To address the lack of contact parameters for seed ginger and the insufficient accuracy of existing discrete element models used in simulations of seed ginger machinery, a discrete element modeling and contact parameter calibration method for irregularly shaped seed ginger was proposed. A discrete element model of seed ginger was constructed using 3D laser scanning and an adaptive multi-sphere filling method. The contact parameters between seed ginger and steel were calibrated using free-fall collision tests, inclined-plane tests, and combined inclined-plane–horizontal-plane tests. Using the angle of repose as the response variable, the optimal parameter combination was determined through steepest ascent tests and response surface methodology. The relative error between the simulated angle of repose and the value measured in the bench test was 2.24%. The results provide a modeling basis for the optimization and design of key components of seed ginger machinery and for investigating the dynamic interaction mechanisms among seed ginger particles.

IntroductionRapid advances in smart agriculture have highlighted the potential of large language models (LLMs), while practical applications remain limited by privacy risks, high training costs, and hallucinations. Focusing on garlic cultivation, this study proposes a knowledge-graph-enhanced framework that improves domain-specific LLMs through graph-based retrieval.MethodsA hybrid clustering algorithm with intra-cluster multidimensional ranking is employed to identify ten core entity types from garlic-related corpora. Few-shot learning and chain-of-thought prompting are further integrated to optimize entity and relation extraction using Qwen 2.5:32B, leading to the construction of a structured garlic cultivation knowledge graph. The knowledge graph is integrated with LLMs via GraphRAG, while prompt reconstruction strategies are adopted to enrich contextual information and constrain generation, thereby improving domain grounding and reducing hallucinations.ResultsExperimental results demonstrate absolute improvements of 13.0, 37.0, and 28.2 percentage points in precision, recall, and F1-score, respectively, for triple extraction compared with the baseline LLM, along with a 37.5 percentage point increase in retrieval-based question-answering accuracy.DiscussionThe proposed approach is interpretable and locally deployable without dependence on commercial APIs, offering a transferable methodological reference for domain-specific knowledge graph construction and intelligent question answering across diverse crop systems.

This study introduces a hybrid algorithm combining an enhanced A* algorithm with the Dynamic Window Approach (DWA) to improve agricultural robot navigation. The A* algorithm incorporates a Grey Wolf Optimizer (GWO) for dynamic heuristic weighting based on obstacle density and a bidirectional search strategy to boost efficiency. Path smoothing is achieved via key point selection and third-order B-spline fitting. In DWA, a Q-Learning mechanism adaptively optimizes weight coefficients, and a posture adjustment function eliminates initial heading deviations to avoid redundant steering. Simulations reveal that in simple environments, the proposed method reduces the computation time by 19.1% and increases the robot speed by 24.2%, with only a 1.7% increase in path length. In complex settings, it reduces the execution time by 20.1 %, shortens the path length by 1.2 %, and raises the speed by 25 %. Tests in a greenhouse demonstrate effective navigation in single- and multi-row operations, with 19.3 % and 37.7 % lower distance deviations, and 8.7 % and 14.6 % lower heading deviations, respectively. The experimental results demonstrate that the fusion algorithm, by simultaneously optimizing global and local path planning, significantly enhances the navigation accuracy, operational efficiency, and environmental adaptability of agricultural robots, thereby meeting the critical requirements for intelligent navigation in precision agriculture.

