, and lay a methodological foundation for high?throughput phenotypic screening in disease?resistant breeding and the establishment of a standardized evaluation system for disease phenomics.

Abstract:Lightweight detection, tracking, and counting passion fruit are crucial technologies for achieving intelligent harvesting in smart agriculture. In real orchard environments, factors such as overlapping occlusion with leaves and branches, along with lighting variations, often result in missed detections, false detections and repeated counting. To address these challenges, a lightweight counting framework was proposed based on YOLO 11n?CLL and BoT?SORT. In object detection, YOLO 11n?CLL was built upon YOLO 11n as its baseline model. Firstly, a lightweight context guided block was introduced to enhance contextual perception capability. Nextly, the large separable kernel attention mechanism was incorporated to improve multi?scale feature modeling. Finally, the lightweight shared detail?enhanced convolutional detection head was employed to effectively reconstruct the texture features of occluded fruits. For object tracking, the BoT?SORT tracker was adopted, which incorporated a camera?motion?compensation module to mitigate camera?shake interference and improve tracking accuracy. In fruit counting task, a region?based counting method was adopted to achieve accurate counting of passion fruits. Results showed that YOLO 11n?CLL achieved mAP@0.5 of 87.0% with a compact 5.0 MB model size. The BoT?SORT tracker achieved a HOTA of 62.4% and a MOTA of 68.4%. The region?based counting method yielded an average counting accuracy of 92.8%, outperforming the line?based counting and ID?based counting method by 8.8 percentage points and 29.9 percentage points. The results demonstrated that the proposed framework enabled detection, tracking, and counting of passion fruits and provided technical support for intelligent harvesting in passion fruit orchards.

Abstract:Pollination is a critical stage in muskmelon cultivation, and mechanical pollination has emerged as a significant development direction due to its efficiency benefits. Accurate identification of female and male flowers is essential for the operation of pollination robots. For identifying melon female and male flowers, an improved YOLO 12s target detection network was applied, and the systematic deployment was implemented on edge device. By replacing the Backbone of the original model with the ShuffleNetV2 structure, the model?s complexity and computational load were reduced, and the purpose of improving detection speed was achieved. Additionally, the knowledge distillation was also employed to further improve the performance of the improved model. A total of 3 597 muskmelon images were annotated in the greenhouse environment and then sent to the improved YOLO 12s object detection network for model training. Under identical experimental conditions, a comprehensive comparison was conducted with current mainstream algorithms. The melon flower detection model was evaluated across five metrics: precision, recall, mean average precision, model size, and detection speed. Test results showed that the model achieved a precision of 79.93%, recall of 87.65%, mAP of 86.54%, a model size of 4.10 MB, and a detection speed of 153.46 f/s. Compared with YOLO 12s, YOLO v10s, YOLO v8s, YOLO v5s, SSD, and Faster R?CNN models, the detection speed was improved by 79.32%, 75.11%, 45.14%, 38.23%, 746.44% and 1 168.26%, respectively. Furthermore, the model maintained accurate detection performance under complex conditions such as varying lighting, partial occlusion, and image blur. When deployed on the Jetson Xavier NX embedded platform, this model achieved a mAP of 86.62%, the detection speed was 26.37 f/s. Compared with YOLO 12s and YOLO v5s, the detection speed was 90.40% and 15.51% faster, respectively. This performance was sufficient to meet the practical requirements for both accuracy and real?time processing in pollination operations. The results demonstrated that the lightweight model can accurately and efficiently identify male and female melon flowers. With only a minor loss in accuracy, it significantly reduced memory consumption, facilitating its deployment on edge devices. The research result can provide technical support for mechanical pollination of melons.

Abstract:Pear leaf spot disease, caused by fungi of the genus Alternaria, is a widespread infectious disease that severely impacts the growth and yield of pear trees. Qualitative analysis of the infection severity of pear leaf spot disease, coupled with precise pesticide application based on this assessment, is of paramount importance for reducing economic losses and achieving a reduction in pesticide usage. The hyperspectral imaging technology was employed to analyze different types of pear leaves—-healthy, mild, moderate, and severe leaves. By integrating spectral, image, and chlorophyll features, a high?precision disease grading model was developed to accurately classify and assess the severity of black spot disease on pear leaves. Hyperspectral data were collected by using a specialized imaging system. The spectral data were preprocessed with Savitzky?Golay (SG) smoothing, and characteristic wavelengths were selected through the successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), which identified the optimal wavelengths (OWs1 and OWs2). Texture features (TFs) were extracted by using gray?level co?occurrence matrix (GLCM) analysis. chlorophyll content (SPAD) was measured with a plant nutrition meter and served as an indicator of disease severity. Different combinations of spectral, texture, and chlorophyll features were used as input variables to build disease grading models based on radial basis function neural networks (RBFNN), convolutional neural networks (CNN), and a hybrid CNN?LSTM model incorporating an attention mechanism (CLATT). The models? performances were compared and analyzed. The results from the test set showed that the CLATT model, which utilized the combined features of OWs1, TFs, and SPAD, achieved the highest classification accuracy, with an average recognition rate of 98.63%. The detection accuracies for healthy, mild, moderate, and severe leaf samples reached 98.00%, 98.21%, 99.24%, and 99.06%, respectively. The research results can provide ideas for grading detection of pear leaf black spot disease and guide the implementation of accurate pesticide application.

