Abstract:Canopy-under navigation path recognition in tobacco fields is often hindered by leaf and weed occlusion, as well as significant variations in plant morphology, presenting challenges for the autonomous operation of human-machine collaborative harvesting robots. To address these issues, a novel canopy-under navigation path recognition method was proposed based on the EME-Net, an inter-row image semantic segmentation model. Built upon the DeepLabV3+ architecture, EME-Net featured an encoder that employed the ECA-MobileNetV2 (dubbed EMNet) to replace the original Xception backbone for efficient feature extraction, enabling the model to effectively capture key inter-row path features. The pyramid split attention (PSA) multi-scale feature fusion mechanism was introduced to enhance the representation of inter-row boundary features, particularly under occlusion. Additionally, the ECA mechanism, embedded at both the output and terminal of EMNet, filtered out irrelevant features from tobacco field images, improving feature utilization without reducing the number of channels. To mitigate accuracy degradation caused by foreground-background imbalance, a robust BCE_DiceLoss function was proposed, combining binary cross entropy (BCE) Loss and DiceLoss. Based on the autonomous traversal region masks outputed by EME-Net, the least squares method was used to reshape edge points and extract inter-row navigation lines. Experimental results showed that EME-Net achieved a mean pixel accuracy (mPA) of 91.3% and a mean intersection over union (mIoU) of 88.9%, surpassing the baseline model DeepLabV3+ by 7.9 and 6.1 percentage points, respectively. The average detection frame rate reached 29.5 frames per second, outperforming mainstream segmentation models such as PSPNet, U-Net, HRNet, and Segformer. In practical tobacco field navigation path recognition experiments, the proposed method effectively extracted navigation lines in canopy-under areas with varying levels of occlusion. The mean heading deviation ranged from 1.45° to 3.80°, and the average lateral pixel distance varied from 1.46 pixels to 3.68 pixels. This method met the practical requirements of inter-row navigation tasks and provides a reliable technical solution for the autonomous transportation operation of human-machine collaborative harvesting robots.

Abstract:Aiming to meet the requirements of edge-aware and edge-planning navigation, it is necessary to perform real-time headland detection to determine the operable area and effectively extract the tillage boundary within the operable region for dynamic planning of navigation paths. In response to challenges such as large headland detection errors caused by the coexistence of tilled and untilled zones in non-homogeneous field scenarios during stubble field tillage operations, and reduced accuracy of tillage boundary extraction due to environmental factors like lighting, a headland detection method was proposed based on the partitioned dispersion of the average grayscale values of image rows, and a tillage boundary detection method using pre-extracted boundaries to define a dynamic region of interest. The headland detection was performed by analyzing the grayscale variation trends in the color space. A single frame was partitioned to evaluate the horizontal distribution of average grayscale values, and an independent dynamic threshold was used to determine the presence of a headland. For tillage boundary navigation line extraction, coarse superpixels were firstly used for preliminary image segmentation to extract pseudo navigation lines and determine the region of interest. Then a four-direction bidirectional gradient adaptive weight total variation algorithm was applied for noise filtering and denoising. The region image was finely segmented by using a two-dimensional cross-entropy method. Finally, the Canny operator was used to extract edge feature points, and pre-screening was performed on boundary points to be fitted. The navigation line was then fitted by using the random sample consensus algorithm, ultimately achieving accurate navigation line detection. Experimental results showed that the proposed headland recognition method achieved a detection accuracy of 96.04%, with an average processing time of 11.17 ms/f. The navigation line extraction method yielded an average angular deviation of 1.31° from manually annotated navigation lines. At the median image height, the average horizontal pixel deviation and spatial deviation were 10.95 pixels and 32.04 mm, respectively. The average processing time for navigation line extraction was 86.65 ms/f. This demonstrated the method's capability for stable and effective navigation line extraction, providing a reference for autonomous rotary tillage operations in stubble fields.

Abstract:Aiming at the problem of low accuracy of positioning and navigation system during the parking of agricultural machinery, an adaptive zero velocity update algorithm containing velocity constraints and heading angle constraints was proposed, and a single-antenna BDS/INS integrated system on a self-developed small agricultural machinery was built to verify the feasibility and advantages of the proposed method. In order to ensure that the agricultural machinery remained in a stationary state during zero velocity update, a zero-velocity detector based on the acceleration of x axis (right side) and the angular velocity variance of z axis (top side) was designed through the analysis of the measured data, and a large number of misdetected zero-velocity intervals that were discretely distributed but with short durations were eliminated based on the length of the zero-velocity duration window. Due to the inability of a single antenna combination system to provide a valid estimated value of heading angle at rest, the heading angle at the moment of parking would be used as the input value for the zero-velocity update heading angle measurement equation during the corresponding parking phase. And in order to reduce the influence of the abnormal BDS positioning observations on the integrated system, Sage-Husa adaptive filtering was introduced to estimate its positioning noise in real time. The field experiments showed that the zero-velocity detection method had a high accuracy rate, which was in line with the actual driving state of agricultural machinery. During periods of BDS availability, adaptive zero velocity update improved the accuracy of heading angle, velocity, and positioning by approximately 99.3%, 93.3%, and 50%, respectively. When BDS signals were disturbed, the positioning accuracy improvement can exceed 90%. During a 60-second BDS outage, pure INS navigation errors grew rapidly and led to failure, whereas zero velocity update maintained high INS accuracy for an extended period, meeting the precision requirements for unmanned agricultural machinery operations in hilly and mountainous areas. The research result demonstrated that the method can improve the accuracy and anti-interference of single antenna BDS/INS integrated navigation system under complex working conditions, and it was also helpful to provide references for other unmanned agricultural machinery research.

Abstract:Aiming to address low localization accuracy, unreliable fruit recognition, and poor map quality in vineyard robots, PDS-SLAM, a dense mapping algorithm that integrated semantic segmentation was proposed. Built on ORB-SLAM3, each image was partitioned during feature extraction;the regional FAST threshold was adaptively adjusted according to regional corner counts;and quadtree uniformization method with minimum distance was applied, which improved spatial uniformity and matching robustness of feature points, thereby enhancing localization accuracy. A network, PDSNet, was proposed by integrating a DSA module into PIDNet, enhancing spatial perception of grape clusters and improving fruit recognition. A dense mapping thread and an octree thread were introduced: images were projected to recover local dense point clouds via a point cloud recovery algorithm;statistical outlier filter and radius filter were applied to remove aberrant points;semantic masks were used to annotate grape clusters, yielding a dense semantic map that was finally converted into an octomap. In experiments on the EuRoC dataset and a self-collected dataset, a 27.3% reduction in absolute trajectory error (ATE) on the MH03 sequence relative to ORB-SLAM3 and a 15.5% average increase in matched ORB features were achieved, indicating improved localization accuracy. PDSNet achieved an IoU of 78.9% for grape segmentation at 126.92 f/s. The results demonstrated that PDS-SLAM enhanced localization perception and produced dense semantic maps and octree maps, supporting autonomous navigation and precision operations for orchard robots.

Abstract:The decomposition of straw releases nutrients that support crop growth, thereby increasing soil organic matter and fertility. Aiming to achieve rapid decomposition based on the field returning, a tossing-type combination machine was designed for rapid decomposition in the field return of straw. The optimized crank-rocker dimensions were solved based on Graff's theorem and pressure angle conditions, where the length of the crank was defined as one length factor. A model coupling the discrete element method and multibody dynamics was established to simulate the mixing process of liquid decomposition agents and solid phases (soils and straws), utilizing the Kneading contact model. A mixing index was innovatively defined to quantify the mixing performance of the machine in simulations and the field. It was shown that the length factors of crankshaft, connecting rod, rocker arm, frame, and theoretical maximum tillage depth were solved as 1.00, 2.60, 2.60, 3.50, and 1.492, respectively. By using the definition of the mixing index, the calculated mixing indices of the simulation and field were 0.54 and 0.47, respectively, where the relative error between simulations and fields was 12.94%. A systematic technology was proposed for quantifying the solid-liquid mixing, which could take effect simultaneously in both simulation and physical environments. Novel research perspectives and solutions were provided for the mechanization of promoted decomposition in maize straw returned directly.