Consistent shoot orientation and uniform plant spacing are essential for high-yield, mechanized ginger production. However, current seeding systems still rely heavily on manual ginger seed placement, which limits the level of automation, particularly under stacked and occluded conditions. In this study, we present an integrated vision-guided robotic system for automatic ginger seed picking, shoot orientation adjustment and spacing control. For visual perception, we develop an enhanced YOLO11-seg detector with three architectural modifications: an asymmetric dual-stream RGB-D backbone for joint appearance and geometric feature extraction, a lightweight depth branch built with GhostConv and a proposed C3Ghost-SS module integrating SimAM and SGE attention, and an additional small-object detection head to improve shoot recognition in stacked and occluded scenes. To support reliable 3D localization, we further develop a rapid board-free hand–eye calibration procedure and a training-free biharmonic depth completion method. These perception and localization modules are integrated with a low-cost robotic arm to form a closed-loop system for seed picking, reorientation, and in-row spacing control. Across model scales (n → x), the improved network raises mAP@0.5 by 7.2 %, 7.6 %, 6.8 %, 7.0 %, and 6.5 % over the YOLO11 baseline. The calibration achieves 90th-percentile absolute errors of 1.85 mm (X), 2.07 mm (Y), and 2.91 mm (Z). For depth completion, the biharmonic equation method reduces MAE and RMSE by ∼ 62 % and 67 %, and increases PSNR and SSIM by ∼ 28 % and 5 %, respectively, while maintaining high computational efficiency. System-level tests yield 76.5 % seed-picking success, 86.3 % reorientation success, and spacing accuracy within ± 2.3 cm. These results demonstrate the feasibility of the proposed closed-loop robotic system for automated ginger seed manipulation under complex planting conditions, and provide a practical step toward more autonomous ginger planting.

During the separation of ginger rhizomes from stems, ginger stems are prone to brittle fracture due to stress concentration. To enhance ginger harvesting efficiency and minimize breakage, this study focused on the development of a low-degree-of-freedom cutting mechanism with dual-clamping. This device primarily consists of differential-speed dual chains, a cutting blade, an electric motor, a stem disposal mechanism, a drive shaft, and a support frame. Based on comprehensive mechanical testing of ginger stems—including creep, stress relaxation, puncture, and shear tests—the key mechanical and physical properties of the primary harvesting sections were determined. Utilizing an EDEM-RecurDyn co-simulation algorithm, an interaction model between the stems and the cutting blade was established. Key operational parameters investigated were blade rotational speed, cutting angle, and stem base cutting distance (the distance from the stem-rhizome junction to the cutting point), with stem cutting completeness rate and breakage rate serving as the evaluation metrics for harvesting quality. Regression models correlating these metrics with the operational parameters were developed, enabling the determination of the optimal operating parameters for the rhizome-stem separation device. The optimized parameters are as follows: blade rotational speed of 190 r/min, cutting angle of 8.8°, and stem base cutting distance of 9.5 cm. Field trials demonstrated that the optimized separation device significantly mitigated brittle fracture incidents and markedly reduced blockages. Under optimal conditions, the cutting completeness rate reached 94.9%, while the breakage rate was reduced to 4.49%. These experimental results align closely with the predictions derived from the regression model optimization. This research provides a theoretical foundation for the design of ginger rhizome-stem separation devices.

This study investigates the interaction mechanisms between the dibber, soil, and garlic during directional garlic seeding using the Discrete Element Method (DEM), aiming to improve planting uprightness and planting-depth consistency. A discrete element model of the garlic seeding process was established to analyze the dynamic interactions among the dibber, soil, and garlic from a microscopic perspective, and the reliability of the simulation results was verified through macroscopic experiments. The results show that dibbling depth, lifting height, dibbling speed ratio, and soil-particle surface energy are key factors influencing planting uprightness and depth consistency. Within a certain range, increasing the dibbling depth and lifting height significantly improves uprightness, although the effect diminishes beyond critical thresholds. The influence of the dibbling speed ratio on uprightness exhibits a nonlinear trend, in which both excessively low and excessively high ratios reduce uprightness. In addition, lower soil-particle surface energy leads to increased uprightness and improved soil backfilling performance. Experimental validation confirmed strong agreement between the simulation and physical test results, with an average relative error of less than 10%. This study provides a theoretical foundation and numerical simulation tools for optimizing directional garlic seeding technology, offering important guidance for improving planting uprightness and planting-depth consistency.

