Combining multiple crop protection Unmanned Aerial Vehicles (UAVs) as a team for a scheduled spraying mission over farmland now is a common way to significantly increase efficiency. However, given some issues such as different configurations, irregular borders, and especially varying pesticide requirements, it is more important and more complex than other multi-Agent Systems (MASs) in common use. In this work, we focus on the mission arrangement of UAVs, which is the foundation of other high-level cooperations, systematically propose Efficiency-first Spraying Mission Arrangement Problem (ESMAP), and try to construct a united problem framework for the mission arrangement of crop protection UAVs. Besides, to characterise the differences in sub-areas, the varying pesticide requirement per unit is well considered based on Normalized Difference Vegetation Index (NDVI). Firstly, the mathematical model of multiple crop-protection UAVs is established and ESMAP is defined. Furthermore, an acquisition method of a farmland's NDVI map is proposed, and the calculation method of pesticide volume based on NDVI is discussed. Secondly, an improved Genetic Algorithm (GA) is proposed to solve ESMAP, and a comparable combination algorithm is introduced. Numerical simulations for algorithm analysis are carried out within MATLAB, and it is determined that the proposed GA is more efficient and accurate than the latter. Finally, a mission arrangement tested with three UAVs was carried out to validate the effectiveness of the proposed GA in spraying operation. Test results illustrated that it performed well, which took only 90.6 % of the operation time taken by the combination algorithm.

As a significant branch of production scheduling problem, the Flexibility Job Shop Scheduling Problem (FJSP) is a typical NP-hard problem. Most conventional flexible workshop scheduling primarily focuses on performance aspects involving production efficiency such as time and quality. In recent years, due to increased energy costs and environmental pollution, ‘low-carbon scheduling’ has garnered attention as a new scheduling paradigm among scholars and engineers. This paper investigates a low-carbon flexible Job shop scheduling problem, proposing a Grey Wolf Optimization algorithm (SC-GWO), aiming to minimize the sum of carbon emission costs and makespan costs. This algorithm employs the Grey Wolf Algorithm (GWO) as the fundamental optimization method, adaptively choosing between global and local searches based on the dispersion degree of individuals. Firstly, integrating the Sine Cosine Algorithm (SCA), the sinusoidal cosine search mechanism is applied to GWO to enhance its local search capability. Secondly, a new leader selection mechanism is introduced to prevent leaders from falling into local optima, thus improving the algorithm’s global exploration capability. Utilizing a nonlinear convergence factor strategy controls the global exploration and local exploitation capabilities in different algorithm stages, enhancing optimization accuracy and accelerating convergence, achieving a dynamic balance between the two. Finally, validation of the SC-GWO algorithm’s ability to solve low-carbon scheduling problems in flexible job shop scheduling instances is conducted. Experimental results demonstrate the superior performance of SC-GWO in solving low-carbon flexible workshop scheduling instances. Comparative experiments against four other advanced algorithms on 22 classic benchmark test functions confirm SC-GWO’s better convergence. Through standard test functions like Bandimarte instances applied to solve FJSP, experimental results showcase the excellent optimization performance of SC-GWO. Compared to HGWO and GWO, the makespan time is reduced by 22.25% and 39.27%, respectively. The proposed SC-GWO algorithm demonstrates favorable solving effects on flexible job shop scheduling instances, meeting actual production scheduling needs.