Abstract:Accurate detection of Korla fragrant pear fruit is a key prerequisite for intelligent low?damage harvesting, as detection accuracy and real?time performance directly affect the operational efficiency and stability of harvesting robots. However, existing methods still face challenges in practical orchard applications due to variations in fruit size, illumination changes, occlusion by leaves and branches, and complex backgrounds. To improve detection accuracy and operational adaptability for Korla fragrant pear, a lightweight improved detection model, termed MEW?YOLO, was proposed based on YOLO 11n. MEW?YOLO introduced a mixed local channel attention (MLCA) module, an efficient up?convolution block (EUCB), and a wise intersection over union (WIoU) loss function, thereby enhancing model adaptability to complex scenes from three aspects: feature enhancement, spatial detail restoration, and bounding?box regression optimization. Specifically, MLCA integrated local and global channel information to strengthen multi?scale feature representation;EUCB reinforced spatial information reconstruction during feature fusion to improve the representation of small and occluded targets;and WIoU optimized the allocation of regression gradients through sample?quality?aware weighting and focusing regulation, enhancing localization robustness under occlusion and overlap conditions. On the collected Korla fragrant pear dataset captured under natural orchard conditions, MEW?YOLO outperformed the baseline YOLO 11n by 2.7, 3.3, 5.3, and 3.7 percentage points in mAP@0.5, mAP@0.5:0.95, precision, and recall, respectively;it used 2.67 ×10^6 parameters and required 6.8×10^9 of computation. The results indicated that MEW?YOLO can provide reliable visual input for automated detection and subsequent harvesting operations in natural Korla fragrant pear orchards, and offered a reference for the lightweight design of detection models for specialty fruit targets.

Abstract:Addressing critical technical bottlenecks in precision orchard spraying, specifically the insufficient real?time performance, poor generalization in complex scenarios, and model lightweighting challenges in citrus tree canopy instance segmentation, an innovative lightweight instance segmentation model was proposed based on an enhanced YOLO 11n?seg architecture. Key technical innovations included employing depthwise separable convolution (DSConv) to compress parameters in critical layers to merely 11.3% of the original structure, coupled with a channel separation strategy that significantly reduced computational redundancy, introducing a novel global attention mechanism (GAM) that achieved fused three?dimensional channel?spatial weighting through dimensional permutation operations, effectively suppressing 42.7% of overexposed region misdetections while enhancing edge feature representation and designing a lightweight segmentation detection head (LSDH) that integrated multi?scale feature fusion with dynamic channel pruning, reducing computational load by 31.4% while maintaining segmentation accuracy. To address data scarcity, a specialized RGB?D citrus canopy dataset containing 2 500 annotated samples captured by using Kinect DK depth cameras was constructed. This dataset was expanded through depth threshold filtering and five?dimensional adversarial augmentation (incorporating geometric transformations, photometric variations, and synthetic noise injection) to comprehensively represent complex orchard environments. Experimental validation under realistic 35% foliage occlusion conditions on a mobile spray platform operating at 0.5 m/s demonstrated the model?s superior performance: segmentation accuracy ((Seg) AP50) reached 92.6% (2.4 percentage points improvement over baseline), inference time achieved 0.178 s (12.7% faster than that of YOLOACT), and parameter count was reduced to only 2.53×10^6 (24% of Mask R?CNN). Field deployment results confirmed the system?s practical viability: utilizing keyframe point cloud fusion technology, image processing latency was constrained to 320 ms, enabling precise spraying control with just 0.2025 m displacement error at 0.5 m/s vehicle speed (total system delay: 404.93 ms). Variable?rate spraying validation showed 45.75% pesticide reduction, spray distribution uniformity (coefficient of variation) of 10.87%, and 17.53% reduction in overspray within overlapping canopy regions demonstrating improvements over conventional spraying methods.