Fruit size is a key trait in small-fruit breeding, yet its measurement remains labor intensive and prone to human error. To address this, we developed a non-destructive, automated size measurement system based on machine vision and LabVIEW, designed for small fruits such as cherry, blueberry, and walnut. The system integrates a modular architecture, including flat-field correction, calibration, and pattern matching sub-VIs, to ensure user-friendly operation. These sub-VIs also enable the system’s core data analysis, such as real-size conversion through calibration and noise reduction for data accuracy through flat-field correction. An optimized image processing pipeline (grayscale conversion, Canny edge detection, morphological operations) enables precise contour extraction, even for fruits with stems or irregular surfaces. The system supports multi-species adaptation through lightweight parameter adjustments, without hardware modification. Experiments involved 15 samples per species (cherry ‘Tieton’, blueberry ‘Northland’, walnut ‘Xiangling’). A gold-standard protocol was established using a pre-calibrated digital caliper operated by two experienced technicians, with the mean of six replicates per fruit defined as the true value. Results demonstrated low root mean square errors, with coefficients of determination (R2) exceeding 0.98. Paired t-tests confirmed no significant differences from the gold standard. The system achieved a measurement speed of 0.4 s per fruit, six times faster than manual methods, and complied with the precision requirements of GB/T 26906-2024 (Sweet Cherry). This system offers a cost-effective, high-throughput solution for fruit breeding and phenotyping, effectively overcoming the limitations of manual measurement.

Crops are prone to lodging with the decline of stem moisture and the intervention of other factors in the mature harvest period, such as garlic, which is difficult to harvest mechanically. To solve this problem, the plant-correcting reel for harvesting lodging garlic plants, bumped and deformed with plants many times to pull and lift them into a conveyor, was proposed in this study. This study analyzed the motion trajectory equation and key influencing factors of the reel and defined the lifting and plant-correcting stages as three processes of contact, stirring and release. For example, the contact deformation model and system energy equations were established in the contact process. Besides, in the stirring process, the garlic plantcorrecting conditions were established through the dynamic simulation test analysis of garlic seedling trajectories and the deflection model of garlic stem was constructed. Furthermore, in the release process, the expressions of rubber bars rotation and garlic plant offset bending curvature were constructed and the optimal number and distribution form of bars were determined. Meanwhile, the mechanism and key operating parameters of the auxiliary lifting mechanism of the divider were established. Through the single-factor test, the influence of reel speed, forward speed and reel height on the success feeding rate was analyzed under different bars distribution forms; Through multi-factor experiments, the interaction contour map of various factors was constructed. When reel speed, forward speed and reel height were 3 rad/s, 3.5 m/s, and 540 mm, the feeding success rate was 98.73%. The optimization factors were tested and verified, which met the operational requirements of a high feeding success rate and low loss rate of garlic harvest. This study combines laboratory virtual as well as field experiments and analyzes of trajectory of bars, contact deformation and deflection model of garlic plant, and reel rotation and garlic plant offset bending curvature to solve the problem of garlic lodging mechanized harvest and yield reduction.

Garlic is a versatile crop of high economic value, with an increase in the growth of the cultivation scale. Planting garlic with bottom-side (root) down and pointy-tip (clove) up is essential in agriculture because the orientation of the clove significantly affects garlic quality. In this study, we propose a clove orientation recognition technique based on capacitance sensing technology, where clove states are determined by utilizing characteristic differences in capacitance variations associated with different orientations of cloves. First, we applied Maxwell simulations to obtain capacitance variation during the falling process. Second, we conducted field experiments using the capacitive sensing device to obtain the capacitance variations during the falling of garlic in an unstructured environment. Third, the continuous capacitance signal collected during the field experiment was segmented into short-term feature signals containing individual garlic fallings. Finally, Long Short-Term Memory (LSTM) and Frequency-Residual-GoogLeNet (F-Res-GoogLeNet) deep models were trained to recognize garlic falling states. Our best model achieved an accuracy of 96.75%, which meets the agricultural requirements for garlic cultivation. This study demonstrates that monitoring capacitance data in field environments can identify garlic cloves’ orientation, eventually enhancing garlic’s final yield, quality, and economic benefits.

