南方丘陵山地油菜和小麦播种机械化发展现状与趋势  

Development status and trend of mechanization of rapeseed and wheat sowing in hilly and mountainous region of southern China

作　　者：廖宜涛[1,2] 施彬彬 王传奇 廖庆喜 武安阳[1] 欧耀徽 LIAO Yitao;SHI Binbin;WANG Chuanqi;LIAO Qingxi;WU Anyang;OU Yaohui(College of Engineering,Huazhong Agricultural University,Wuhan 430070,China;Key Laboratory of Agricultural Equipment in Midlower Reaches of The Yangtze River,Ministry of Agriculture and Rural Affairs,Wuhan 430070,China)

机构地区：[1]华中农业大学工学院,武汉430070 [2]农业农村部长江中下游农业装备重点实验室,武汉430070

出　　处：《农业工程学报》2025年第1期12-26,共15页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家重点研发计划项目(2021YFD1600503);国家自然科学基金项目(51975238);国家现代油菜产业技术体系专项(CARS-12-17);湖南省智能农机装备创新研发项目(HN202308)。

摘　　要：中国南方丘陵山地油菜和小麦的机械化生产是解决南方地区冬闲田难题的有效途径,也是保障国内粮油安全的重要措施,但受地形条件和种植模式影响,目前南方丘陵山地油菜、小麦机械化播种水平较低且发展缓慢,仍处于“无机可用”或“无好机可用”状态。该文从生产区划、应用场景、技术装备3个方面分析了南方丘陵山地油菜小麦机械化生产现状,通过梳理油菜和小麦典型生产场景与实际应用机具之间的矛盾,明确了湿黏稻茬土壤、复杂丘坡地形、油菜和小麦种植农艺要求、区域经济落后、经营主体倾向和技术市场空缺等是影响南方丘陵山地各油麦产区机械化播种发展水平不充分的主要因素;通过分析现阶段油菜小麦机械化播种装备应用、技术发展及南方丘陵山地油麦机械化播种的技术卡点和装备发展需求,提出了研发适应南方丘陵山地黏重湿烂土壤条件、丘坡地形和小地块播种的智能化油麦兼用联合直播机是提高油麦机播水平的装备基础,并明确黏重与湿烂土壤种床整备、丘陵山地起伏地表播深控制、油麦兼用精量排种、智能化播种控制等关键技术是研究重点。研究可为南方丘陵山地油菜、小麦机械化播种水平提升提供参考。The accurate location of targets can greatly contribute to the precision operations within agricultural scenes.Binocular stereo vision can be used to obtain the three-dimensional(3D)perception of the real world.Considerable application potential can depend mainly on the 3D localization and point cloud reconstruction of targets in agricultural environments.This study aims to review the latest research on binocular stereo vision and its applications in the agricultural field.Firstly,the pipeline of binocular stereo vision was summarized,including binocular camera calibration,epipolar rectification,and stereo matching.The binocular camera was utilized to calculate the depth of targets using disparity data.The objective of stereo vision calibration was then to determine the intrinsic and extrinsic parameters of the camera,including reference calibration,active vision calibration,self-calibration,and neural network calibration.A mapping was also established among points in pixel and world coordinates.In epipolar rectification,the constraints were employed to reduce the search space,in order to match the points from two dimensions to one.Stereo matching was used to calculate the disparity,in order to match the left and right images in both feature-based and deep learning.Furthermore,the local,global,and semi-global methods were categorized in the search range of matching pixels.The local method was used to search for the matching points within surrounding areas,the global method was to minimize the global energy function,and the semi-global method was to aggregate the costs from various directions.In contrast,more complex features were learned to enhance the stereo matching using deep learning.The network frameworks were introduced,such as convolutional neural networks(CNN),generative adversarial networks(GAN),transformers,neural architecture search(NAS),iterative optimization(IO)and graph neural network(GNN).CNNs performed extensive convolution operations to compute the matching costs for high accuracy,including convo

关 键 词：南方丘陵山地 油菜 小麦 机械化播种 发展现状 趋势 

分 类 号：S223.2[农业科学—农业机械化工程]