Abstract:Accurate identification of pepper diseases and pests in complex field environments remains challenging due to dense target distributions, significant scale variations, and practical deployment constraints of existing detection models. To address these issues, PDPI?YOLO, a efficient detection model was proposed based on an enhanced YOLO 11n architecture. Firstly, the model reconstructed the feature extraction structure using MobileNetV4 as the backbone, leveraging its efficient inverted residual blocks and integrated attention mechanisms to enhance multi?scale feature representation. Secondly, the original SPPF module in the backbone was replaced by the SPPF_LSKA feature enhancement module. By integrating the large kernel attention (LSKA) mechanism, this module augmented global contextual information at the end of the backbone, providing superior top?level semantic inputs for the feature pyramid and thereby optimizing the efficacy of feature fusion within the pyramid structure. Furthermore, a dynamic upsampling operator (Dysample) was incorporated into the feature fusion process to adaptively enhance multi?level feature integration and localization accuracy. Finally, the EfficientHead optimized the detection head structure, ensuring high precision while handling dense and scale?varying targets. Experiments on a public Kaggle dataset of six common pepper diseases and pests showed that PDPI?YOLO achieved a precision of 80.8%, recall of 75.7%, mAP50 of 83.1%, and mAP50-95 of 43.5%, outperforming the baseline by 5.6, 1.7, 3.8, and 1.2 percentage points, respectively. When deployed in a visual interface, the model achieved inference times under 0.1 s per image, meeting real?time requirements. Validation on a self?built dataset of Dongshan smooth?skinned pepper further confirmed its generalization capability and robustness, with inference times below 0.2 s per image. This model can offer practical technical support for automated disease and pest monitoring systems and intelligent plant protection equipment in edge environments.

Abstract:Corn is one of the three major grain crops in China, and precise application of weeds at the seedling stage is crucial to ensure its yield and quality. Weeds in the seedling stage compete with corn for sunlight, water, and nutrients, seriously affecting the normal growth of corn and even leading to yield reduction or death. Therefore, accurate and fast weed identification is the basis for precise weed control. Existing weed recognition in field environments is mostly based on small field?of?view image datasets, and few studies related to corn weed recognition in field multi?row environments are seen. In the field multi?row environment, because a single image data can cover multiple rows of crops at the same time, the number of target objects is dense, and the recognition range of weeds is larger in a single detection. However, because a variety of weeds are common in the seedling stage of corn, and the weeds and crops overlap each other, the image data in the multi?row environment is more complex, and it is more difficult to control the detection accuracy and computational volume of the conventional model. To address the above problems, northeast seedling corn was taken as the research object. The corn seedling and weed datasets used for model training and containing both single and multi?targets were collected, and data enhancement operations were performed on the datasets. The specific main content was to address the problem of insufficient target detection speed in the process of precision application and weed control, which led to the deviation of target application. A field corn weed identification method with an improved YOLO v8n model was proposed by using a northeastern corn field as the research object. By collecting pictures of corn seedlings and weeds in the field under the range of multiple rows as a training set, which included single weeds, multiple weeds of multiple species, and the coexistence of corn seedlings and weeds, the backbone network of the YOLO v8 model was replaced by the GhostNet lightweight feature extraction network to simplify the feature extraction process and reduce the number of model parameters. The weighted bidirectional feature pyramid network (BiFPN) was introduced to enhance the information acquisition ability of the model. Triplet Attention, a lightweight attention mechanism, was introduced into the neck network of the model, and the loss function was replaced with Wise?IoU v3 to improve the generalization ability and fine reading of the model. The experimental results showed that compared with the original model, the improved model parameter count was reduced by 37.90%, the detection speed was increased by 32.10%, the mean average precision (mAP) was improved by 0.65 percentage points, the precision was improved by 1.85 percentage points, and the recall was improved by 1.40 percentage points. The research results can provide a basis for decision?making in herbicide reduction application.