In order to solve the problem of low upright rate of traditional machinery, a method for determining the garlic clove orientation based on capacitive detection technology was proposed. There were differences in morphological structure between the bud and the root: under the same width, the volume of the bud was smaller than that of the root. The feature was exploited and converted to an average dielectric constant difference to determine the orientation of the garlic clove by detecting the capacitance. The feasibility of the method was verified based on the electric field analysis of ANSYS in the research process. In addition, the corresponding bud adjustment device was designed. Orthogonal tests took the short axis radius, long axis radius, and bottom angle of the device as the test factors and the upright rate as the test indicators, and the test data were analyzed by Design-Expert 11.1.2.0 software to obtain the order of influence of each factor on the index value. The results showed that when the bottom angle, short axis radius, and long axis radius were 80°, 22.99 mm, and 27.79 mm, respectively, the device performance was optimal and the theoretical upright rate was 96.6%. The test verification of the optimized factors was basically consistent with the optimized results. With the pole plate parameter as the test factor and the signal-to-noise ratio as the test indicators, the test data showed that the device performance was optimal when the pole plate parameter was 45 mm×8 mm×0.10 mm. The prototype test was carried out with Jinxiang and Cangshan garlic cloves, and the qualified rate was 95.0%, which met the requirements of garlic sown. The research result can provide support for the application of capacitance detection technology in precision sowing equipment.

Garlic is an important crop of high economic value, with an increase in the growth of the plant every year. The direction of garlic clove during growth affects garlic quality significantly, which includes seedling emergence time, yield, and appearance quality. To improve garlic quality, we propose a garlic clove direction recognition approach using capacitance sensing technology. First, we simulate the feasibility of determining garlic clove direction using capacitance data. Second, we conduct field experiments to obtain the capacitance change of garlic falling under varying statuses using a capacitance sensing device. Third, we create a set of data with individual garlic falling states by segmenting continuous capacitance signals into short-term feature signals via feature analysis. Last, the garlic falling states are learned by training LSTM and F-Res-GoogLeNet models. Our results using LSTM and F-Res-GoogLeNet models achieved recognition accuracies of 94.75% and 96.75%, respectively, which meets the agricultural requirements for garlic cultivation. Our study shows that garlic cloves’ direction could be recognized during their fall with capacitance data, which could improve overall garlic quality and economic benefits.

Variable transmission ratio gears have shown considerable potential as key components of variable speed transmission steering boxes that balance steering portability and sensitivity. The objective of this study is to develop a novel method for designing variable transmission ratio tooth surfaces to circumvent the shortcomings of the meshing theory method. The involute rack of the variable transmission ratio pinion-and-rack pair is considered as a set of countless and infinitely close transverse sections, each of which is referred to as a gear element. Assuming evenly distributed circles in the physical domain of the variable transmission ratio tooth surfaces of the pinion, the problem of generating a tooth surface point is transformed into solving the specific geometric feature intersection point of each circle and corresponding gear element during variable transmission ratio meshing. Thereafter, a mathematical model and algorithm are developed to generate tooth surface points in an approximately even pattern. The normal deviations of the gear-element–generated tooth surface points and the corresponding tooth surface points calculated using meshing theory show that the proposed design method has a sufficiently high accuracy. Finite element models are employed analyzing the contact pattern, contact stress, bending stress, and transmission ratio error. The results indicate that the layout of approximately even tooth surface points using the gear element method is beneficial for improving the fitting precision of tooth surfaces and reducing the contact stress and transmission ratio error. Further, the same derived law of the flank and fillet tooth surfaces is conducive to the continuity of the tooth surfaces, which contributes toward reducing the bending stress. Finally, a prototype is manufactured via CNC end milling. The major contributions lie in a robust and efficient modeling method for variable transmission ratio tooth surfaces, which combined forms a solid foundation for their application.

Green onion (Allium fistulosum L.) is mainly available as factory-produced seedlings. Although factory seedling production is highly automated, miss-seeding during the seeding process considerably affects subsequent transplanting and the final yield. To solve the problem of miss-seeding, the current main method is manual complementary seeding, which is labor-intensive and inefficient work. In this study, an automatic machine-vision-based complementary seeding device was proposed to reduce the miss-seeding rate and as a replacement of manual complementary seeding. The device performs several main functions, including the identification of miss-seeding holes, control of seed case movement, and the seed uptake and release from the seed suction nozzle array. A majority-mechanism-based miss-seeding tray hole rapid-detection method was proposed to enable the real-time identification of miss-seeding tray holes in the tray under high-speed moving conditions. The structural parameters of the vacuum-generated seed suction nozzle were optimized through numerical simulations and orthogonal experiments, and the seed suction nozzle array and seed case were produced using 3D-printing technology. Finally, the complementary seeding device was installed on the tray-type green onion seeding machine and the effectiveness of the complementary seeding was confirmed by experiments. The results revealed that the average values of the precision, recall, and F1 scores for identifying miss-seeding tray holes were 98.48%, 97.00%, and 97.73%, respectively. The results revealed that the rate of miss-seeding tray holes decreased from 5.37% to 0.89% after complementary seeding.