Abstract:In response to the urgent demand for cotton sowing machinery in wheat-cotton intercropping production in the Yellow River Basin, and the limitations on large-scale application of wheat-cotton intercropping technology due to the lack of supporting agricultural machinery, a design of a precision cotton intercropping seeder was presented, which can complete multiple operations, including furrow opening, sowing, soil covering, and pressing in a single pass, achieving precise cotton sowing in wheat-cotton intercropping. Based on the sowing requirements of the wheat-cotton intercropping model, a complete seeder design scheme was proposed, and the structural parameters of key components such as the profiling mechanism, furrow opener, seeding device, soil covering device, and pressing device were determined. Stress analysis of the machine's longitudinal stability showed that the design met the requirements for stable operation. Discrete element simulation of the furrow opening process was conducted to analyze the effects of forward speed and soil entry depth on soil disturbance. Field trials were conducted, with operating speed, furrow depth, and the horizontal spacing between the furrow opener and the seed drill as influencing factors, and sowing qualification rate and missing rate as the experimental indicators. A three-factor, three-level orthogonal experiment was designed to validate the sowing performance of the precision cotton intercropping seeder. The results indicated that when the machine operated at a speed of 3.4 km/h, a furrow depth of 45 mm, and a horizontal spacing of 360 mm between the furrow opener and the seed drill, the sowing qualification rate reached 91.7% and the missing rate was 0.64%, meeting the requirements of wheat-cotton intercropping planting patterns in the Yangtze and Yellow River regions.

Abstract:Aiming to address the limitations of existing maize seeding devices, which struggle to seamlessly switch sowing modes based on the germination rates of different maize varieties, a novel air-suction maize plot seeding device with a double disc combined seeding plate was designed. This device allowed rapid switching between sowing modes by adjusting the seeding plate's position. The structure and working principle of the seed-metering device were detailed, and the key structural parameters were determined through theoretical analysis. The average negative pressure values of the holes under three sowing modes were analyzed by Fluent software, the experiment showed that there was little change in negative pressure at the hole under three sowing modes. Simulations with EDEM software confirmed that the spine-groove combination disk had good disturbance performance. A comparative bench test was conducted on the seeding performance of different seeders based on the evaluation indicators of missed sowing rate, replay rate, and qualification rate. The test results showed that with the seeding plate speed setting at 22 r/min and negative pressure greater than 4 kPa, the qualified rate of sowing double disc combined device was greater than that of conventional seeder. A sowing mode switching test was conducted for the air-suction double disc combined maize plot seeding device, with the switching state of the seeding pattern as an evaluation indicator. Tests showed that when the switching time was 1 s, the optimal switching speed of the adjacent and the interval sowing mode was 15 r/min and 26 r/min. The proposed air-suction double disc combined maize plot seeder can achieve non-stop switching of sowing modes, the research result can provide a reference for the design and optimization of air-suction seeders for maize plots.

Abstract:Aiming at the problems existing in the open-loop electric-driven seeding process of rapeseed and wheat for the current centralized air-conveyed combined seeder for rapeseed and wheat, such as the lack of dynamic response of seeding rate to the operating speed of the machine and real-time monitoring of the machine's operating status, which result in large errors in the actual seeding rate of the seeder and cumbersome operation procedures for the operator, a speed-adaptive control system for rapeseed and wheat air-assisted centralized metering device was designed. The system was controlled by an STM32 microcontroller. Utilizing human-machine interface information exchange to adjust the seeding rate, precise speed measurement via non-contact radar, and real-time operation posture detection through an inclination sensor, it realized the selection of seeding conditions and accurate identification of the operation state for the seeder, combining with the speed adjustment of the metering device and the dynamic matching of the metering device's drive logic, the automatic control of the electric drive seeding for rapeseed and wheat was achieved. The experimental results showed that the radar field speed measurement method had an error of no more than 2.76% and a stability variation coefficient of no more than 4.12% under different ground conditions, significantly outperforming the ground wheel encoder speed measurement method. The inclination sensor had an angle detection error of 0.13°~0.15°, enabling accurate detection of the seeder's operating posture. Under the field seeding conditions, the control system could achieve speed-adaptive seeding for both rapeseed and wheat at different operating speeds, with the control errors of rapeseed and wheat sowing rates being less than 2% and 3.5%, respectively. The field experiment results showed that when the seeder equipped with the speed-adaptive control system was actually used in field operation, the consistency variation coefficients of the number of plants per row for rapeseed and wheat were 8.59% and 12.96%, respectively, meeting the requirements of the sowing operation standard for rapeseed and wheat. Moreover, compared with the traditional open-loop uniform speed drive method of the seed metering motor, the consistency variation coefficients of the number of plants per row for rapeseed and wheat were decreased by 8.33 percentage points and 5.06 percentage points, respectively.

Abstract:Considering the practical problems of low seedling emergence rate due to drought and low temperature during the sowing period of spring rapeseed seeding method, the traditional film-laying sowing process is complex and the position of seeds and film holes is inconsistent during unit operation, the process scheme of sliding film cutting+synchronous seeding was proposed, a rapeseed laying and perforating synchronous seeder that can realize the integrated operations of rotary tillage, fertilization, film laying, soil covering, perforation and sowing was developed. The influencing factors of the plastic film rotation conditions and the membrane side covering were analyzed, the structure and working parameters of the film covering device was determined. Based on the motion principle of cam swing roller mechanism, the punching mechanism was designed, the structural parameters of the punching mechanism was determined according to the agronomic pore spacing and membrane pore length of rapeseed planting, the dynamic model of punching on the membrane was established, the matching design of the operating parameters of the unit was carried out, and the speed ratio coefficient between the forward speed of the machine and the cam speed λ was obtained as 2.0~2.5;the synchronization analysis of membrane pores and seeds was carried out, the structural parameters of the synchronous seeding mechanism were determined, the main factor affecting the synchronization rate of seeds and membrane pores was analyzed, the length of seed channel p was horizontally constrained by the seed box, and its value range was 18~34 mm. Using the DEM-MBD coupling simulation, the quadrature orthogonal rotation test was carried out with the speed ratio coefficient λ and the horizontal constraint of the seed box and the length p of the seed track as the test factors, the simulation results showed that the synchronization rate between seed and membrane hole was 94.04% when the speed ratio coefficient λ and the seed box horizontal constraint seed track length p were 2.29 and 27 mm. The results of field experiments with a combination of optimal parameters were as follows: the average width of the film edge covering was 80.44 mm, the average length of the film hole was 49.44 mm, and the coefficient of variation of the stability of the film hole length was 5.30%. The hole spacing was 144.76 mm, the synchronization rate was 85.60%, and the synchronization between rapeseed and membrane pores was good, which provided a way for the development of film laying and perforation seeder in spring rapeseed production area.