Abstract:Underwater object detection in industrial recirculating aquaculture systems presents significant challenges due to low?quality captured images and fish occlusions. To address these limitations, an enhanced YOLO v10s?based underwater Atlantic salmon detection method, termed YOLO v10s?SEAM?D2S was proposed. Firstly, a separated and enhancement attention module (SEAM) was incorporated into the neck network to amplify feature responses in non?occluded regions, thereby mitigating response loss in occluded areas and improving the model?s robustness in detecting occluded targets. Secondly, a novel depth?to?space (D2S) convolution was introduced to enrich the effective information within intermediate feature maps, enhancing the model?s capability to detect objects in blurred images. Finally, grouped spatial convolution (GSConv) was integrated into the neck network to reduce computational complexity, specifically by decreasing floating point operations per second (FLOPs) and memory footprint. Experimental evaluations demonstrated that the proposed YOLO v10s?SEAM?D2S model significantly outperformed the baseline YOLO v10s on an underwater Atlantic salmon detection dataset collected in an industrial recirculating aquaculture environment. The proposed model achieved a mean average precision (mAP) of 91.6% and a precision of 84.9%, yielding respective exhibited superior robustness in detecting occluded targets. Compared with existing state?of?the?art methods, the proposed approach achieved higher detection accuracy and was particularly well?suited for underwater object detection in industrial recirculating aquaculture systems.

Abstract:Defecation frequency serves as a crucial indicator for assessing pig health and environmental hygiene. However, existing methods struggle to accurately identify and quantify individual defecation behaviors in multi?pig housing environments and long?duration scenarios. To address these challenges, a spatiotemporal attention?based inverse reasoning (SAIR) framework for automated analysis of pig defecation was proposed. The framework first designed a spatial attention?enhanced YOLO v8 model for detecting pigs, pig hindquarters, and feces. It incorporated an improved BoT?SORT algorithm for pig tracking. Subsequently, an adaptive weighted fusion multi?layer perceptron network was proposed for refined recognition of suspected feces with significant background interference and low resolution. Starting from the feces detection frame, the framework backtracks historical trajectories using K?Means clustering to analyze spatial correlations between pigs and feces. Simultaneously, temporal correlations between feces and pigs were computed by using a temporal attention mechanism. Finally, by integrating the spatial and temporal correlations, the model identified the specific pig involved in defecation. In short video tests, the proposed method achieved classification accuracy, recall, and precision of 97.7%, 97.2%, and 98.1%, respectively. In long video scenarios, the F1?score, recall, and precision reached 88.5%, 90.0%, and 87.1%, respectively. The proposed approach effectively reduced recognition errors caused by multiple fecal discharges in a single defecation event and trajectory misjudgments in long?term sequences, providing reliable support for pig defecation behavior recognition and health assessment.

Abstract:It is of great significance to estimate the aboveground biomass of rice accurately and timely for precision management of rice field. However, the existing researches focus on using single UAV remote sensing data, which is difficult to achieve accurate estimation of aboveground biomass in the late growth stage of rice due to the spectral saturation effect. To this end, the drone multispectral remote sensing images, meteorological data, and aboveground dry biomass data of rice during the 2023 and 2024 growing seasons were collected. A multi?source feature fusion model for aboveground biomass estimation was constructed to achieve accurate and effective estimation throughout the entire growth period and across multiple growing seasons.The results showed that the vegetation index，vegetation index and texture characteristics，vegetation index and texture characteristics and effective product temperature as the input variable，using multiple linear regression（MLR）, random forest（RF）, partial least squares（PLS）and support vector machine（SVM） to establish the rice ground biomass estimation model. The accuracy was gradually improved and the model accuracy established by the RF algorithm was the highest. With the vegetation index as the model input variable，the adjusted coefficient of determination (adjusted R2) during flowering，late flowering and all reproductive stages were 0.71,0.67 and 0.7，respectively，root mean square error (RMSE) were 268.62 g/m2，300.29 g/m2 and 249.43 g/m2，respectively. With the vegetation index and texture features as the model input variables，the corresponding adjustment R2 were respectively 0.75, 0.72 and 0.74，RMSE were 213.79 g/m2，239.81 g/m2 and 289.46 g/m2，respectively. With vegetation index and texture characteristics and effective product temperature as input variables，the corresponding adjustment R2 were respectively 0.84, 0.87 and 0.87，and RMSE were 176.9 g/m2，162.81 g/m2 and 163.08 g/m2. Using 2024 data as validation, RF achieved an adjusted R2 of 0.60 and RMSE of 288.19 g/m2 across the entire growth cycle, enabling precise estimation of aboveground dry biomass in rice across growing seasons. The proposed integrated approach combining UAV remote sensing and meteorological data provided a robust method for accurate aboveground biomass estimation throughout the growth cycle and across seasons, offering technical support for precision rice management in smart agriculture.

