[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4918":3,"related-4918":87},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":65,"slug":68,"view_count":22,"doi":69,"paper":70,"created_at":86},4918,"RESEARCH ON THE INFLUENCE OF DESIGN AND TECHNOLOGICAL PARAMETERS OF PRECISION AGRICULTURE EQUIPMENT ON THE QUALITY OF TECHNOLOGICAL OPERATIONS","https:\u002F\u002Fdoi.org\u002F10.37128\u002F2306-8744-2026-3-8","The article presents a theoretical investigation of the influence of design and technological parameters of precision agriculture equipment on the quality of technological operations. The main parameters determining machine performance are analysed, including positioning accuracy, operating speed, working width, actuator response time, geometric arrangement of working elements and metering accuracy. Particular attention is paid to the interaction between the navigation system, control system, actuator and working element, since operation quality depends on their coordinated performance. Analytical relationships are derived for spatial delay, transverse displacement of outer working elements, overlap and skip coefficients, and a comprehensive quality index. Numerical modelling is used to establish the effect of travel speed and actuator response time on spatial delay and the influence of heading-angle error and machine geometry on transverse displacement. The results show that increasing travel speed causes a proportional increase in spatial delay when actuator response time remains unchanged. Even small heading-angle errors can cause considerable displacement of outer working elements as the distance from the centre of rotation increases. A multi-criteria approach to assessing operation quality is proposed, taking into account positioning error, spatial delay, overlaps, skips and deviation of the actual application rate from the prescribed value. The proposed relationships can be used to substantiate design parameters, operating modes and control-system settings of precision agriculture equipment. The results may be applied to seeding, fertiliser application and spraying machines to reduce technological losses, improve treatment uniformity, optimise resource consumption and increase the efficiency of mechanised agricultural production.","本文对精准农业设备的设计参数与工艺参数对技术作业质量的影响进行了理论研究。分析了决定机器性能的主要参数，包括定位精度、作业速度、工作幅宽、执行机构响应时间、工作部件的几何布置以及计量精度。特别关注导航系统、控制系统、执行机构与工作部件之间的相互作用，因为作业质量取决于它们协调运行的表现。推导了空间延迟、外侧工作部件横向位移、重叠系数与漏作业系数以及综合质量指数的解析关系式。采用数值模拟方法，确定了行进速度与执行机构响应时间对空间延迟的影响，以及航向角误差与机器几何构型对横向位移的影响。结果表明，在执行机构响应时间保持不变时，行进速度增大会导致空间延迟成比例增加。即使很小的航向角误差，随着距旋转中心距离的增大，也会引起外侧工作部件相当大的位移。提出了评估作业质量的多准则方法，综合考虑定位误差、空间延迟、重叠、漏作业以及实际施用量与规定值的偏差。所提出的关系式可用于论证精准农业设备的设计参数、运行模式与控制系统设置。研究结果可应用于播种、施肥和喷药机械，以降低技术损失、改善作业均匀性、优化资源消耗并提高机械化农业生产效率。",null,"Vibrations in engineering and technology","2026-10-12T00:00:00Z","论文",10,false,65,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,16,8,9,1,"该论文从理论建模角度分析精准农业装备参数对作业质量的影响，方法新颖、结论可靠，对农机设计与控制优化有参考价值，但属细分领域学术成果，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","精准农业","变量作业","农业装备","导航定位",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,58],{"code":46,"score":19,"supported":47,"title":48,"abstract":50,"tags":51},"I1",true,[49],"precision agriculture",[28,49],[27,28],{"code":33,"score":53,"supported":47,"title":54,"abstract":55,"tags":57},4,[],[56],"机械化",[30],{"code":59,"score":60,"supported":47,"title":61,"abstract":62,"tags":64},"F3",3,[],[63],"施肥",[],[66,67],"精准农业装备 作业质量","农机 导航系统 定位精度","精准农业装备作业质量-4918","10.37128\u002F2306-8744-2026-3-8",{"doi":69,"openalex_id":71,"authors":72,"venue":10,"cited_by_count":76,"oa_url":77,"card":78,"direction":84,"ingested_from":85},"W7221509088",[73],{"name":74,"orcid":75},"Jurij