针对勺链式大蒜播种机在取种过程中的漏取问题,该研究设计了一种以激光传感器、转勺式补种器、补种箱为核心的大蒜漏取种检测与补种装置,并搭建了试验台.利用漫反射激光传感器对取种情况进行检测,根据输出脉冲判断取种勺是否漏取种,设计了以STM32处理器、电磁离合器为核心的漏取种检测与补种控制系统.为得到取种勺漏取蒜种时的临界脉冲宽度,进行了取种勺遮挡脉冲获取试验,得到Ⅰ、Ⅱ、Ⅲ级金乡蒜种在正常提种速度范围内以不同姿态被取起时的脉冲范围.在0.09～0.15 m/s提种速度区间内,蒜种与种勺的最大脉冲宽度为440.6 ms、最小为121.6 ms;空勺时的脉冲宽度最大为87.9 ms、最小为53.3 ms,判断取种勺是否漏取蒜种的临界脉冲宽度定义为100 ms.为确定最佳补种时刻与控制脉冲宽度,实现精准投种,对取种与投种过程进行分析,在提种速度为0.12 m/s时,传感器检测到漏取种后延时625 ms电磁离合器通电工作,持续363 ms后电磁离合器断电停止工作,此时可保证蒜种正好落入导种筒内,完成补种.为验证系统整体性能,进行取补种试验.结果表明:针对不同提种速度,系统对漏取种检测的成功率均为100％,其中在0.12 m/s的提种速度下,经补种后金乡Ⅰ、Ⅱ、Ⅲ级蒜种播种成功率分别从90.13％、93.13％、95.88％提高至99.25％、99.75％、100％.研究结果为研制具有补种功能的勺链式大蒜播种机提供了理论依据及技术支撑.

为解决大葱移栽作业中配套动力轮距与垄距不匹配的问题,研制了基于大葱移栽机的卧式旋耕机.该旋耕机位于移栽装置的正前方,可一次完成开沟、起垄、移栽、覆土和镇压等不间断作业.旋耕机采用整体框架式结构和中间链轮传动的传动方式,使旋耕作业更加平稳,主要工作部件包括悬挂架、减速箱、旋耕刀轴、旋耕刀、输入轴、链轮及链条等.应用CAD、SolidWorks、ANSYS等软件进行图样设计、三维建模和应力分析,并对旋耕刀等关键机构进行重点设计,通过理论分析和有限元分析确定了影响旋耕刀切削阻力因素、旋耕机功率消耗大小、旋耕刀最大应力和变形位置.在山东青州市华龙大葱试验基地田间试验,结果表明:与该旋耕机配套的动力为35～ 80kW;可完成耕深3～18cm,满足葱苗移栽深度5～13cm的农艺要求;碎土率均值为74.92,满足旋耕刀设计与葱苗栽植要求.

A consistent orientation of ginger shoots when sowing ginger is more conducive to high yields and later harvesting. However, current ginger sowing mainly relies on manual methods, seriously hindering the ginger industry’s development. Existing ginger seeders still require manual assistance in placing ginger seeds to achieve consistent ginger shoot orientation. To address the problem that existing ginger seeders have difficulty in automating seeding and ensuring consistent ginger shoot orientation, this study applies object detection techniques in deep learning to the detection of ginger and proposes a ginger recognition network based on YOLOv4-LITE, which, first, uses MobileNetv2 as the backbone network of the model and, second, adds coordinate attention to MobileNetv2 and uses Do-Conv convolution to replace part of the traditional convolution. After completing the prediction of ginger and ginger shoots, this paper determines ginger shoot orientation by calculating the relative positions of the largest ginger shoot and the ginger. The mean average precision, Params, and giga Flops of the proposed YOLOv4-LITE in the test set reached 98.73%, 47.99 M, and 8.74, respectively. The experimental results show that YOLOv4-LITE achieved ginger seed detection and ginger shoot orientation calculation, and that it provides a technical guarantee for automated ginger seeding.