Abstract:High?quality training samples are crucial for crop recognition using remote sensing. The timely and accurate acquisition of winter wheat samples serves as the foundation for such identification. However, obtaining sample points is often challenging, representing a key factor that limits the classification and recognition of crops using satellite remote sensing imagery. To accurately identify multi?year winter wheat planting areas in the Guanzhong Plain, an automatic sample generation and sample migration strategy suitable for winter wheat recognition in this region was constructed based on Sentinel?2 satellite remote sensing imagery. The Guanzhong Plain was divided into five study areas (Baoji, Xianyang, Xi?an, Tongchuan, and Weinan) according to municipal administrative boundaries. Using 2020 as the reference year and 2019 and 2021 as the migration years, a method for remote sensing identification of winter wheat fields was developed and validated. An automatic sample generation method was developed based on an existing 30 m spatial resolution Chinese winter wheat planting distribution dataset to obtain training samples for the Guanzhong Plain in 2020. The accuracy of automatic sampling was verified using measured samples from Xianyang City. Concurrently, the random forest algorithm and remote sensing imagery from four distinct growth stages (overwintering, regreening, heading, and maturity) were employed to classify and identify winter wheat fields, yielding recognition results for each stage. Subsequently, based on the automatically acquired training samples, the Euclidean distance (ED) and spectral angle distance (SAD) were utilized to determine the optimal growth stage and corresponding classification thresholds for sample migration in the test areas (Xianyang, Baoji, and Xi?an). Finally, the effectiveness of identifying winter wheat using the optimized growth stage and thresholds was validated in the threshold verification areas (Weinan and Tongchuan), and spatial distribution maps of winter wheat in the Guanzhong Plain from 2019 to 2021 were generated. The results indicated that the automatic sample generation method utilizing the winter wheat product base map can obtain accurate and reliable winter wheat field samples. For the reference year, the overall accuracy (OA) of winter wheat field recognition in the Guanzhong Plain exceeded 93%, with F1?scores higher than 93%. The relative error between the extracted area and Shaanxi provincial statistical data (except weina) was less than 23%, and compared with the base map data, it was less than 12%. When conducting inter?annual migration of winter wheat samples in the Guanzhong Plain, the optimal thresholds for ED and SAD were determined to be 0.4 and 0.8, respectively, with the heading stage identified as the optimal growth period. Under these conditions, the overall accuracy of remote sensing recognition for winter wheat fields in the five study areas for 2019 and 2021 was greater than 88%, with F1?scores higher than 89%. The relative error between the extracted area and statistical data (except Weina in 2019) was below 23%, and compared with the base map data, it was below 19%. The research result demonstrated that the proposed automatic sample generation and migration method can accurately and rapidly identify winter wheat planting areas in the Guanzhong Plain.

Abstract:Soil moisture is a significant variable that influences agricultural productivity and climate dynamics. However, conventional GNSS?reflection (GNSS?R) methods for soil moisture retrieval often suffer from limited accuracy and insufficient capability to capture deep data features. A hybrid deep learning model that integrates convolutional neural networks (CNN) and long short?term memory (LSTM) networks for soil moisture retrieval was proposed to improve these issues. This model can fully leverage the advantages of CNN in feature extraction and LSTM in time series modeling, especially the complementarity in multi?source data fusion. By incorporating GNSS?R reflected signals along with brightness temperature (TB) and the integrated attenuation coefficient related to soil roughness and vegetation cover (TR) obtained from the ELBARA?Ⅱ radiometer, the proposed model enabled a more comprehensive representation of soil moisture dynamics. Compared with the conventional GNSS?R retrieval model and classical machine learning methods (such as support vector machine (SVM), multilayer perceptron (MLP), random forest (RF)), the proposed model achieved higher correlation coefficients (R) and lower root mean square errors (RMSE). The results verified that the proposed model offered superior accuracy, robustness, and generalization performance, making it well?suited for continuous soil moisture monitoring in fixed regions. This work demonstrated the potential of deep neural networks for enhancing remote sensing data fusion and improving the accuracy of soil moisture retrieval.