Rakun","https:\u002F\u002Forcid.org\u002F0000-0001-9005-3461",0,"https:\u002F\u002Fvibrojournal.vsau.org\u002Fstorage\u002Farticles\u002FOctober2026\u002F9qndGdguMYcfXmdYoTQ0.pdf",{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"通过理论建模分析精准农业装备设计参数对作业质量的影响。","解析关系推导与数值建模，考虑定位、速度、响应时间等参数。","行驶速度增加导致空间延迟成比例增大，微小航向角误差也会造成外侧工作部件显著偏移。","智慧农业 \u002F 农业物联网","可结合田间实测数据验证模型，并开发基于实时响应补偿的智能控制算法以提升作业精度。","农业人工智能与决策模型","openalex","2026-10-14T23:30:58.397094Z",{"total":88,"page":22,"page_size":88,"items":89},6,[90,173,274,336,413,471],{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":9,"source_name":96,"source_url":93,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":98,"score_detail":99,"sources":103,"tags":105,"primary_topic":108,"secondary_topics":114,"classification":116,"search_phrases":136,"slug":139,"view_count":22,"doi":140,"paper":141,"created_at":172},3974,"A real-time estimation method for wheeled agricultural machinery slip parameters in paddy fields based on the prediction-residual approach","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112478","A real-time estimation method for wheeled agricultural machinery slip parameters in paddy fields based on the prediction-residual approach。Computers and Electronics in Agriculture","基于预测残差法的水稻田轮式农业机械滑转参数实时估计方法。Computers and Electronics in Agriculture","Computers and Electronics in Agriculture","2026-10-01T00:00:00Z",69,{"impact":17,"substance":100,"depth":19,"authority":101,"freshness":21,"relevant":22,"comment":102},18,14,"提出基于预测残差的水田轮式农机滑移参数实时估计方法，方法新颖且面向实际作业场景，但属细分技术论文，产业影响有限。",[104],{"name":96,"url":93},[27,28,106,107,31],"农业机械","水稻生产",{"code":109,"slug":110,"name":111,"path":112,"parent_code":113},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[115],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":117},{"candidates":118,"agricultural_evidence":47},[119,125,131],{"code":109,"score":100,"supported":47,"title":120,"abstract":122,"tags":124},[121],"paddy",[111,123,121],"稻田",[107],{"code":33,"score":126,"supported":47,"title":127,"abstract":129,"tags":130},15,[128],"agricultural machinery",[106,128],[106],{"code":46,"score":132,"supported":14,"title":133,"abstract":134,"tags":135},2,[],[],[27,28],[137,138],"水田 农业机械 滑移率 实时估计","预测残差 滑转参数 估计","水田农业机械滑移率实时估计-3974","10.1016\u002Fj.compag.2026.112478",{"doi":140,"openalex_id":142,"authors":143,"venue":96,"cited_by_count":76,"oa_url":9,"card":167,"direction":82,"ingested_from":85},"W7215087308",[144,147,150,152,154,156,159,161,164],{"name":145,"orcid":146},"Dawen Feng","https:\u002F\u002Forcid.org\u002F0000-0002-1307-8902",{"name":148,"orcid":149},"Boyang Wang","https:\u002F\u002Forcid.org\u002F0000-0003-3613-8792",{"name":151,"orcid":9},"Jing He",{"name":153,"orcid":9},"Yuqi Chen",{"name":155,"orcid":9},"Zhongxian Man",{"name":157,"orcid":158},"Jie He","https:\u002F\u002Forcid.org\u002F0000-0003-2134-0998",{"name":160,"orcid":9},"Zhaodi Liu",{"name":162,"orcid":163},"Gaolong Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7415-0867",{"name":165,"orcid":166},"Lian Hu","https:\u002F\u002Forcid.org\u002F0000-0002-4875-7868",{"tldr":168,"method":169,"finding":170,"direction":82,"opportunity":171},"提出基于预测-残差法的轮式农机水田滑移参数实时估计方法。","预测-残差法，结合农机运动学模型与实时传感数据估计滑移参数。","该方法能在水田复杂环境下实时准确估计农机滑移参数。","可探索多源传感融合与深度学习提升水田滑移估计鲁棒性，并迁移至其他松软地面农机。","2026-10-02T23:30:02.471920Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":9,"source_name":179,"source_url":176,"published_at":180,"category":12,"cover_url":9,"hotness":181,"is_selected":14,"score":182,"score_detail":183,"sources":187,"tags":192,"primary_topic":197,"secondary_topics":203,"classification":210,"search_phrases":258,"slug":261,"view_count":22,"doi":262,"paper":263,"created_at":273},4573,"APPLICATION