Abstract:Aiming to enhance the seeding accuracy and uniformity of pneumatic maize planters under high-speed operating conditions, and reduce seed collision and deviation during the seed delivery process, a CFD-DEM coupled simulation model was developed based on aerodynamic principles to describe the airflow-induced motion of seed particles within the seed delivery tube. The motion response and force characteristics of seeds under airflow action were systematically analyzed. Considering a representative seed delivery structure, the effects of airflow velocity, structural parameters, and buffer zone dimensions on the horizontal velocity of seeds at the outlet were investigated. On this basis, the Box-Behnken design (BBD) method was employed to establish a response surface model for screening and optimizing key structural parameters influencing delivery performance. Simulation results indicated that a properly designed delivery structure can significantly reduce the frequency of particle-wall collisions and fluctuations in outlet horizontal velocity, effectively achieving “zero-velocity seed release”. Further bench tests of the seed delivery system showed good agreement between the simulation and experimental results, with a maximum error of less than 8%, demonstrating the high predictive accuracy of the proposed CFD-DEM model. The research findings can provide a solid theoretical foundation and valuable engineering reference for the structural optimization of seed delivery systems and the development of precise seed release control strategies in high-speed pneumatic planting devices.

Abstract:In view of the current problems such as the inability to detect the hole-forming property of millet and the lack of an accurate method for measuring the hole diameter, a detection method for the hole-forming property of millet based on the K-value method was proposed. Firstly, a criterion for the hole-forming property of millet based on the K-value method was established. Secondly, the boundary points of the sown millet were extracted based on image recognition, and a modified Welzl's algorithm was used to screen the key boundary points and calculate the hole diameter. Finally, a detection system for the hole-forming property of millet was designed, which can complete the shooting of the sown seeds and display the results of the circle-making effect, hole diameter, and hole-forming property detection on the detection interface. The experimental results showed that the time compression ratio of the modified Welzl's algorithm was greater than 1 compared with that of the Welzl's algorithm, RIA algorithm, and Dps algorithm, with the highest reaching 16.94. Moreover, the size of the minimum circumscribed circle made by the modified Welzl's algorithm was consistent with the results made by the Welzl's algorithm and RIA algorithm. The hole-forming property of the spoon chain seed delivery device for millet hole sowing under specific parameter settings was detected. The number of test groups with K values between 75% and 125% accounted for 82.6% of the total tests, the double-sowing situation accounted for 9.1%, and the missed-sowing situation accounted for 8.3%. This indicated that the seed delivery device had good seed sowing stability and hole-forming property under this parameter combination setting. The experimental results showed that this system had certain contributions to solving the practical problem of being unable to detect the hole-forming property.

Abstract:Aiming to address the problem of high tillage resistance encountered by traditional rapeseed furrowing devices in the heavy clay soils of the Yunnan hilly and mountainous regions, a rapeseed furrowing device was designed based on plow body oscillation for drag reduction. A double-moldboard plow with lower resistance was selected as the furrowing plow based on the furrow profile. The maximum oscillation angle of the plow body was determined based on Coulomb's soil pressure theory and the geometric relationship between the plow body oscillation angle and furrow width. Mechanical principles and graphical methods were used to determine the dimensions of the mechanism components and verify its transmission feasibility. Finally, through motion analysis of the oscillating mechanism, the relationship between oscillation frequency and plow body motion performance was clarified. To determine the influence of different operating parameters on the drag reduction performance of the oscillating furrowing plow, identify the suitable operating parameter range, and determine the optimal operating parameters, single-factor and Box-Behnken simulation experiments were conducted using EDEM, with plow body oscillation angle, frequency, and operating speed as experimental factors and traction resistance as the evaluation index. Single-factor experimental results showed that the suitable operating speed of the device was 0.5~0.75 m/s, the oscillation frequency was 6~8 Hz, and the plow body oscillation angle was 8°~9.5°. Box-Behnken experimental results showed that the optimal operating parameters of the device were an operating speed of 0.50 m/s, an oscillation frequency of 7.98 Hz, and an oscillation angle of 8.1°. Field verification test results showed that, under the optimal parameter combination, the average traction resistance of the device was 1021.03 N, a 19.00% reduction compared with the traditional fixed-assembly plow body (1260.73 N), with an error of 3.9% compared with the simulation prediction. The furrow depth stability coefficient and furrow width stability coefficient were no less than 93.8%. The oscillating furrowing device showed significant drag reduction effects and stable furrow formation, providing a reference for the optimized design of furrowing devices.

Abstract:Aiming at the problems that the roots and stems of mechanized transplanting of bare rape seedlings are intertwined, which makes it difficult to separate seedlings and the conventional seedling separation device is easy to damage the roots and stems, a seedling separation process of artificial assisted row placement + flexible clamping transportation + intermittent dial separation was proposed, and a flexible clamping seedling separation device was developed. A flexible clamping seedling separation device was designed. The material characteristics of rapeseed bare seedlings during the optimal transplanting period were measured, determining the transverse conveyor belt width of the flexible clamping mechanism to be 216 mm. The dynamics of rapeseed bare seedlings during the flexible clamping and conveying process were analyzed, and the feed inlet angle was determined to be 30°. The trajectory of manual seedling separation was simulated, and rigid-body guidance of the separation mechanism was analyzed based on Burmester theory, resulting in the determination of a crank length of 32 mm and a rocker length of 50 mm. Motion parameter matching analysis was conducted for the flexible clamping mechanism and the seedling separation mechanism, determining the flexible clamping mechanism's conveying speed to be 25~55 mm/s and the separation mechanism's poking frequency to be 50~120 times/min. An orthogonal bench test was conducted with the separation outlet angle, the conveying speed of the flexible clamping mechanism, and the poking frequency of the separation mechanism as test factors, and the seedling separation success rate and seedling damage rate as evaluation indices. The test results showed that the optimal parameter combination was a separation outlet angle of 55°, a conveying speed of 48 mm/s for the flexible clamping mechanism, and a poking frequency of 85 times/min. Under these conditions, the seedling separation success rate and damage rate of the device were 89.06% and 3.91%. The field test showed that each mechanism of the seedling separation device ran smoothly, and the operation performance met the requirements of mechanized transplanting of bare rape seedlings. The research results can provide technical reference for the design of automatic transplanting equipment for bare rape seedlings.

Abstract:In view of the problems such as the lack of deep planting mode and harvesting equipment for scallions on flat land in Qinghai region, as well as the high cost and low efficiency of manual harvesting, a crawler self-propelled cross-row scallion harvester was designed. This machine integrated multiple functions such as depth limit excavation, clamping and conveying, and orderly laying, and can complete the operations of trenching, scallion excavation, conveying and collecting in one go. Low-resistance harvesting was achieved by using a U-shaped plough for soil breaking and excavation structure in combination with a rotating knife group for trenching. The overall passability and stability of the machine were enhanced through a hydraulic drive system. The parameters of the hydraulic system were matched and optimized, and the selection and design of the hydraulic motor and variable pump were completed. Meanwhile, the flow-pressure characteristic tests of the hydraulic pump and the flow characteristic tests of the hydraulic motor were carried out. Performance tests showed that under the start-up and high-load conditions of the harvester, the flow rate and pressure of the hydraulic pump and the flow rate of the hydraulic motor all underwent sudden changes, and then entered a stable state, indicating that the system provided stable energy supply, responded quickly, and had a good pump-motor matching, which can adapt to different high-altitude working conditions. The results of field trials showed that the net working efficiency of this machine was 0.35 hm2/h, the damage rate was 1.22%, the loss rate was 0.81%, the success rate of loading and unloading was 98.63%, and the effective excavation rate was 97.84%. All indicators were superior to the industry standards, verifying the structural feasibility and adaptability of this machine, and providing technical support and equipment foundation for the mechanization of scallion harvesting in plateau areas.