Abstract:Climate change and high?intensity human activities have significantly increased the uncertainty of hydrological and water usage processes, posing risks to water resource regulation in irrigation districts. In response to the uncertainties caused by the combination of different frequency precipitation, runoff, and water demand, a research framework of “joint probability analysis of supply and demand?optimal scheduling of reservoirs?identification of water supply risks” was proposed. Considering the nonlinear interdependence among precipitation, water demand, and river source inflow in the irrigation district, a Vine Copula joint probability distribution model was proposed to analyze the probability of the combination of water supply and demand under different wet and dry conditions. A multi?objective reservoir group optimal scheduling model was constructed to balance fairness and efficiency, as well as the competition between agricultural and ecological water use, and to extract reservoir scheduling schemes under different supply and demand wet and dry conditions. A multi?attribute decision?making cloud model and a reservoir group water supply risk assessment index system were coupled to analyze the water supply risks of different scheduling schemes. The proposed research framework was applied to the reservoir scheduling of the Baojixia Irrigation Area. The results showed that Vine Copula can effectively represent the joint uncertainty of the three variables of precipitation, runoff, and water demand with different frequencies. Among them, the joint probability of scenario dry precipitation, dry runoff, and abundant water demand (DDW) was the highest (13.56%), which was close to the actual encounter probability (13.33%). The multi?objective reservoir scheduling model can effectively balance the competition between fairness and efficiency, as well as agricultural and ecological water use. The Gini coefficient of different scheduling schemes was all below 0.4, the irrigation water shortage rate was less than 42.0%, the economic benefit was 761 million~812 million yuan, and the ecological water shortage was less than 1.17×10^8 m3. The scheduling scheme C4 corresponding to scenario dry precipitation, dry runoff, and abundant water demand (DDW) had the highest water supply risk, which was 49.02%, and the comprehensive risk level was grade Ⅲ. The research can provide a scientific basis for the water supply risk of reservoir groups under the joint uncertainty of water supply and demand in irrigation districts.

Abstract:In order to improve solar energy utilization and further reduce the electricity dependence of solar?heat pump drying systems, a solar?heat pump greenhouse drying system was designed based on phase change heat storage, consisting of a drying chamber, heat pump, heat collection?storage, and control modules, featuring four intelligent operating modes (SE, S?HP, HP, HS?HP). The system utilized solar collectors to gather heat and paraffin phase change materials for energy storage, combined with heat pump dehumidification to achieve multi?energy complementarity and automatic control. Drying experiments were conducted using kelp as the test material to evaluate the drying efficiency, energy consumption, and solar energy utilization under different weather conditions by monitoring drying characteristics and system performance indicators such as SMER, COP, and thermal efficiency. The results indicated that during all?day operation, both multi?energy drying modes, solar?heat pump (S?HP) and heat storage?heat pump (HS?HP), can significantly increase the effective moisture diffusion coefficient (Deff) of the kelp. During the daytime, solar energy provided 29.82%~34.30% of the energy for the drying system. At the same time, the maximum temperature of the collector can reach 74℃, with average thermal efficiency ranging from 42.43% to 65.99%, and the heat storage tank demonstrates good energy storage capability. At night, the phase change heat storage system supplies preheating energy for kelp drying, accounting for 20.17% of the total energy supply. Compared with the heat pump (HP) mode, the heat storage?heat pump (HS?HP) mode reduced drying time by 18.18%, saved 28.10% energy, and the coefficient of performance (COP) was increased by 25.82%. Utilizing phase change energy storage can improve solar energy utilization by 31.54%~45.67%. In summary, the solar?heat pump?phase change heat storage multi?energy cooperative drying technology had significant advantages in improving drying efficiency and energy savings, providing a theoretical basis and technical support for the study of key technologies and equipment for drying large quantities of low?value aquatic products.

Abstract:The environmental conditions of Angora rabbit houses directly affect the health of rabbits, wool production performance, and industrial benefits. Proper thermal environment control is therefore essential for ensuring breeding efficiency. A standardized closed?type Angora rabbit breeding house was selected as the research object, and three?dimensional models of the house in winter and summer were constructed by using CFD numerical simulation. The spatial distributions of temperature and wind speed were analyzed. Results showed that in winter, the overall wind speed inside the house was low, turbulence was likely to occur in the upper layer, while the lower layer exhibited high temperatures with great fluctuations. In summer, wind speed was increased significantly, and the overall temperature presented a south?to?north increasing trend, with noticeable heat accumulation in the lower layer. The model was validated by using experimental measurement data. The relative error between the temperature simulation results and the measured data was 3.34%, which could reproduce the macro spatial distribution of the indoor temperature field. The overall relative error of wind speed was 11.97%, mainly attributed to the sensitivity of low?wind?speed areas inside the house to measurement accuracy and numerical calculation. In the future, the error can be further reduced by optimizing measurement methods and model parameters. Based on the simulation results, the anti?bite partition structure of rabbit cages was optimized. The improved partition effectively enhanced airflow uniformity inside the cages, reduced spatial fluctuations of temperature and wind speed, and improved the stability of the microenvironment. The research result demonstrated that CFD simulation combined with structural optimization can provide theoretical support and engineering guidance for environmental regulation and cage improvement in rabbit houses, with promising application prospects.