OF ARTIFICIAL INTELLIGENCE AND AUTOMATION TECHNOLOGIES IN MODERN AGRICULTURAL MACHINERY","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23255982","The rapid development of artificial intelligence (AI), robotics, computer vision, Internet of Things (IoT), satellite navigation, remote sensing, and autonomous control systems is transforming the design and operation of modern agricultural machinery. Agricultural machinery is increasingly evolving from conventional mechanical equipment into intelligent cyber-physical systems capable of sensing field conditions, processing large volumes of data, making operational decisions, and executing agricultural tasks with limited human intervention. This article examines the application of artificial intelligence and automation technologies in tractors, harvesters, sprayers, robotic platforms, unmanned aerial vehicles, and other agricultural machinery. The study is based on a systematic analysis of scientific publications, international institutional reports, and documented agricultural technology adoption data. Particular attention is given to autonomous navigation, machine vision, precision spraying, variable-rate application, crop monitoring, yield prediction, robotic weeding, harvesting, and safety systems. Evidence from the United States demonstrates substantial growth in precision-agriculture adoption: automated guidance systems were used on more than half of the acreage planted to several major crops, while in 2023 guidance autosteering was used by 52 percent of midsize farms and 70 percent of large-scale crop-producing farms. Scientific literature also demonstrates the growing role of machine learning in yield estimation, nitrogen management, disease detection, and robotic perception. However, high acquisition costs, inadequate rural connectivity, data governance issues, cybersecurity, technical complexity, and safety risks remain significant barriers. The study concludes that AI-enabled agricultural machinery can contribute to higher operational efficiency, more precise input application, reduced operator fatigue, improved resource management, and greater agricultural sustainability, provided that technological development is accompanied by appropriate infrastructure, human-capacity development, interoperability standards, and safety regulation.","人工智能（AI）、机器人技术、计算机视觉、物联网（IoT）、卫星导航、遥感及自主控制系统的快速发展，正在深刻改变现代农业机械的设计与运行方式。农业机械正日益从传统机械设备演变为智能信息物理系统，能够感知田间条件、处理海量数据、做出作业决策，并在有限人工干预下执行农业任务。本文探讨了人工智能与自动化技术在拖拉机、联合收割机、喷雾机、机器人平台、无人机及其他农业机械中的应用。研究基于对科学文献、国际机构报告及已记录的农业技术采用数据的系统分析。重点关注自主导航、机器视觉、精准喷雾、变量施用、作物监测、产量预测、机器人除草、收获及安全系统。来自美国的证据表明，精准农业的采用率大幅增长：自动导航系统已应用于多种主要作物一半以上的种植面积，而2023年，52%的中型农场和70%的大型作物生产农场使用了导航自动转向技术。科学文献也表明，机器学习在产量估算、氮素管理、病害检测和机器人感知中的作用日益增强。然而，高昂的购置成本、农村网络连接不足、数据治理问题、网络安全、技术复杂性及安全风险仍是重要障碍。研究结论认为，在技术发展的同时配套适当的基础设施、人力能力建设、互操作性标准和安全监管，人工智能赋能的农业机械能够有助于提高作业效率、实现更精准的投入施用、减轻操作人员疲劳、改善资源管理并增强农业可持续性。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-09T00:00:00Z",25,81,{"impact":184,"substance":18,"depth":100,"authority":185,"freshness":20,"relevant":22,"comment":186},22,13,"系统梳理AI与自动化在农机领域应用的综述性论文，含美国精准农业采纳率等实证数据，对智慧农业主题聚合有较高参考价值。",[188,189],{"name":179,"url":176},{"name":190,"url":191},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[27,193,194,195,28,196],"农业人工智能","农业机器人","农业物联网","自动驾驶农机",{"code":198,"slug":199,"name":200,"path":201,"parent_code":202},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[204,205],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":206,"slug":207,"name":208,"path":209,"parent_code":202},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":40,"locked":14,"version":41,"method":42,"evidence":211},{"candidates":212,"agricultural_evidence":47},[213,222,228,235,242,247,253],{"code":198,"score":100,"supported":47,"title":214,"abstract":216,"tags":221},[215],"artificial