Abstract:The harvesting of tobacco leaves in China mainly depends on manual harvesting and self-transport of the harvester. The low level of mechanization, high labor intensity and high cost of tobacco leaf harvesting seriously affect the efficiency of tobacco leaf harvesting. Therefore, for the small-area and unstructured harvesting scenarios of tobacco fields, a predictive scheduling model for human-machine collaborative tobacco harvesting and transshipment was proposed based on a discrete-time hybrid system, which modeled the harvester's operation behavior as a discrete event, and then predicted its future transshipment demand, so as to drive the self-developed tobacco leaf transshipment robot to carry out active and forward-looking path planning, replacing the traditional passive response mode. Non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) was used to solve the model and generate the corresponding human-machine collaborative optimization scheduling scheme. The simulation experiment verified the performance of the NSGA-Ⅱ algorithm in multi-objective optimization scheduling and the effectiveness of the predictive scheduling model of harvesting and transshipment by analyzing the influence of the ratio of the harvester and the transshipment robot and the threshold FR_tr of the harvester request on the harvesting efficiency. The results showed that when the number ratio of the transfer robot to the harvester was 1:2 and the FR_tr was set to 0.7, the non-productive operation time of the human-machine collaborative harvesting and transfer was reduced by 88.6% compared with that of the manual harvesting and transfer, and the harvesting efficiency was 95.7%. The results of simulated tobacco field experiments showed that when the number ratio of transfer robot to harvester was 1:1, the non-productive operation time of tobacco leaf harvest and transfer based on predictive scheduling strategy was 48.58 s, and the harvest efficiency was 83.1%. Compared with manual harvesting and reactive scheduling strategy, the harvest efficiency of tobacco leaf harvest and transfer was increased by 16.8 and 8.5 percentage points. When the number ratio of transfer robot to harvester was 1:2, the non-productive operation time of tobacco harvest and transfer based on predictive scheduling strategy was 53.14 s, and the harvest efficiency was 82.5%. The harvest efficiency was 14.6 and 9.9 percentage points higher than that of manual harvesting and reactive scheduling. The results can provide a reasonable and effective scheduling scheme for human-machine collaborative operation of tobacco leaves.

Abstract:Spatial stratification is a key link in the monitoring and evaluation of cropland quality. Taking Horqin Left-Wing Middle Banner as an example, a method for spatial heterogeneity stratification in arable cropland quality was proposed based on categorical principal component analysis. The weights of cropland quality indicators were quantified by categorical principal component analysis method, and the indicators were classified into important and general indicators by combining the mean-standard deviation grading method. The important indicators were classified by using a classification and gradation method according to the corresponding national standard, and the spatial clustering stratification of the general indicators were carried out based on the two-step clustering method. The spatial heterogeneity stratification of cropland quality was achieved by constructing the stratification and fusion rules. The stratification results were evaluated in both quantitative and qualitative terms by using geographical detector and consistency testing method, respectively, and were compared with the full-indicator clustering stratification method. The results showed that the indicators of cropland quality in Horqin Left-Wing Middle Banner were divided into important indicators and general indicators, and the results of spatial heterogeneity stratification for cropland quality were high, medium-high, medium, medium-low, and low strata, and the corresponding proportions of stratified areas were 12%, 22%, 28%, 24% and 14%, respectively. The q values of the stratification results calculated using the geographic detector were 0.74 and 0.67, and the stratification results were consistent with the evaluation data of cropland quality and the grades of cropland quality. The effect of spatial heterogeneity stratification was better than that of the full-indicator clustering stratification method. The developed spatial heterogeneity stratification method of cropland quality can highlight the contributions of important indicators to cropland quality, and provide a technical support for rapid screening and dynamic monitoring of cropland quality. The developed method for spatial heterogeneity stratification in arable cropland quality further highlighted the contribution of key indicators to soil fertility, thereby providing technical support for rapid screening and dynamic monitoring of arable cropland quality.

Abstract:The research on the relationship between the changes in dietary structure of Chinese residents and the scale of demand for arable land resources can provide scientific basis and decision-making reference for ensuring the diverse and healthy dietary and nutritional needs of residents, maintaining national food security, and promoting the sustainable use of arable land resources. Methods such as dietary farmland footprint, LMDI decomposition, and scenario analysis were used to analyze the impact of changes in Chinese residents' dietary structure on farmland demand from 2013 to 2023. Four scenarios were set up to provide reference for the direction of future changes in residents' dietary structure. The results indicated that the total food consumption of Chinese residents was significantly increased. The dietary structure of residents was transitioning from a "plant-based dominant" to an "animal plant balanced" dietary structure, and the differences in dietary structure between urban and rural residents were gradually narrowing. The total dietary land footprint of Chinese residents and the per capita dietary land footprint both showed a fluctuating increase trend, with significant differences in the total dietary land footprint between urban and rural residents. Dietary structure factors became the core driving force for increasing demand for arable land resources. Based on a comprehensive analysis of four scenarios, China's arable land resources were currently in a state of overall scarcity. By adjusting the dietary structure, such as transitioning to Scenario D (ecological and nutritional double excellent dietary structure), the pressure on arable land resources can be effectively alleviated.

Abstract:With the rapid and accurate acquisition of rural road information, essential data are provided for agricultural machinery operation navigation and high-standard farmland construction evaluation. To address challenges such as occlusion, small spectral differences, and diverse geometric shapes in complex rural environments, an improved semantic segmentation model, SMC_ResUnet, was proposed based on the Res-Unet architecture with controllable encoding depth. Using ResUnet50 as the backbone, the strip pooling module was introduced in the encoder to enhance the extraction of long-range spatial features of rural roads. Additionally, the CA attention module was incorporated into the residual blocks to improve the perception of subtle road features through positional information, thereby reducing omission errors. A hybrid pooling module was integrated into the encoder-decoder pathway, combining strip pooling and standard pyramid pooling to balance the recognition of rural roads with diverse shapes while minimizing false positives. The proposed model was validated on a high-resolution rural road dataset from Nenjiang City, Heilongjiang Province. Experimental results demonstrated that SMC_ResUnet outperformed comparison models, achieving an average accuracy of 98.58%, recall of 83.40%, MIoU of 78.06%, and F1-score of 85.89%, with an overall accuracy of 97.41% in large-scale rural road extraction. Ablation experiments confirmed the effectiveness of each module in addressing specific challenges of rural road identification. The model's generalization capability was further verified by using the Deep Globe Road Extraction Dataset. The research result can provide a valuable reference for acquiring rural road information and guiding agricultural machinery navigation.

Abstract:Accurate and timely crop yield estimation is of great significance for ensuring food security and promoting sustainable agricultural development. Focusing on the Guanzhong Plain in Shaanxi Province, selecting the vegetation temperature condition index (VTCI), leaf area index (LAI), and fraction of photosynthetically active radiation (FPAR) as remotely sensed feature parameters closely related to winter wheat growth. By combining the advantages of long short-term memory network (LSTM) in processing time series data with the capability of the attention mechanism (AM) to extract key information, an LSTM-AM deep learning model for winter wheat yield estimation was constructed. The results indicated that the coefficient of determination (R2) for the LSTM-AM model was 0.65, with a root mean squared error (RMSE) of 496.43 kg/hm2 and a mean absolute percentage error (MAPE) of 7.31%, and a normalized root mean squared error (NRMSE) of 0.11. Compared with the LSTM model (R2=0.60, RMSE is 527.81 kg/hm2, MAPE is 7.85%, NRMSE is 0.15), the LSTM-AM model demonstrated higher estimation accuracy and mitigated the underestimation of high yields and overestimation of low yields observed in the LSTM model. To further enhance the model's interpretability, the permutation feature importance (PFI) method for an in-depth explanation of the estimation results was utilized. The findings indicated that FPAR from late April to early May, VTCI from late March to early May, and LAI from late April to late May significantly influenced the final yield of winter wheat. Therefore, the LSTM-AM model not only exhibited high estimation accuracy but also enhanced interpretability through the PFI method, providing a valuable reference for subsequent winter wheat yield estimation.