Abstract:Aiming at the problems existing in the die?roller clearance adjustment process of the cotton straw biomass pellet ring die mill, such as nonlinear coupling interference, time delay of the pneumatic system, decrease in lubricating viscosity, increase in thermal expansion clearance and attenuation of component stiffness under high?temperature working conditions, a transfer function model of the pneumatic drive transmission system was established and a fuzzy PID control system was designed. The transmission system was composed of three subsystems in series, namely the pneumatic motor, worm gear and worm, and ball screw. Combined with the physical characteristics of high?temperature working conditions, the first?order model of the pneumatic motor with a time delay of 0.358 s, the first?order model of the worm gear and worm with efficiency reduced to 0.82 at 85℃, and the second?order model of the ball screw with the thermal stiffness attenuation coefficient introduced were derived respectively. Finally, the overall transfer function model reflecting the actual working conditions was constructed. The designed fuzzy PID control system took the forming force deviation E and deviation change rate EC as inputs, and dynamically adjusted the ΔKp, ΔKi and ΔKd parameters to realize overshoot suppression. In addition, a mechanical limiter was adopted to ensure that the clearance was not less than 0.6 mm, so as to prevent die?roller collision. To verify the system performance, a prototype test platform was built and dynamic response tests were carried out. The results showed that the average rise time of the system was 4.8 s, the average adjustment time was 10.5 s, the overshoot was only 2.5%, the steady?forming force error was not more than 5.8%, and the steady?state coefficient of variation of die?roller clearance was 2.05%. The forming rate of the produced pellet fuel reached 96% with a density of 1 290 kg/m3. All indicators met the requirements of industrial production, which proved that the system had high precision and strong reliability.

Abstract:Polyphenols are natural organic molecules containing benzene rings, widely present in grapes and wines, contributing significantly to wine's distinctive color and astringency. Traditional polyphenol analysis methods primarily rely on HPLC, but the complexity of its procedures results in delayed outcomes. Infrared spectroscopy, a rapid and non?destructive analytical technique, enables qualitative and quantitative polyphenol analysis in wine by identifying characteristic absorption of specific functional groups. Based on recent advancements in infrared spectroscopy research related to wine polyphenols, the absorption characteristics of specific functional groups of major wine polyphenols in mid?infrared (MIR) and near?infrared (NIR) spectral regions were initially elucidated, clarifying the theoretical foundations for their spectroscopic analysis. Subsequently, detailed methodologies for constructing infrared spectral analysis models were discussed, encompassing spectral preprocessing, outlier elimination, and dataset partitioning. The MIR spectral models for qualitative analysis and molecular structure identification (supervised and unsupervised learning) and NIR spectral models for quantitative analysis (linear and nonlinear models) were specifically addressed. Finally, it outlined the application of infrared spectroscopy in smart viticulture, intelligent winemaking, and wine traceability, and the future developments in detailed spectral characterization were prospected, miniaturized spectroscopic devices were highly integrated, and intelligent modeling was enhanced, offering guidance for improving wine production technology and advancing intelligent winemaking research.

Abstract:Ningxia Yinchuan appellation is one of the core wine producing regions in China. A certain degree of research has been carried out on microorganisms in Yinchuan appellation in recent years, but the current mechanism regarding the role of microorganisms in shaping the flavor profile of wines is unknown, which restricts the directed regulation of fermentation microorganisms aimed at strengthening the appellation profile. The dominant strains in the natural alcohol fermentation process of Cabernet Sauvignon wine were used for single?strain fermentation, and then gas chromatography?ion mobility spectrometry (GC?IMS) and gas chromatography?mass spectrometry (GC?MS) were used to qualitatively and quantitatively analyze volatile compounds in Cabernet Sauvignon wine, and investigate the contribution of dominant strains during alcohol fermentation to the key flavor compounds in the final Cabernet Sauvignon wine from Yinchuan, Ningxia. The results showed that different strains had different effects on wine flavor. Moreover, a single strain cannot produce all the key flavor compounds, which meant the final flavor of wine was formed by the interaction of multiple strains. Among them, Hanseniaspora uvarum (R?Hu), Metschnikowia pulcherrima (R?Mp), Lachancea thermotolerans (R?Lt), Hanseniaspora vineae (R?Hv), Papiliotrema flavescens (R?Pf), and wild Saccharomyces cerevisiae D213, D501, E325, F319 and F513 were the high?yielding strains with the key aroma characteristics in Cabernet Sauvignon wine from Yinhuan, Ningxia. R?Hu produced high levels of β?damsone, R?Mp produced high levels of ethyl hexanoate and ethyl decanoate, R?Lt produced high levels of phenylethanol, R?Hv produced high levels of isoamyl acetate, R?Pf produced high levels of ethyl octanoate, ethyl decanoate, ethyl acetate and phenylethanol, D213 produced high levels of ethyl octanoate, ethyl decanoate, ethyl acetate and phenylethanol, D501 produced high levels of isoamyl alcohol and phenylethanol. E325 produced high yield of ethyl n?caproate, ethyl octanoate, ethyl decanoate and isoamyl alcohol, F319 produced high yield of ethyl n?caproate and F513 produced high yield of isoamyl alcohol.