intelligence",[217,218,219,215,220],"机器学习","计算机视觉","machine learning","computer vision",[193],{"code":33,"score":223,"supported":47,"title":224,"abstract":225,"tags":227},17,[128],[106,226,128],"收割机",[],{"code":206,"score":13,"supported":47,"title":229,"abstract":230,"tags":234},[],[231,232,233],"物联网","internet of things","IoT",[195],{"code":236,"score":88,"supported":47,"title":237,"abstract":238,"tags":241},"I3",[],[239,240],"遥感","remote sensing",[],{"code":46,"score":243,"supported":47,"title":244,"abstract":245,"tags":246},5,[],[28],[27,28],{"code":248,"score":60,"supported":47,"title":249,"abstract":250,"tags":252},"F4",[],[251],"除草",[],{"code":254,"score":22,"supported":14,"title":255,"abstract":256,"tags":257},"F6",[],[],[194],[259,260],"农业机械 人工智能 自动化","精准农业 自动驾驶 农机","农业机械人工智能自动化-4573","10.5281\u002Fzenodo.23255982",{"doi":262,"openalex_id":264,"authors":265,"venue":179,"cited_by_count":76,"oa_url":176,"card":268,"direction":82,"ingested_from":85},"W7221138004",[266],{"name":267,"orcid":9},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":269,"method":270,"finding":271,"direction":82,"opportunity":272},"系统综述AI与自动化技术在现代农机中的应用现状、效益与障碍。","系统分析科学文献、国际机构报告及美国精准农业采用数据。","AI农机可提升效率与可持续性，但成本、连接性、数据治理与安全仍是主要障碍。","可研究低成本AI农机方案、农村连接与数据治理标准，及人机协同安全监管框架。","2026-10-10T23:30:13.826410Z",{"id":275,"title":276,"url":277,"summary":278,"summary_zh":279,"content":9,"source_name":280,"source_url":277,"published_at":281,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":282,"score_detail":283,"sources":285,"tags":287,"primary_topic":291,"secondary_topics":292,"classification":293,"search_phrases":305,"slug":308,"view_count":76,"doi":309,"paper":310,"created_at":335},4408,"Tree-level yield mapping for nut trees harvested with shake-catch harvesters, Part II: Uncertainty estimation and tree identification","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104605","A system that generates yield maps at tree-level resolution for almond orchards and, more generally, shake-catch harvested nut crops is presented in a two-part series of papers. Part I developed an on-harvester yield monitor that uses a laser scanner to profile the continuous nut flow on the conveyor belt and a kernel-based deconvolution was introduced to recover individual-tree yields by fitting a mixture of parametric kernels whose superposition reproduces the measured flow. This paper (Part II) addresses two remaining challenges: (1) quantifying uncertainties in deconvolved, tree-level yields and (2) identifying individual trees that correspond to those yields. Because direct per-tree ground truth after harvest is impractical under mixed-flow conditions, a Monte Carlo method is introduced to experimentally record and weigh single-tree flow profiles and to construct virtual rows in a stochastic harvest simulator. This enabled uncertainty to be analysed systematically over simulated tree-to-tree harvest times and conveyor transport times. A sensor-fusion approach based on dual RTK-GNSS measurements and vibration-sensor timestamps is also introduced to associate harvesting events with individual trees and their deconvolved yield estimates for georeferenced map generation. The results showed that the PMG2-Tukey kernel generally produced lower mean relative errors and lower standard deviations than the other kernel families. Uncertainty was reduced as overlap between consecutive tree flows decreased. The proposed sensor-fusion method enabled the identification of individual trees and the generation of tree-level yield maps.","本文分两部分介绍了一套系统，该系统可为扁桃（almond）果园以及更广泛地，为振动采收-承接式（shake-catch）收获的坚果作物生成单树分辨率的产量图。