Abstract:Timely acquisition of soil moisture content (SMC) of edible roses is crucial for achieving precision irrigation. Unmanned aerial vehicle (UAV) multispectral technology was adopted, and through field experiments, SMC data at different soil depths and corresponding UAV multispectral images were collected during the flowering period of edible roses, with vegetation indices and texture features having strong correlations with crop parameters established. Grey relational analysis (GRA) was used to evaluate the influence degree of vegetation indices and texture features on SMC at each soil depth, and parameters with significantly correlated coefficients to SMC at each depth were selected as model input variables (Combination 1: vegetation indices;Combination 2: texture features;Combination 3: vegetation indices and texture features). Random forest (RF), extreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) models were employed to model SMC at each soil depth respectively. The results showed that the inversion effect of the 0~10 cm soil layer was overall better than that of the 10~20 cm and 20~30 cm soil layers, the mean coefficient of determination (R2) of the validation sets of each model for the 0~10 cm layer was 0.12~0.21 higher than that of the deeper soil layers, while the root mean square error (RMSE) and mean relative error (MRE) were reduced by 0.8~1.5 percentage points and 3~5 percentage points respectively. At the optimal soil depth of 0~10 cm, the GBDT model with Combination 3 as input performed the best, with R2=0.8363, RMSE of 1.28%, and MRE of 5.06%, which was superior to the RF and XGBoost models. Finally, the SHapley Additive exPlanations (SHAP) analysis method was used to reveal the importance of spectral vegetation indices (SVI) and mean (MEA) in constructing the prediction model and clarify the influence of the top-ranked SHAP values, and the results can provide a basis for UAV multispectral monitoring of SMC in edible roses and a reference for the rapid evaluation of crop growth under the condition of integrated water and fertilizer management.

Abstract:Aiming at the problem that single visual sensor SLAM technology has low accuracy and poor reliability in dynamic environment, which leads to the inability to accurately estimate the camera pose, a visual SLAM algorithm based on weighted static feature points and selective optimization (CW-SLAM) was proposed. Firstly, dynamic feature point detection was added to the front end. After using the GC-RANSAC algorithm to separate the inner/outer points and fit the optimal basic matrix, a weighted static feature detection method was designed to eliminate the error constraints from the dynamic features, improve the matching accuracy, and use the stable features for back-end pose optimization. Secondly, the factor graph model was used to construct a new structure with vision as the main system and IMU as the auxiliary system. By introducing the auxiliary system IMU odometer factor to constrain the main system error, and receiving the VIO factor to realize the motion prediction and pose optimization. Finally, a selective optimization strategy was proposed to eliminate the influence of temporary static targets. After clustering the loopback key frames, the selective optimization of the constraint group was established according to the factor graph optimization model to filter out the false positive loopback hypothesis. Compared with the classical SLAM algorithm, the effectiveness of the algorithm was verified on the TUM public dataset and in the real environment. The experimental results showed that the algorithm can effectively suppress the influence of dynamic and temporary stationary targets on pose estimation, and improve the accuracy and reliability.

Abstract:Accurate estimation of the harvesting pose of tomato peduncles is critical for achieving low-damage, high-efficiency robotic harvesting. The slender structure and diverse growth orientations of tomato peduncles make single-fruit harvesting via peduncle cutting particularly challenging. A method for estimating the harvesting pose of tomato peduncles was proposed by combining instance segmentation and point cloud analysis. Firstly, a dataset for instance segmentation of tomato fruits and peduncles was constructed. The YOLO v8s-seg model, demonstrating balanced performance during evaluation, was then selected for segmenting tomato fruits and peduncles. Nextly, the midpoints of the skeleton lines in the predicted peduncle regions were extracted as harvesting points. A linear fitting process was applied to the spatial point cloud corresponding to the peduncle skeleton lines to determine the peduncle growth direction, thereby generating a harvesting pose that aligned the end-effector perpendicular to the peduncle. Additionally, the ripeness of the fruit was determined by calculating the percentage of red area within each fruit's segmented region, and a greedy matching method was used to pair fruits and peduncles to enable selective harvesting. A tomato harvesting experimental platform was set up to validate the harvesting pose estimation method in a greenhouse. Experimental results showed that the trained instance segmentation model achieved a precision, recall, and mAP of 85.2%, 80.6%, and 86.9% on the test set, respectively. The accuracy of the proposed ripeness recognition method reached 97.17%, and the success rate of the fruit-peduncle matching method was 92.25%. For forward-growing fruit clusters, the detection rates of peduncles and fruits were 93.63% and 96.36%, respectively. The harvesting point identification and pose estimation accuracies were 96.11% and 89.32%, respectively, with an overall success rate of reaching the harvesting point of 60.91%. The proposed method demonstrated feasibility for peduncle-cutting tomato harvesting tasks, providing a reference for autonomous operation of tomato-picking robots in greenhouse environments.

Abstract:This paper presents an autonomous navigation system for unmanned pig farming, using multi-source information fusion. It integrates LiDAR and IMU data via the Fast-LIO2 algorithm for odometry, constructs maps with a factor graph-optimized loop closure method, and localizes using both map registration and reflective pole matching, outperforming Adaptive Monte Carlo Localization. Path planning is handled by Dijkstra's algorithm for global paths and the Time-Elastic Band for local paths, ultimately realizing autonomous navigation in pig house scenarios. Experimental results show that the system achieves mapping with a maximum absolute error of 0.077 m and relative error of 3.79%, with higher accuracy compared to mapping algorithms without loop closure detection. Localization accuracy averages 0.066 m in X and 0.052 m in Y for map registration, and 0.046 m in X and 0.042 m in Y for reflective pole matching. When the mobile platform navigates at a speed of 0.3 m/s, the maximum lateral deviation between the actual navigation points and the target points is 0.09 m, the maximum longitudinal deviation is 0.089 m, and the average heading angle deviation is 7.06°. The system meets high-precision requirements for mapping, localization, and navigation in swine houses, supporting unmanned pig farming operations.

Abstract:Sow body condition is an important indicator for evaluating its production performance and guiding accurate feeding. In recent years, machine vision-based body condition scoring has received much attention. However, since most of the current studies rely on two-dimensional images and human-defined geometric features, the two-dimensional images are difficult to reflect the rich three-dimensional body features of the pig body, so the dimension of feature extraction is limited. At the same time, the limited number and dimension of human-defined features make it difficult to include detailed features related to the body condition, resulting in a large amount of information loss leading to a low scoring accuracy. A 3D point cloud dataset was constructed by deep vision technology, and a class-balanced loss function-representative surfaces (CBLoss-RepSurf) sow body condition scoring model was proposed based on the class-balanced loss function, which automatically extracted high-dimensional features related to body condition scoring, and realized end-to-end sow body condition scoring. The end-to-end sow body condition scoring was realized by automatically extracting high-dimensional features related to body condition scoring. Tested with 3,093 point clouds of 57 sows at different stages, the results showed that the accuracy of the CBLoss-RepSurf model was improved by 1.54 percentage points compared with the RepSurf model without class-balanced loss, and the scoring accuracies of sows at empty, late gestation, and weaning stages were 94.87%, 92.50%, and 85.50%, respectively, which was comparable to that of the sows scored based on the features of body mass and body size. Compared with the body mass and body size characteristics of the sow body condition scoring method, the accuracy was 6.30 percentage points, 7.00 percentage points and 0.36 percentage points higher, which can achieve more accurate scoring for different stages of sow body condition and provide feasible method support for the automatic scoring of sow body condition in large-scale aquaculture.