Abstract:Aiming to address the critical quality control challenge of detecting cracked quail eggs in egg processing, where manual and machine?assisted visual inspection are both inefficient and inaccurate, a high?performance, lightweight online non?destructive detection system was developed based on a comprehensively improved YOLO 11 deep learning architecture. YOLO 11n was selected as the baseline for its favorable balance of speed and accuracy, then systematically optimized for the specific task of identifying subtle cracks on variably patterned quail eggshells through two core phases: network architectural refinement and model compression. In the network refinement phase, the backbone network integrated the C3k2_IDWC module to capture multi?scale textural details and directional contextual information of fine cracks, while the neck network replaced the original SPPF module with SPPF_LSKA (to focus on crack regions via spatial attention) and standard upsampling with EUCB_SCM (to enhance cross?channel information interaction and feature fusion). For model compression, the enhanced network underwent global sparsification via the LAMP pruning algorithm to eliminate redundant parameters, followed by dual knowledge distillation combining BCKD (logit?level guidance) and CWD (feature?level imitation) to transfer knowledge from the high?performance teacher model to the compact student model, yielding YOLO 11?Quail. Experimental results on a dark?field backlighting dataset showed YOLO 11?Quail achieved 94.6% mAP, 95.1% precision and recall, 184.8 f/s inference speed, and only 1.6×10^6 parameters. Deployed on Jetson Nano with a multi?angle tracking algorithm, it delivered an 8.33% false detection rate, 0 missed detection rate, and 68.59 ms latency, satisfying the high?precision real?time demands of industrial production lines.

Abstract:As a piece of key mechanical equipment in agriculture and construction, the performance improvement of loaders is of great significance. The current study mainly focused on the loading and unloading performance of loaders under a given static load, without considering the interaction between the material system, the mechanical system, and the hydraulic system in the excavation process. The loading and unloading performance of the loader was mainly investigated by the fluid simulation and rigid?diffusive coupling methods, which were not able to reflect the real?time interaction between the material system, the mechanical system, and the hydraulic system in the loading and unloading process. For this reason, a loading and unloading performance model of the working device of the widely used valve?controlled loader was constructed based on the machine?liquid?bulk multi?coupling method, on this basis, the loading and unloading performance evolution law of the loader under different composite actions was investigated. The results showed that the lifting hydraulic cylinder rodless chamber pressure was about 8 MPa, the bucket hydraulic cylinder rodless chamber pressure was about 4 MPa, and the bucket hydraulic cylinder rodless chamber pressure was more stable than the lifting hydraulic cylinder rodless chamber pressure under steady state operation. When the opening degree of both the lift hydraulic valve and the bucket hydraulic valve was 62.5%, the full bucket rate of the loader reached 91.48%, and the lift action had obvious influence on the digging volume of the loader. The peak power of the hydraulic pump under the variable lifting hydraulic valve opening condition was higher than that under the variable dumping bucket hydraulic valve opening condition, and the material disturbance velocity was first stabilised and then decreased with the increase of lifting hydraulic valve opening, and increased and then decreased with the increase of dumping bucket hydraulic valve opening. The research findings may serve as a reference for the digital design of loaders.

Abstract:In the process of traditional force feedback joystick interacting with people, force sensors are relied on to measure the operating force. However, installing force sensors significantly increases the user?s operational burden and raises the manufacturing cost of the force feedback handle. To alleviate the user?s operational burden and reduce manufacturing costs, a force estimation strategy was proposed for a two?degree?of?freedom force feedback joystick based on an improved Stribeck friction model. A model was established to describe the handle?s dynamics. The improved Stribeck model was used to characterize the nonlinear friction in the joystick?s dynamics model. The traditional Stribeck friction model was analyzed, and improvements were made to address the problem of abrupt changes in frictional force when the velocity switched between positive and negative zero points. Experiments were designed to identify the parameters in the model equations, considering the characteristics of the joystick system. The least squares method was used to fit curves of the system?s gravity and friction parameters to determine the corresponding parameters. Based on the identified parameters, the force at the joystick?s end was estimated. Experimental results showed that the estimated force was basically consistent with the actual data, verifying the accuracy of the proposed force estimation strategy.