As an important branch of production scheduling, the flexible job shop scheduling problem (FJSP) is a typical NP-hard problem. Researchers have adopted many intelligent algorithms to solve the FJSP problem, nonetheless, the task of dynamically adapting its essential parameters during the computational process is a significant challenge, resulting in the solution efficiency and quality failing to meet the production requirements. To this end, this paper proposes an adaptive gray wolf fast optimization algorithm (SS-GWO), which adopts the gray wolf algorithm (GWO) as the basic optimization method, and the algorithm adaptively selects the global search or local search according to the degree of agglomeration of individuals. Firstly, a non-linear convergence factor strategy is employed to control the global exploration and local exploitation capabilities of the algorithm at different stages. This enhances optimization precision and accelerates convergence speed, achieving a dynamic balance between the two. Secondly, the spiral search mechanism of Whale Optimization Algorithm is used in GWO to improve the exploration capability of Gray Wolf Optimization Algorithm. Finally, the effectiveness of SS-GWO model is verified by comparison experiments. The comparison demonstrates the superiority of SS-GWO over the other five state-of-the-art algorithms in solving the 22 classical benchmark test functions. SS-GWO is applied to solve FJSP by means of the standard test function bandimarte calculus. The optimal solution and performance of SS-GWO for solving FJSP are compared with other algorithms. The experimental results show that the SS-GWO algorithm has good optimization performance, and the maximum completion time is reduced by 19% and 37% compared with that of IGWO and GWO, respectively, and the proposed SS-GWO algorithm achieves a better solution effect on flexible job shop scheduling instances, which can satisfy the actual production scheduling needs.

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.

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.

针对现有蔬菜通用排种器大多以单粒取种为主、播种效率低、播种精度差等问题,将气吸式排种器与机械式排种器的优点充分糅合,研制了一款针对大葱育苗的精量排种器.通过理论分析和计算,确定了关键部件工作参数.搭建试验台,对排种器进行播种性能试验,以合格率、漏播率、重播率为评价指标,以窝眼倾斜角度、气室真空度、排种器转速为因素,进行三因素三水平二次回归正交试验.根据试验数据结果,采用Design-Expert软件建立了响应面数学模型,并对数学模型进行优化,结果表明:当窝眼倾斜角度为13.1°、排种器转速为9.91r/min、气室真空度为3.14kPa时,排种效果最佳.以优化后的参数进行试验验证,结果表明:漏播率平均值为2.524％,重播率平均值为1.132％,合格率平均值为96.344％,满足大葱精量播种要求.

通过研究翻转式教学方法、教学策略和教学内容,针对线上教学,开发相应学习资源,提出教学引导独立学习的有效改革措施,完善、提高创新型人才培养的质量.利用翻转课堂的教学模式,建立完善和牢固的知识体系,培养良好的创新能力和创新思维;同时提高教师的指导能力和科研能力.通过基于翻转课堂模式的创新人才培养研究,提高研究生教学水平,促进研究生培养水平的整体提高.

Quality grading in antler mushroom industrial production is a labor-intensive operation. For a long time, manual grading has been used for grading, which produces various problems such as insufficient reliability, low production efficiency, and high mushroom body damage. Automatic grading is a problem to be solved urgently for antler mushroom industrial development with increasing labor costs. To solve the problem, this paper deeply integrates the single-stage object detection of YOLOv5 and the semantic segmentation of PSPNet, and proposes a Y-PNet model for real-time object detection and an image segmentation network. This article also proposes an evaluation model for antler mushroom's size, which eliminates subjective judgment and achieves quality grading. Moreover, to meet the needs of efficient and accurate hierarchical detection in the factory, this study uses the lightweight network model to construct a lightweight YOLOv5 single-stage object detection model. The MobileNetV3 network model embedded with a CBAM module is used as the backbone extractor in PSPNet to reduce the model's size and improve the model's efficiency and accuracy for segmentation. Experiments show that the proposed system can perform real-time grading successfully, which can provide instructive and practical references in industry.

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.

针对目前生姜机械化播种难以实现"种芽朝向一致"农艺要求的问题,该研究提出了一种基于深度学习的生姜种芽快速识别及其朝向判定的方法.首先,构建生姜数据集.其次,搭建YOLO v3网络进行种芽的识别,包括:使用Mosaic等在线数据增强方式,增加图像的多样性,解决小数据集训练时泛化能力不足的问题;引入DIoU(Distance Intersection over Union)边框回归损失函数来提高种芽识别回归效果;使用基于IoU的K-means聚类方法,经线性尺度缩放得到9个符合种芽尺寸的先验框,减少了先验框带来的误差.最后进行壮芽的选取及其朝向的判定.测试集中的结果表明,该研究提出的生姜种芽识别网络,平均精度和精准率、召回率的加权调和平均值F1分别达到98.2％和94.9％,采用GPU硬件加速后对生姜种芽的检测速度可达112帧/s,比原有YOLO v3网络的平均精度和F1值分别提升1.5％和4.4％,实现了生姜种芽的快速识别及其朝向的判定,为生姜自动化精确播种提供了技术保证.