Abstract:Aiming to address the high labor intensity, low efficiency of traditional pig body size measurement, and the lack of edge computing methods for automated measurement, an automatic pig body size measurement approach tailored for the Jetson Orin NX edge platform was proposed. A time-series dataset comprising 10,413 posture images and 9,555 keypoint images was constructed and partitioned by enclosure. To achieve a lightweight yet high-performing model, an improved YOLO 11nds-based architecture was developed for posture and keypoint detection. The network width coefficient was reduced to compress model size, and DySample dynamic upsampling was incorporated to reduce computational redundancy and enhance cross-scale feature interactions, enabling efficient feature extraction and fusion with fewer parameters. A three-stage weight conversion process from pt to ONNX to engine was employed to reconstruct the network structure, optimize inference speed, and deploy the model on Jetson Orin NX. The SGBM algorithm was integrated to obtain 3D coordinates of key measurement points for body size estimation. Experimental results demonstrated that the improved YOLO 11nds model achieved superior accuracy, inference speed, and parameter efficiency. For posture detection, it reached 99.17% of accuracy, an F1 score of 94.26%, an inference speed of 156.25 f/s, and 8.2×10? parameters. For keypoint detection, it achieved 97.50% OKS, 97.06% PCK, 169.49 f/s, and 8.7×10? parameters. Optimizations improved inference speeds by 265.47% and 362.71% for the two tasks. Automatic measurements using videos from January 28 and March 9, 2024, showed mean relative errors of 1.58%, 1.70%, 2.17%, 2.00%, and 2.93%, and 1.90%, 2.18%, 2.90%, 3.10%, and 2.80% for body length, body width, rump width, body height, and rump height compared with that of manual measurements. This method demonstrated high accuracy and real-time performance, efficiently operating on Jetson Orin NX and providing a reliable solution for automated pig body size measurement.

Abstract:Aiming to address the challenges of diverse scales, similar visual characteristics, and high model complexity in detecting grape diseases in field environments, a novel detection method was proposed based on YOLO v5s-RCW. Using YOLO v5s as the baseline network, the receptive-field attention convolution (RFAConv) was introduced into the feature extraction structure to dynamically generate spatial features in the receptive field matching the size of the convolution kernel and assign unique attention weights to each receptive field in the network, which effectively mitigated the parameter sharing limitation of traditional convolutional operations in handling disease targets of varying sizes. Additionally, the compatible intersection over union (CIoU) loss function was replaced with the wise intersection over union (WIoU) loss function, optimizing the penalty mechanism for anchor boxes of different sizes and significantly reducing performance fluctuations caused by variations in disease scale. The results demonstrated that compared with YOLO v5s, the model improved accuracy, recall, and mean average precision by 7, 6, and 1.7 percentage points, respectively. The model's feature discrimination capability was further enhanced by integrating the convolutional block attention module (CBAM) into the backbone network, while reducing the number of parameters by 18.3%. The YOLO v5s-RCW model was deployed on a cloud server, and users can call the detection function transformed into an application programming interface (API) through the WeChat mini program, enabling convenient detection of grape diseases. The average inference time of the model was 13.9 ms, which improved the efficiency and accuracy of disease detection and provided technical support for grape disease detection.

Abstract:In container-based vertical agricultural production systems, supplementary lighting is a key technical approach for regulating crop growth, optimizing resource utilization, and improving production efficiency. However, most existing lighting control strategies rely on fixed time cycles or empirical parameters, lacking effective perception of crop growth stages, which limits their adaptability and precision under dynamic growth conditions. To address these challenges, a lettuce growth stage recognition model named self attention based-YOLO (SAB-YOLO) was proposed to realize accurate and automated identification of crop growth periods in complex visual environments. The proposed model was developed by introducing multiple structural improvements to the YOLO v5 framework. Firstly, the conventional convolutional backbone was replaced with a Swin Transformer network based on self-attention mechanisms, which enhanced the ability of the model to capture long-range dependencies and global semantic information. Secondly, an asymptotic feature pyramid network (AFPN) with denser cross-layer connections was adopted in the Neck to strengthen multi-scale feature fusion and improve robustness to large scale variations among different growth stages. Furthermore, a convolution transformer fusion (CTF) module that integrated convolutional operations with self-attention was designed and embedded into the detection head to further enhance global contextual representation. In addition, the Inner-SIoU loss function was employed to improve bounding box regression accuracy and accelerate model convergence. Experimental results on a mixed dataset collected from open-source platforms and a container-based plant factory showed that the proposed model achieved a precision of 88.5% and an mAP_0.5 of 92.1%, outperforming the baseline YOLO v5 model. Furthermore, an intelligent supplementary lighting system based on growth stage recognition was designed and validated, demonstrating the practical applicability of the proposed method in precision agriculture.

Abstract:Sugarcane is a globally important crop for both sugar production and bioenergy, and it is widely cultivated in tropical and subtropical regions. Effective disease diagnosis is essential to ensuring agricultural productivity and economic returns. In response to challenges posed by complex field environments, such as uneven lighting, low recognition accuracy, and limited detection efficiency, a novel algorithm: sugarcane disease classification algorithm using global-local attention mechanism (SDCA-GLAM) was proposesd. To enhance model capacity, the linear projection layers in a modified Vision Transformer (ViT) were replaced with deformable convolution modules, enabling adaptive extraction of lesion textures and leaf-edge information. Re-parameterized convolution was incorporated to strengthen spatial positional encoding, and deep convolutional modules were embedded in the multilayer perceptron to extract high-dimensional semantic features. To improve both accuracy and model efficiency, a parallel global-local self-attention architecture was designed. The local branch leveraged window attention to refine fine-grained textures, while the global branch reduced the spatial dimensions of key/value vectors via pooling and aggregating critical region information using a hyperparameter α. Finally, LayerNorm was replaced with BatchNorm to reduce the memory and time overhead caused by frequent reshaping. Experimental results on an 11-class sugarcane leaf dataset demonstrated that SDCA-GLAM achieved an accuracy of 88.26%, a throughput of 1,620 images per second, and a model size of 2.76×10^7. The proposed method outperformed mainstream models in both accuracy and efficiency, making it suitable for real-time mobile deployment in field diagnosis of sugarcane conditions.

Abstract:Aiming to address the limited classification performance of non-canonical short texts for agricultural technology consultation, which stemed from semantic sparsity and insufficient feature representation. Based on the Jilin Provincial Agricultural Information Service Platform, it constructed a dataset of Chinese agricultural technology short texts under real scenarios (average length <30 characters) and proposed a fusion optimization model (ErcNet) based on deep semantic reconstruction and dual-driven feature enhancement. Differentiated from traditional methods that only improved single structures for standard agricultural texts, this work achieved dual breakthroughs in the entire agricultural technology field: firstly, it adopted a fixed weighted fusion strategy for the last three encoder layers of pre-trained language models, built a deep semantic representation completion mechanism, and this mechanism effectively resolved semantic fragmentation in spontaneous questions from farmers;secondly, it designed a coordinated optimization module combining multi-scale convolution and efficient channel attention (ECA), established a "semantic density enhancement-discriminative boundary sharpening" dual-driven architecture during feature extraction, and this architecture significantly improved the feature discriminability of non-canonical short texts. Finally, it proposed a global-local feature fusion mechanism, which further complemented semantic extraction. Experimental results showed that on the self-constructed dataset, the model outperformed ERNIE, TextCNN, and other models, with precision at 96.82%, recall at 96.96%, and F1 score at 96.88%;in cross-domain testing on the THUCNews dataset, it achieved 91.70% accuracy, which verified the method's generalization ability.