第一部分开发了一种机上产量监测器，利用激光扫描仪对传送带上连续的坚果流进行剖面测量，并引入基于核函数的反卷积方法，通过拟合参数化核函数的混合模型来重建单树产量，该混合模型的叠加可再现实测流量。本文（第二部分）解决剩余的两个挑战：（1）量化反卷积后单树产量的不确定性；（2）识别与这些产量对应的单株树木。由于在混合流条件下，收获后直接获取每棵树的地面真值不切实际，因此引入了一种蒙特卡洛方法，通过实验记录和称量单树流量剖面，并在随机收获模拟器中构建虚拟行。这使得能够在模拟的树间收获时间和传送带运输时间上系统地分析不确定性。本文还引入了一种基于双RTK-GNSS测量和振动传感器时间戳的传感器融合方法，将收获事件与单株树木及其反卷积产量估计相关联，以生成地理参考产量图。结果表明，PMG2-Tukey核函数通常比其他核函数族产生更低平均相对误差和更低标准差。随着连续树流之间重叠的减少，不确定性降低。所提出的传感器融合方法能够实现单株树木的识别和单树产量图的生成。","Biosystems Engineering","2026-10-07T00:00:00Z",75,{"impact":17,"substance":184,"depth":100,"authority":101,"freshness":21,"relevant":22,"comment":284},"面向摇振采收坚果的树级产量制图与不确定性量化研究，方法新颖、结论可靠，但属细分领域技术论文，产业影响有限。",[286],{"name":280,"url":277},[27,28,288,289,290],"传感器融合","果园机械化","产量遥感",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":294},{"candidates":295,"agricultural_evidence":47},[296,301],{"code":33,"score":60,"supported":47,"title":297,"abstract":298,"tags":300},[],[299],"harvester",[],{"code":46,"score":132,"supported":14,"title":302,"abstract":303,"tags":304},[],[],[27,28],[306,307],"坚果 摇振采收 产量图","RTK-GNSS 果树 识别","坚果摇振采收产量图-4408","10.1016\u002Fj.biosystemseng.2026.104605",{"doi":309,"openalex_id":311,"authors":312,"venue":280,"cited_by_count":76,"oa_url":277,"card":330,"direction":82,"ingested_from":85},"W7220845516",[313,315,318,320,322,325,327],{"name":314,"orcid":9},"Villacrés Juan",{"name":316,"orcid":317},"Patrick H. Brown","https:\u002F\u002Forcid.org\u002F0000-0001-6857-8608",{"name":319,"orcid":9},"Yufang Jin",{"name":321,"orcid":9},"Jose L. Zárate-Valdez",{"name":323,"orcid":324},"Margherita A. Germani","https:\u002F\u002Forcid.org\u002F0009-0007-1775-3358",{"name":326,"orcid":9},"Ricardo Camargo",{"name":328,"orcid":329},"Stavros George Vougioukas","https:\u002F\u002Forcid.org\u002F0000-0003-2758-8900",{"tldr":331,"method":332,"finding":333,"direction":82,"opportunity":334},"提出蒙特卡洛不确定度估计与传感器融合树木识别方法，实现摇振采收坚果树的单树产量制图。","蒙特卡洛模拟、虚拟行构建、双RTK-GNSS与振动传感器融合。","PMG2-Tukey核误差更低，树流重叠减少可降低不确定度，传感器融合能识别单树并生成产量图。","可探索多传感器融合在复杂果园场景下的鲁棒性，以及实时单树产量监测与变量施肥决策的集成。","2026-10-08T23:30:05.803549Z",{"id":337,"title":338,"url":339,"summary":340,"summary_zh":341,"content":9,"source_name":342,"source_url":339,"published_at":343,"category":12,"cover_url":9,"hotness":13,"is_selected":47,"score":344,"score_detail":345,"sources":348,"tags":350,"primary_topic":353,"secondary_topics":354,"classification":355,"search_phrases":385,"slug":388,"view_count":53,"doi":389,"paper":390,"created_at":412},3564,"Research Progress on Intelligent Seeding Technology and Equipment: The Development of Seeders from Multi-Functional Integration to Agricultural Intelligent Agents","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16191884","Seeding constitutes a key crop-production operation that governs seed spatial arrangement, crop population structure, and potential yield formation, and forms the foundation of precise, efficient, and eco-friendly farming. However, field soil properties, regional climate, and crop agronomic requirements exhibit strong spatio-temporal heterogeneity. Conventional seeding operations based on manual experience and fixed preset parameters cannot meet the demands of large-scale precision agriculture. Enabled by progress in precision agriculture, intelligent sensing, artificial intelligence, and autonomous machinery, modern intelligent seeding systems integrate precision seed metering, high-precision environmental perception, and closed-loop dynamic self-regulation. Such systems can improve plant-spacing uniformity and enable