为满足大蒜定向播种的农艺要求,针对现有大蒜鳞芽调整方法对杂交蒜适应性差的问题,该研究设计了一种基于Jetson Nano处理器的大蒜鳞芽朝向自动调整装置.采用双卷积神经网络模型结构,其中一个神经网络模型对大蒜是否被喂入进行实时监测,检测到大蒜喂入调整装置后,一个ResNet-18网络模型对蒜种鳞芽朝向进行判断,当鳞芽朝上时大蒜鳞芽调整机构打开Y型料斗使大蒜以鳞芽朝上的姿态直接落下,当鳞芽朝下时大蒜鳞芽调整机构翻转180°带动大蒜一起翻转后以鳞芽朝上的姿态落下,实现大蒜鳞芽朝向实时调整.神经网络模型推理及舵机控制采用英伟达边缘计算处理器Jetson Nano进行处理.利用离散元分析软件EDEM结合正交试验方法对调整装置的关键结构参数进行优化,并以杂交大蒜为试验对象进行台架试验,试验结果表明:大蒜鳞芽调整成功率为96.25％,模型推理时间0.045 s,平均每粒大蒜调整时间为0.785 s,满足大蒜播种机播种要求.该文研究结果可为解决杂交大蒜直立播种问题及边缘计算在精密播种设备中的应用提供有益参考.

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.

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.

为提高甘薯移栽水平,解决现有移栽机结构单一、缺少浇水部件的难题,研制了一款集精细化旋耕整地、起垄、移栽及浇水等功能为一体的甘薯裸苗复式移栽机.介绍了甘薯裸苗复式移栽机的基本结构、工作原理和相关参数等.田间试验结果表明,该机栽植频率为每行每人40株,漏栽率1.51％,栽植深度合格率93.1％,栽植株距合格率92.4％,均符合国家标准要求.

The planting area of garlic in China accounts for more than 90％ of the whole area in the world. The major producer of garlic also needs mechanized harvesting in modern agriculture in recent years. Two kinds of harvesting technologies are divided mainly into: the widely-used segmented harvesting for the separation of garlic and soil individually, whereas, the new combined harvesting to concurrently separate the garlic, stem, root, and soil. However, the current garlic combine-harvester in the world can only collect the garlic under the upright position and wide rows, but cannot achieve the harvesting and cutting roots of lodging garlic plants. In this study, a novel garlic combined-harvester was designed to improve the harvesting efficiency, while reducing the labor intensity, according to the current state of garlic planting. Some specific conditions were also considered, including the working process of digging, the posture correction of garlic seedling, clamping, cutting, and low-damage collecting. The parameters of key components were determined using a theoretical and dynamic simulation of the operation process. A Box-Behnken orthogonal test was performed, where the forward speed, digging depth, and clamping distance were taken as the test factors, whereas, the damage rate and loss rate were the evaluation indices. A three-factor three-level Box-Behnken simulation was also carried out. A field test was conducted at a planting cooperative in Mindong, Shandong Province of China in May 2019. The test object was set as a variety of "Jinxiang Red Garlic". The shaft of shovel and gearbox gears were adjusted to change the distance between the clamping chains, thereby obtaining the different forward speeds, digging depths, and chain spacing during the test. The damage and loss rates were recorded for the harvested garlic in the field. Variance and response surface were utilized to determine the effects of forward speed, digging depth, and clamping distance on the evaluation index. A Design-Export software was used to optimize the model. An optimal combination of parameters was achieved as followed: the forward speed of 0.51 m/s, digging depth of 97.2 mm, and clamping distance of 7.6 mm, corresponding to the damage and loss rates of 0.63％ and 1.25％, respectively. The parameters were tested in the field to verify the accuracy of the optimized model. The relative errors of all indices in the predicted and experimental data were less than 5％, indicating that the model was reliable and suitable for prediction and further optimization. The main factors affecting the damage and loss rates of garlic were determined for the forward speed, digging depth, and clamping distance. The finding can provide a sound theoretical reference for the design and optimization of garlic combine-harvester in mechanized production of intelligent agriculture.