Abstract:Aiming to improve the accuracy of regional groundwater depth prediction, a CEEMDAN-hybrid algorithm-LSTM prediction model was proposed. Based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method, the groundwater depth data from 15 farms under the jurisdiction of Jiansanjiang Branch office were decomposed into five modal components, effectively reducing the complexity of the input data. Meanwhile, a hybrid optimization algorithm combining the red fox optimization (RFO) algorithm and the whale optimization algorithm (WOA) was employed to optimize key parameters of the long short-term memory (LSTM) neural network model, including time step, number of hidden units, batch size, and learning rate, thereby further enhancing the model's prediction accuracy. Monthly precipitation and paddy field irrigation well volume were used as input factors for the LSTM model to separately predict the five modal components, and the final groundwater depth prediction was obtained by summing the predicted values of each component. The results showed that compared with the back propagation (BP) neural network model and the recurrent neural network (RNN) model, the CEEMDAN-hybrid algorithm-LSTM model reduced the root mean square error (RMSE) by more than 43%, and increased the coefficient of determination R2 and Nash-Sutcliffe efficiency coefficient (NSE) by more than 18%. Prediction results indicated that from 2023 to 2027, the overall groundwater depth in the Jiansanjiang Branch office area would vary by up to 6.22%, with southern farms generally having greater groundwater depths than northern farms.

Abstract:Aiming to comprehensively implement the water management concept of "prioritizing water conservation and spatial equilibrium", and support the coordinated development of water resource utilization and ecological security, taking Qingtongxia Helan County Irrigation Area in Ningxia as object, the potential direction of agricultural water conservation focusing on the two core constrains—the regional available water resources and the rationale groundwater depth required for ecological security was analyzed. By employing a water-land resources balance allocation model and setting ten scenarios, potential directions for advanced agricultural water-saving intensification were explored. The results indicated that the following measurement should be implemented to achieve spatially balanced development and synergistic agricultural water-saving, if implementing single water-saving measures: priority should be given to reducing rice planting area and controlled irrigation, followed by improving canal system water utilization efficiency and expanding high-efficiency water-saving irrigation area. The optimal control ranges for these measures were: rice planting reduction ratio should be controlled between 50% and 75% with the remaining area under controlled irrigation;canal system water utilization coefficient should be maintained at 0.64;regarding high-efficiency water-saving irrigation promotion, the area proportion in Yanghuang and Yellow River diversion regions should not exceed 80% and 35%, namely maintaining 28.48 km2 and 132.20 km2, respectively.

Abstract:Efficient use of rainfall is an effective strategy for saving irrigation water. To ensure rice yield and enhance rainfall utilization efficiency in rice cultivation, the water-saving irrigation strategies were proposed based on short-term weather forecast, considering the drought and flood tolerance characteristics of rice. Field experiments were then conduced. In strategy Ⅰ, irrigation was applied only if no drainage was forecasted within five days after irrigation. Otherwise, it was withheld. If there were three consecutive days of decision not to irrigate, then mandatory irrigation would occur on the fourth day. Strategy Ⅱ followed the same rules but applied only half the irrigation amount. Using field trial data from 2023 to 2024, a quantitative comparative analysis was conducted between water-saving irrigation strategies and conventional irrigation strategies. Results showed that strategy Ⅰ and strategy Ⅱ reduced irrigation water use by 11.385% and 22.935% on average compared with the conventional strategy over two years, without reducing yield. Rainfall utilization was increased by 4.71 percentage points and 6.045 percentage points, respectively. In terms of irrigation frequency, strategy Ⅰ reduced two irrigation events in both years, while strategy Ⅱ kept the same events as the conventional strategy in 2023, but required nine additional irrigation events in 2024. Strategy Ⅱ significantly improved rainfall utilization by applying small dose irrigations with more events. In conclusion, the proposed weather forecast-based strategies, which involved delaying irrigation during drought periods to maximize short-term rainfall, significantly reduced the risk of yield loss and improved rainfall utilization. These strategies ensured rice yield, saved water, and reduced emissions, offering significant benefits for smart irrigation decision-making in southern China.

Abstract:Multi-point root zone micro-irrigation (MRMI) is a promising technology for precision water management, particularly for shallow-rooted crops in soils with high water-holding capacity. Aiming to address the weak stress tolerance of shallow root systems in native macadamia trees, a condition resulting from low-phosphorus strategies. By utilizing the high water-holding capacity of Yunnan red soil, a targeted, 4-point spatial micro-irrigation strategy was proposed and validated to steer and remodel root morphology in seedlings. Field greenhouse experiments indicated that under a time-differentiated irrigation regime (The total daily irrigation amounts were 1.08 mm, 0.83 mm, and 0.58 mm for months of 0~4, 5~8, and 9~12, respectively.), both MRMI and surface drip irrigation (SDI) created vertical soil moisture gradients. However, the gradients under MRMI were more stable and concentrated. At 1.08 mm and 0.83 mm application rates, MRMI established a suitable soil moisture range (0.30~0.35 cm3/cm3) in the middle and deep layers (20~40 cm). At the 0.58 mm rate, the 40 cm soil layer also remained consistently within this range, thereby creating effective moisture-driven conditions for horizontal expansion and deep vertical penetration of the roots. At the 12th month, root sample metrics showed that indicators for MRMI in the shallow and deep layers were significantly different from those for SDI (p<0.01), while root length density (RLD) and root volume (RV) in the middle layer showed significant differences (p<0.05). Compared with SDI, MRMI reduced the horizontal spread of the root system by 17.95% while increasing its vertical penetration depth by 23.53%. This fostered the evolution of a multi-tiered, near-bell-shaped root architecture, characterized by moderate horizontal spread and significant vertical penetration, which enhanced the root system’s dual capacity for absorbing both soil water and nutrients. The research presented an innovative irrigation-based strategy to overcome morphological defects in crop roots, offering new insights for cultivating stress tolerance in shallow-rooted, sparsely planted crops.

Abstract:Insufficient accuracy in dynamic temperature field reconstruction significantly constrains the responsiveness and control efficacy of precision feeding decision systems. To address this critical limitation, a novel dynamic data fusion algorithm was proposed based on an improved support function (Fast-NF). The algorithm constructed a spatio-temporal weight optimization model for sensor data by coupling dynamic time warping (FastDTW) with a multi-layer polynomial decay mechanism. This approach effectively overcame the technical limitations of traditional methods, particularly concerning computational efficiency and compensation for missing data. Experimental results demonstrated that compared with conventional Gaussian-based methods, the root mean square error (RMSE) of the fusion temperature was reduced from 0.436 7℃ to 0.387 5℃, a decrease of 11.3%. The window calculation time was shortened from 14.568 8 s of the standard DTW-NF algorithm to 5.839 4 s, and the efficiency was improved by 59.9%. Consequently, the developed dynamic temperature field reconstruction technology, leveraging this method, successfully achieved dimensionality elevation from discrete monitoring points to a continuous field domain. This advancement can provide robust support for precision feeding decision systems, offering a significant improvement in reconstruction accuracy and computational efficiency for practical agricultural applications. The core innovation lied in the synergistic Fast-NF mechanism integrating FastDTW and decay weighting.