precise seeding-depth control under standard open-field conditions, yet face noticeable performance limitations in GNSS-denied complex environments including dense crop canopies and greenhouses. This review outlines the evolutionary trajectory of seeding machinery and summarizes research progress regarding precision seeding, multi-functional equipment integration, multi-source information perception, and intelligent decision-making. Integrated design principles covering mechanical optimization, electronic control, and perception-driven decision systems are elaborated. Four developmental phases of seeding equipment are identified: mechanical precision operation, electronic intelligent regulation, multi-functional module integration, and intelligent cognitive integration. Current intelligent seeding technologies are constrained by limited adaptability to complex farmland conditions, unstable multi-source data fusion, insufficient long-term operational reliability, and high deployment costs across diverse scenarios, restricting their broad field-scale adoption. Future research should combine agronomic knowledge with artificial intelligence to improve environmental awareness and autonomous decision-making capability, develop low-cost, high-reliability integrated seeding equipment, and support the construction of intelligent agricultural machinery systems.","播种是决定种子空间分布、作物群体结构和潜在产量形成的关键作物生产环节，也是精准、高效、绿色农业的基础。然而，田间土壤特性、区域气候和作物农艺要求具有强烈的时空异质性。基于人工经验和固定预设参数的传统播种作业无法满足大规模精准农业的需求。在精准农业、智能感知、人工智能和自主机械等领域的进步推动下，现代智能播种系统集成了精密排种、高精度环境感知和闭环动态自适应调节。此类系统可在标准露地条件下提高株距均匀性并实现精量播种深度控制，但在全球导航卫星系统（GNSS）拒止的复杂环境中，包括密植作物冠层和温室，仍面临明显的性能局限。本文综述了播种机械的演进轨迹，总结了精量播种、多功能装备集成、多源信息感知和智能决策方面的研究进展。阐述了涵盖机械优化、电子控制和感知驱动决策系统的集成设计原则。识别出播种装备的四个发展阶段：机械精量作业、电子智能调控、多功能模块集成和智能认知集成。当前智能播种技术受限于对复杂农田条件的适应性不足、多源数据融合不稳定、长期运行可靠性不够以及多场景部署成本高昂，制约了其在田间的广泛规模化应用。未来研究应将农艺知识与人工智能相结合，提升环境感知和自主决策能力，开发低成本、高可靠性的集成播种装备，支撑智能农机体系建设。","Agronomy","2026-09-25T00:00:00Z",82,{"impact":184,"substance":346,"depth":100,"authority":185,"freshness":20,"relevant":22,"comment":347},21,"系统梳理智能播种装备从机械化到智能体四阶段演进，指出GNSS受限环境与多源数据融合瓶颈，对智慧农业装备研发有参考价值。",[349],{"name":342,"url":339},[27,193,351,28,352],"智能农机","智能播种",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":356},{"candidates":357,"agricultural_evidence":47},[358,362,368,375,379],{"code":46,"score":20,"supported":47,"title":359,"abstract":360,"tags":361},[],[28,49],[27,28],{"code":33,"score":363,"supported":47,"title":364,"abstract":365,"tags":367},7,[],[366,128],"农机",[351],{"code":369,"score":88,"supported":47,"title":370,"abstract":371,"tags":374},"H2",[],[372,373],"土壤","soil",[],{"code":198,"score":53,"supported":47,"title":376,"abstract":377,"tags":378},[],[215],[193],{"code":380,"score":60,"supported":47,"title":381,"abstract":382,"tags":384},"B7",[],[383],"温室",[],[386,387],"智能播种 装备","Agronomy 智能播种 装备","智能播种装备-3564","10.3390\u002Fagronomy16191884",{"doi":389,"openalex_id":391,"authors":392,"venue":342,"cited_by_count":76,"oa_url":339,"card":407,"direction":84,"ingested_from":85},"W7214297818",[393,395,397,399,401,404],{"name":394,"orcid":9},"Yuting Dong",{"name":396,"orcid":9},"Yapeng Wu",{"name":398,"orcid":9},"Shiguo Wang",{"name":400,"orcid":9},"Xiaohu Guo",{"name":402,"orcid":403},"Xin Lu","https:\u002F\u002Forcid.org\u002F0000-0003-4462-3472",{"name":405,"orcid":406},"Zhong Tang","https:\u002F\u002Forcid.org\u002F0000-0002-2724-115X",{"tldr":408,"method":409,"finding":410,"direction":82,"opportunity":411},"综述智能播种技术装备从多功能集成到农业智能体的四阶段演进及瓶颈。","文献综述，梳理精量播种、多源感知与智能决策的集成设计。","智能播种在开阔农田表现良好，但复杂环境下适应性、数据融合与成本仍受限。","GNSS拒止的冠层与温室环境下低成本高可靠感知与自主决策播种装备是研究空白。","2026-09-26T23:30:47.821171Z",{"id":414,"title":415,"url":416,"summary":417,"summary_zh":9,"content":9,"source_name":418,"source_url":416,"published_at":419,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":420,"score_detail":421,"sources":423,"tags":425,"primary_topic":428,"secondary_topics":429,"classification":431,"search_phrases":452,"slug":455,"view_count":22,"doi":456,"paper":457,"created_at":470},2909,"Field-scale