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.

针对山东地区辣椒春播秋收为主的一年一作传统种植方式存在的土地利用率低、经济效益差、半自动移栽机作业效率低、劳动强度大等问题,基于农机农艺融合提出了"大蒜(或小麦)+辣椒"新型轮作种植模式及高密度辣椒栽培方式,并设计了适用于该模式的可一次完成开沟、自动送苗、自动取苗、自动栽植及覆土等功能的自走式高密度智能辣椒移栽机.田间试验结果表明:该机可以较好地实现高密度栽植且栽植株距稳定,当设定株距为16cm时,株距变异系数为4.1％,整体栽植合格率为91.4％.机具各项指标符合行业标准要求,可满足实际使用需求.

针对目前国内大蒜收获强度大、收获效率低及收获成本高等问题,设计了分段式大蒜收获机.该机主要由挖掘装置、限深装置及夹持装置、打捆装置等组成,采用手扶拖拉机作为动力源和安装平台,夹持装置采用链条设计,打捆装置可实现收获后大蒜的打捆作业.该机可一次完成三行大蒜的挖掘、夹持输送、打捆等收获作业,省时省力,高效低耗.应用CAD、SolidWorks等软件进行图样的设计和三维模型的建立,并对挖掘装置、夹持装置等关键装置进行重点设计.在山东兰陵县神山镇进行了大蒜种植田间试验,结果表明:该机器生产率0.1hm2/h,漏蒜率为1.9％,伤蒜率为0.58％,损失率为1.9％,挖掘深度为8cm.研究结果可为大蒜收获机械的研究提供参考.

To address the problem of low garlic clove-head-turning rates in mechanized sowing of garlic, this study proposes an algorithm for identifying garlic clove orientation using machine vision. First, the algorithm performs preprocessing operations such as image enhancement and color space conversion; then, it identifies and labels the garlic clove regions by determining the connected regions. Each garlic clove region is derived for independent analysis, and three garlic clove features of each clove are calculated. Then, based on the distinctiveness of each feature parameter with clove recognition, comprehensive feature parameters are obtained by fusing the information of the three feature parameters. Finally, garlic clove head is identified on the basis of these comprehensive feature parameters. Furthermore, two garlic varieties, ‘Cangshan’ and ‘Jinxiang’ garlic, are tested by capturing images of both single and multiple garlic cloves. The experimental results indicate that the average recognition rate of the algorithm is 97.44%, and the average running time is 0.61 s. The correct recognition rate of ‘Jinxiang’ garlic is 94.51%, while that of ‘Cangshan’ garlic is 100%. In summary, the algorithm has strong adaptability and high accuracy, and it can provide a useful reference for an intelligent garlic clove-head-turning mechanism for garlic planters.

Garlic is an important economic crop whose planting areas has been annually increasing. Studies have shown that the direction of garlic cloves at the time of sowing has important effects on the germination time, yield and visual appearance of garlic. In order to ensure that the garlic cloves are upright when garlic is planted, an adjustment device based on computer vision has been designed to re-direct garlic cloves. As the garlic clove enters the adjustment device, images are collected by an industrial camera, from which the direction of the clove is identified through image analysis. In order to effectively identify the clove direction in images, a multi-feature algorithm is proposed. This algorithm is found to have higher accuracy than the single-feature recognition method, especially for garlic varieties with large individual differences. Once the clove’s direction is known, the adjustment device manipulates the direction of garlic clove to move it into the ideal planting position. Experimental results showed that the adjustment rate of ‘Jinxiang’ and ‘Cangshan’ garlic was 94.6% and 97.5%, respectively, and the average adjustment time was 1.13 s and 1.24 s, respectively. The proposed method of clove adjustment will not only contribute to improved precision planting outcomes for garlic but could also be extended to other crops whose yield levels are crucially affected by the seed direction.