Abstract:Aiming to address the issues of poor slicing quality and frequent breakage in sweet potato strip cutting machines, a segmented sweet potato strip cutter that integrated slicing and strip cutting functionalities was developed. The mechanical and kinematic processes involved in sweet potato slicing and strip cutting were analyzed, leading to the design of a linear reciprocating slicer, a circular blade disk strip cutter, and associated feeding and conveying devices, with key structural parameters identified. Experiments were conducted by using crank speed and strip cutting speed as variables, and breakage rate and section smoothness as metrics. A second-order regression orthogonal rotational composite design was employed for strips with cross-sections of 12 mm×12 mm and 24 mm×24 mm. Variance analysis was performed by using Design-Expert software, and response surface analysis was used to determine the effects of interaction factors on the metrics. Optimal experimental conditions were identified and validated. The results indicated that for 12 mm×12 mm strips, crank speed had a more significant impact on breakage rate, while strip cutting speed more heavily influenced section smoothness. The optimal quality was achieved at a crank speed of 16.40 r/min and a strip cutting speed of 131.30 m/s, resulting in an 8.34% breakage rate and 95.94% section smoothness. For 24 mm×24 mm strips, strip cutting speed was the predominant factor affecting both breakage rate and section smoothness. The best quality was observed at a crank speed of 44.10 r/min and a strip cutting speed of 183.10 m/s, with a breakage rate of 5.63% and section smoothness of 91.19%, meeting the technical requirements.

Abstract:Firmness is a key indicator for evaluating the ripeness of kiwifruit, which is essential for fruit maturity evaluation and commercial grading. It’s aimed at the challenges of poor 'ready-to-eat' quality and the lack of low-cost grading technology for domestic kiwifruit. Focusing on the 'Cuixiang' kiwifruit, a novel method combining vision-based tactile sensing and deep learning techniques for kiwifruit firmness detection was proposed. An integrated desktop automation device, which combined feeding, conveying, detecting, and grading functions, was developed to enable non-destructive firmness evaluation and 'ready-to-eat' maturity grading for small to medium batches of kiwifruit. The fabrication method, detection principles, and hardware system construction of the vision-based tactile sensor, as well as the software control process were firstly outlined. A deep learning model, ResNet18_CBAM-LSTM with the CBAM attention mechanism, was constructed and trained. A 'ready-to-eat' grading method was proposed, and its correlation with firmness was analyzed and used as the basis for the device's grading process. Subsequently, the prototype device was evaluated, and non-destructive testing was assessed by using Mann-Whitney U significance tests based on kiwifruit respiration intensity. Experimental results showed that the proposed vision-based tactile sensing method was repeatable and stable, with a contact force variation coefficient of 0.54%. The neural network's predicted firmness and the actual firmness of the kiwifruit yielded an R2 value of 0.81 and an RMSE of 1.39 N, demonstrating accurate firmness assessment. The optimal 'ready-to-eat' firmness range for the test kiwifruit was found to be between 8.72 N and 14.28 N. Using this reference, the prototype's fruit feeding success rate was 89.92%, with a grading accuracy of 90.16% and sorting efficiency of 4.12 s per fruit. Under Mann-Whitney U testing, the P-value for the difference between the test and control groups after grading by the device was greater than 0.05. Overall, the prototype demonstrated stable operation and effective detection, achieving non-destructive grading. The research result can provide a reference for the firmness detection and 'ready-to-eat' grading of domestic kiwifruit.

Abstract:Aiming at the problems of insufficient adaptability to ridge-planting parameters and limited endurance in existing electric agricultural machinery, a dual-power crawler tractor was designed. Based on operational requirements, the key components were designed and matched. An electronic architecture centered on a dual-power system, consisting of a power battery and greenhouse grid power was proposed, along with a corresponding power switching control strategy. The crawler tractor's virtual prototype model was established by using RecurDyn software, and dynamic characteristic simulations were conducted for its straight-line driving and steering conditions. The performance testing of the prototype showed that the pre-charging system can raise the DC bus voltage to the expected value within 410 ms. During the switching process between the power battery and greenhouse grid power, the maximum voltage fluctuation rate was 9.8%, with switching time of 700 ms. The insulation resistance of the entire high-voltage system to ground was greater than 30 MΩ, demonstrating good electrical stability and operational safety. Under typical greenhouse soil conditions, the average deviation rates during straight-line travel were 4.8% in power battery mode and 5.1% in greenhouse grid power mode, with minimum turning radius of 1 168.5 mm. Under rotary tillage conditions, the maximum speed fluctuation rate of the power take-off (PTO) motor was 5.9%, and the average speed of the PTO shaft was 541 r/min, both meeting national standards. Field tests in the facility showed that the overall energy loss rate in greenhouse grid power mode was 5.2 percentage points higher than that in power battery mode. The dual-power tractor exhibited complementary advantages in continuous operation capability and mobility, providing an effective solution for power equipment in protected agriculture.

Abstract:Aiming to address the problem of excessive slip of the driving wheels in a distributed drive electric tractor operating on split-μ road surfaces, which adversely affects vehicle stability and traction performance, a torque coordination and distribution control strategy aimed at reducing wheel slip ratio and enhancing overall vehicle stability was proposed. A longitudinal motion controller was designed to calculate the required system output torque, while a traction anti-slip controller and a yaw moment controller were developed accordingly. At the torque allocation layer, driving force distribution models were established for both the wheel-extrication condition and the split-μ road condition, and the optimal torque distribution was obtained by using quadratic programming. For complex operating conditions such as split-μ roads, a wheel coordinated steering strategy based on the pure pursuit model was further investigated to mitigate the effects of yaw moment and heading angle variations on the tractor. To validate the effectiveness of the proposed control strategy, both simulation studies and field experiments were conducted. Experimental results demonstrated that under wheel-extrication conditions, the proposed control strategy achieved an output torque of 23.16 N·m and an extrication time of 1.5 s, representing reductions of 14.48% and 44.44%, respectively, compared with conventional PI control. These improvements ensured sufficient power reserve during extrication and significantly enhanced system responsiveness. Under split-μ road conditions, the proposed strategy enabled active regulation of system torque output, yielding average driving wheel slip ratios of 0.121 7, 0.111 9, 0.140 3, and 0.136 8. Compared with sliding mode control, the lateral deviation was maintained within -0.25 m to 0.25 m, allowing the tractor to track the desired speed while maintaining the intended trajectory. Overall, the proposed control strategy effectively improved the driving stability of the tractor under complex road conditions.

Abstract:A revolutionary three-degree-of-freedom (3-DOF) parallel mechanism was introduced, which was capable of dual-mode operation with partially decoupled motion. This mechanism can generate two distinct types of output motion—pure translational (3T) and two-translational-one-rotational (2T1R)—without the need to alter its topological structure during operation. This unique capability allowed the mechanism to adapt to different technological processes and fulfill a wide range of industrial requirements. Then a thorough analysis of the mechanism's topological, kinematic, and dynamic performances in both operational modes was conducted. The topological characteristics of the mechanism were carefully examined, and direct and inverse solutions for position, velocity, and acceleration were derived. Furthermore, an investigation into singular configurations was carried out to ensure the stability and reliability of the mechanism. Based on the direct position solution, the workspace of the mechanism was calculated, and workspace optimization was performed by using the differential evolution (DE) algorithm to enhance its performance and efficiency. Additionally, dynamics modeling of the mechanism was performed by using the virtual work principle and the serial single open chain method. This modeling allowed for the determination of driving force curves, which were essential for the design and optimization of the mechanism's drive system. Finally, a concise conceptual design was presented for the application of this mechanism in two operational modes on production lines. In the 2T1R mode, the mechanism was utilized for inkjet printing of drinking cups, demonstrating its precision and accuracy in positioning and motion control. In the 3T mode, the mechanism was used for boxing operations, showcasing its ability to handle larger loads and perform complex movements. This dual-mode capability of the mechanism offered unparalleled flexibility and adaptability for various industrial applications, making it a valuable addition to the manufacturing industry.