prediction and spatial mapping of tractor tractive efficiency in tillage operations using machine learning","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10443-5","Field-scale prediction and spatial mapping of tractor tractive efficiency in tillage operations using machine learning。Precision Agriculture","Precision Agriculture","2026-09-18T00:00:00Z",71,{"impact":17,"substance":18,"depth":223,"authority":101,"freshness":20,"relevant":22,"comment":422},"发表于核心期刊 Precision Agriculture 的研究，用机器学习实现田块尺度拖拉机牵引效率预测与空间制图，方法新颖、对精准耕作有实用价值，但属细分领域进展，公共影响有限。",[424],{"name":418,"url":416},[27,193,28,426,427],"土壤耕作","农机作业",{"code":198,"slug":199,"name":200,"path":201,"parent_code":202},[430],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":432},{"candidates":433,"agricultural_evidence":47},[434,438,444,448],{"code":198,"score":17,"supported":47,"title":435,"abstract":436,"tags":437},[219],[219],[193],{"code":33,"score":439,"supported":47,"title":440,"abstract":442,"tags":443},11,[441],"tractor",[441],[],{"code":46,"score":243,"supported":47,"title":445,"abstract":446,"tags":447},[],[49],[27,28],{"code":369,"score":22,"supported":14,"title":449,"abstract":450,"tags":451},[],[],[426],[453,454],"拖拉机 牵引效率 机器学习","耕作 牵引效率 空间制图","拖拉机牵引效率机器学习-2909","10.1007\u002Fs11119-026-10443-5",{"doi":456,"openalex_id":458,"authors":459,"venue":418,"cited_by_count":76,"oa_url":9,"card":9,"direction":84,"ingested_from":85},"W7213532695",[460,462,464,466,468],{"name":461,"orcid":9},"So-Yun Gong",{"name":463,"orcid":9},"Seung-Min Baek",{"name":465,"orcid":9},"Seung-Yun Baek",{"name":467,"orcid":9},"Yong-Joo Kim",{"name":469,"orcid":9},"Wan-Soo Kim","2026-09-19T23:30:03.382845Z",{"id":472,"title":473,"url":474,"summary":475,"summary_zh":9,"content":476,"source_name":477,"source_url":9,"published_at":478,"category":479,"cover_url":9,"hotness":13,"is_selected":14,"score":98,"score_detail":480,"sources":482,"tags":484,"primary_topic":487,"secondary_topics":488,"classification":489,"search_phrases":509,"slug":512,"view_count":76,"doi":9,"paper":9,"created_at":513},5504,"山西因地制宜发展农业新质生产力 智能农机加速下乡","http:\u002F\u002Ftuopin.ce.cn\u002Fnews\u002F202610\u002Ft20261016_3260414.shtml","《山西省智慧农业行动计划（2026—2030年）》细化落地举措，围绕丘陵山地特点推进智慧农业先行先试、智慧农（牧、渔）场建设与技术创新。全省坡耕地1686.66万亩、占耕地总面积29.06%，为此加强丘陵山区智能农机研发引进，开展丘陵山区杂粮农机具研究，组建省级智慧农业产业技术体系。","**阅读提示**\n\n**我省依托科研院所与高校、企业联合攻关，量身研发本土化、适配型智能农机装备。当前，智能技术稳步融入耕种管收储全产业链条，推动农业生产实现精准管控、降本增效。同时，相关部门统筹推行农业社会化服务、落实专项政策补贴、开展定向技能培训等多项举措，打通技术落地堵点，构建智慧农业发展体系，让数字红利惠及广袤田野，书写黄土高原现代农业转型发展新答卷。**\n\n晨光破晓，阳光渐暖。太原市小店区北格镇梁家庄村的智慧农业大棚内，智能设备精准感知田间环境变化，保温卷帘随气温变化平稳升降，全程无需人工操作。\n\n这座由山西优班图科技股份有限公司联合山西农业大学（省农科院）共建的实验基地，是我省设施农业智能化升级的生动缩影，见证着传统农业向智慧农业转变的迭代蜕变。\n\n“大棚卷帘并非一次性全开，而是根据温度变化幅度循序渐进调节。”基地负责人李鹏飞介绍。大棚内各类传感器全天候实时采集温湿度、光照强度、二氧化碳浓度等核心环境数据，通过智能系统自动联动卷帘、通风、灌溉等设备，实现种植环境动态精准调控，彻底颠覆了传统“看天种地”的粗放模式，全面迈入“数据管棚”的智慧农业新阶段。\n\n农业新质生产力，核心是数字技术与农业生产的深度融合。2026年中央一号文件明确提出，要因地制宜发展农业新质生产力，促进人工智能与农业发展相结合，拓展无人机、物联网、机器人等应用场景。对标国家部署，《山西省智慧农业行动计划（2026—2030年）》细化落地举措，加快数字技术与农业深度融合，提升农业智能化水平，提高农业生产效率、资源利用率和农民收入，推动山西现代农业高质量发展。\n\n依托政策牵引、科研攻关、产业赋能，三晋大地各类智能农机、数字装备加速落地，贯穿农业生产全链条，为特色种养提质增效、群众稳定增收注入数字动能。\n\n**立足实际**\n\n**研发适配型智能农机**\n\n我省丘陵山地广、零散坡耕地多。第三次国土调查数据显示，全省坡耕地达1686.66万亩，占耕地总面积29.06%；梯田耕地达1421.68万亩，占耕地总面积24.49%。地块零散、坡度不均、田间道路狭窄，适配小块坡耕地的专用农机短缺，是丘陵山区农业提质增效的主要瓶颈。\n\n靶向破解本土生产难题，我省聚焦农业生产薄弱环节，联动科研机构、涉农企业、专业合作社精准攻关，量身研发适配丘陵山区作业的智能农机装备，补齐山区农业机械化、智能化短板。