针对人工采摘效率低、成本高且劳动力缺乏的问题,利用采摘机器人实现果实的自动化采摘日益成为研究的热点.视觉识别作为采摘机器人的关键技术之一,其发展对实现自动化采摘具有重要意义.采摘环境的复杂性为机器人正确识别目标带来了困难,使得目前的识别算法研究仍面临着较大的挑战.本文阐述了常见的几种目标分割识别算法,并将其归纳为基于特征、像素和深度学习的三类识别方法,综述了国内外学者在采摘机器人识别目标时应用到的分割和识别算法,并对目前存在的问题进行总结,同时对今后的研究趋势进行展望,以期为采摘机器人的研究设计提供参考.

Consistent ginger shoot orientation helps to ensure consistent ginger emergence and meet shading requirements. YOLO v3 is used to recognize ginger images in response to the current ginger seeder’s difficulty in meeting the above agronomic problems. However, it is not suitable for direct application on edge computing devices due to its high computational cost. To make the network more compact and to address the problems of low detection accuracy and long inference time, this study proposes an improved YOLO v3 model, in which some redundant channels and network layers are pruned to achieve real-time determination of ginger shoots and seeds. The test results showed that the pruned model reduced its model size by 87.2% and improved the detection speed by 85%. Meanwhile, its mean average precision (mAP) reached 98.0% for ginger shoots and seeds, only 0.1% lower than the model before pruning. Moreover, after deploying the model to the Jetson Nano, the test results showed that its mAP was 97.94%, the recognition accuracy could reach 96.7%, and detection speed could reach 20 frames·s−1. The results showed that the proposed method was feasible for real-time and accurate detection of ginger images, providing a solid foundation for automatic and accurate ginger seeding.

为确定蒜种离散元模型仿真参数,采用多球聚合的方法建立了蒜种离散元模型,并对可实现测量的模型参数(蒜种-钢板恢复系数、蒜种-蒜种恢复系数、蒜种-钢板静摩擦因数、蒜种-蒜种静摩擦因数)进行了物理试验测定.对不易测量的模型参数(蒜种-钢板滚动摩擦系数、蒜种-蒜种滚动摩擦系数)进行了仿真试验标定,应用、建立了两种不易测量的接触参数与蒜种堆休止角、密相区域圆半径的二次回归模型,最终求解寻优得到参数最优值.通过上述试验方法,得到蒜种离散元模型参数:蒜种-钢板恢复系数为0.511,蒜种-蒜种恢复系数为0.487,蒜种-钢板静摩擦因数为0.473,蒜种-蒜种静摩擦因数为0.503,蒜种-钢板滚动摩擦因数为0.111,蒜种-蒜种滚动摩擦因数为0.108.最后,在得到的接触参数下进行仿真验证试验,结果表明:蒜种堆休止角、密相区域圆半径的仿真值与实际值的相对误差分别为1.36％、1.50％,无明显差异,排种功率、排种速度的仿真与试验值在整体变化趋势上具有一致性,验证了蒜种离散元模型与仿真试验的有效性,可为大蒜播种机械仿真设计与优化提供参考.

大蒜机械化播种的植入环节中,在蒜种-土壤-触土部件强耦合作用下,正头后的蒜种直立度极易变低,如何"保姿植入"成为亟待解决的关键技术.针对此问题,该文以行星轮式大蒜插播机为研究对象,对插播鸭嘴的尖部运动轨迹进行分析,明晰了影响植后蒜种直立度的关键因素为插播鸭嘴的线速度、开启相位角及插播鸭嘴张开角度与凸轮凸起段对应的圆心角之比(开口速比).运用Box-Benhnken中心组合试验方法对插播鸭嘴的线速度、开启相位角、开口速比进行三因素三水平二次回归试验设计,进行了插播试验,采用Design-expert软件建立响应面数学模型,对影响直立度的关键参数进行了综合优化,求解出最优工作参数组合为插播鸭嘴的线速度200 mm/s,开启相位角20°,开口速比2.大田试验结果表明,最优参数作业的蒜种直立度均值为63.2°,较优化前提高了21.8％,满足大蒜种植的蒜种直立度要求.