\n\n8月20日，娄烦县天池店乡南岔村马铃薯新优品标准化种植示范基地内，一垄垄马铃薯长势喜人，智能化种植设备的赋能成效尽显。\n\n“马铃薯长势旺盛，丘陵山区自走式马铃薯种植机发挥了重要作用。”娄烦润和美种植专业合作社负责人尤同义说。这款机具由山西省薯类产业技术体系专家赵旭志、贺孝兵团队研发，可一次性完成开沟、播种、起垄、镇压等一体化作业，通过起垄镇压保墒技术有效提升出苗率，精准解决了丘陵山区小地块大中型农机难进地、小型农机功能单一的行业痛点。\n\n不只马铃薯种植装备，多款适配山区地形的智能农机相继落地推广。针对农业生产各类细分痛点，山西农机科创持续发力、多点突破。\n\n由山西农业大学（省农科院）高粱研究所研究员白文斌团队研发的“大地智牛”全自主覆膜播种机器人，已在阳高、寿阳、襄垣、武乡、永和等多个山区县2000余亩耕地验证使用。该设备集成施肥、覆膜、铺设滴灌带、播种、覆土五道工序，可在6°及以下坡度耕地稳定作业，填补了国内丘陵山区覆膜播种智能农机的空白。设备作业时速稳定，可根据不规则曲线地块自主规划作业路径、动态调整速度，兼顾作业精度与生产效率。\n\n山西沃勃农业发展有限公司与山西农业大学（省农科院）园艺学院等科研部门，聚焦温室果蔬采摘用工紧缺、成本偏高、果实损伤率高的难题，融合多模态视觉与三维定位技术，成功研发履带式自主导航采摘机器人，实现果实精准识别定位、机械臂柔性低损采摘。一台机器人可替代2—3名采摘工，单棚年节约人力成本1.6万元以上。\n\n围绕玉米全生育期绿色除草需求，五台县城园丰农机制造有限公司联合清华大学天津高端装备研究所等单位，集成全地形移动平台、多模态感知组件、智能决策控制器、高精度导航系统、仿生物理除草与松土机构、复合能源系统及物联网作业平台，成功制造覆膜玉米智能除草机器人。该款机器人可自主建立地块与作物行地图，精准识别玉米苗株、杂草、地膜和障碍物，在实现清除田间杂草的同时完成土壤浅松。\n\n太原科技大学和山西农谷现代农业装备科技有限公司等单位，成功研制黄花菜智能采摘机器人。此款采摘机器人依托国际领先的并联机器人技术，可保障高速高精度作业；视觉系统利用高感光相机与多光谱融合增强方法，能快速识别花蕾进行轨迹规划；末端执行器采用了力位混合控制技术，可精准定位采摘点实现无损采摘；控制系统融合激光雷达等多传感器，可实现复杂田块自主导航与动态避障。\n\n一系列本土化、适配型智能农机的研发与落地，精准破解了我省丘陵山地农业生产的诸多短板，为推动山地农业从传统人工劳作向智能高效作业转型提供了全新选择。\n\n**全链赋能**\n\n**贯穿耕管收储全环节**\n\n8月23日，运城市盐湖区龙居镇南花村百忍堂冷库分选车间内，智能分拣设备全速运转，一颗颗红香酥梨经由传送轨道有序输送，通过多维度智能扫描，系统综合比对果体大小、重量、色泽、表皮瑕疵、内部品质等多项参数，依托智能算法自动分级、精准归类，实现标准化分选。\n\n“人工分拣没有统一标准，果品品相、大小参差不齐，很多优质梨果因分拣不规范，卖不上高端市场价。”冷库负责人张蓓坦言。智能分拣设备的投入使用，彻底补齐人工分拣的短板，让优质果品匹配高端价值，有效助力农户增收、产业提质。如今，智能化、数字化技术已全面渗透农业种植、田间管护、采收加工、仓储保鲜全链条，从单一设备示范应用，转向全流程、系统化智慧升级。\n\n田间管护更精细。春季小麦管护关键期，曲沃县曲村镇下坞村麦田里，搭载北斗定位的自由式喷灌机器人灵活作业，180米直径喷射范围实现360°无死角精准供水。相较于传统灌溉模式，设备节水55%左右，作业效率提升85%以上，既省水省力，又保障麦苗均匀受水、长势均衡。小麦灌浆期，临猗县统筹8支专业飞防队伍、119架植保无人机，对全县26.97万亩小麦开展“一喷三防”全覆盖作业，无人机按精准航线低空作业，均匀喷施药剂与叶面肥，筑牢粮食丰产防线。\n\n育苗耕种更高效。山西六新科技有限公司等单位联合研发的数智育苗机器人，搭载双模式自由切换、地块仿形自适应两大核心技术。设备可快速切换条播、撒播作业模式，自动根据苗圃凹凸地表调整播种高度，保证落种均匀；内置定量分料结构，适配大小不同农作物、蔬菜、林木、药材种子，可同步完成播种、覆土、轻度镇压一体化作业，整机机动灵活，适合露天苗圃大范围连续作业。\n\n田间作业更智能。山西恒赫百旺智能科技有限公司研发的丘陵山地无人设备，集成智能感知、精准决策、自主执行功能，可智能生成与坡地、梯田契合的自主飞行航线，实现定高、定速全自主飞行与定量施药作业。\n\n仓储保鲜更安全。太原先元科技有限公司等联合打造的粮食干燥仓储全流程管理系统，以中央集控调度机器人为核心，可智能识别粮食水分，动态调节作业温度、风量，精准完成粮食干燥处理；仓储阶段可24小时自动监测粮情、智能翻粮，全方位保障粮食存储安全。\n\n为持续拓宽智慧农业应用场景，今年7月，省工信厅、省农业农村厅联合发文，聚焦种植、养殖、农产品初加工三大重点领域，启动农业领域机器人典型应用场景遴选工作，以标杆场景引领农机装备创新迭代，推动全省农业机械化、智能化高质量发展。\n\n在北斗导航、智能感知、精准算法等技术支持下，智慧农机已成为三晋大地耕、种、管、收的生力军。\n\n左权县探索建立“县级统筹、农机到户、技术护航、补贴直达”四位一体工作模式，投放300余台配备北斗导航、无人驾驶功能的智能农机，实现耕、种、管、收全链条作业。据该县农业农村局工作人员介绍，智能农机依托导航系统规划作业路径，行距误差控制在2厘米以内，作业深度偏差不超1厘米，可有效杜绝漏耕、重耕问题，种子利用率提升15%以上，农业生产精细化、标准化水平显著提升。\n\n怀仁市依托智慧农机载体，以数据互联互通为支撑，搭建起涵盖耕、种、管、收各环节的智慧农事服务体系，助力当地农业由传统“会种地”向现代“慧种地”迭代升级，大力推行可感知、可控制、可决策的智慧农业发展模式。该市农业机械化发展中心主任郭亮介绍，自2023年起，当地逐步推广农机无人驾驶辅助系统，现有设备保有量约200套，有效降低人工与农资投入成本。\n\n**破解堵点**\n\n**构建长效发展体系**\n\n智能农机、数字装备的广泛应用，为我省农业转型升级、提质增效提供了有力支撑，但高端智能设备造价偏高、小农户购置能力不足等问题，仍然制约着全省智慧农业的规模化普及、全覆盖推广。\n\n“公司研发的履带式智能植保无人机可自主规划路线、24小时不间断作业，性能稳定高效，但价格偏高，目前主要推广于广西、海南等规模化果园种植区，省内仅万荣、临猗等林果大县落地应用。”山西添翼创新科技有限公司总经理闫太平道出行业共性难题。\n\n山西优班图科技股份有限公司等联合研发的智能水肥药一体机同样面临推广困境，设备可根据作物长势精准施肥喷药、节本增效，却因购置成本较高，让众多小农户望而却步。\n\n针对单家独户购置成本高、设备利用率低的痛点，各地立足小农户多、地块碎片化的农业实情，创新探索“智能农机共享+农业生产托管”模式，将高端智能装备转化为社会化服务的“公共农具”，让小农户便捷享受数字农业红利。\n\n社会化服务提质增效，助力普惠共享。临汾市翼城县益农区域农机社会化服务中心，依托全套智能农机装备开展规模化农业托管服务，全程托管土地2万余亩，单环节托管超10万亩，辐射带动周边大量小农户。通过统一智能化作业，实现亩均节本50元以上、增产15%，相关实践成功入选全国农机社会化服务典型案例，为全省智慧农机普惠化推广提供可借鉴的经验。\n\n政策精准托举，打通落地壁垒。相关部门积极落实农机购置与应用补贴政策，将农用无人机、田间智能监测设备、智能耕作机械等新型装备纳入补贴范围，推行优机优补、精准补贴，大幅降低经营主体购置成本。\n\n今年8月，省农机发展中心在清徐县举办农机购置与应用补贴产品现场演示评价活动。本次活动旨在严格规范补贴产品准入标准，夯实农机购置补贴政策实施基础，推动先进适用农机装备的推广应用。参评产品涵盖农用（植保）无人驾驶航空器、农用北斗辅助驾驶系统、北斗农机作业监测终端、田园管理机及微耕机五大品类148个型号。\n\n“我们研发智能农机的最终目标是让农民成为‘白领’，坐在办公室就能种地。”闫太平说，智能农机的广泛落地、高效应用，离不开高素质专业人才的支撑。\n\n今年7月，省农业农村厅印发了《2026年全省高素质农民培育工作实施方案》。根据方案，培育工作专门面向参与智慧农业项目的新型农业经营主体骨干、青年农民骨干、返乡入乡创业群体等重点人群，紧扣我省智慧农业发展核心需求，重点围绕智慧农业发展理念、物联网、大数据、人工智能等信息技术应用、智能农业装备应用、智慧农（牧、渔）场发展等开展培训，破解智能农机“无人会用、无人善用”的人才短板。\n\n同时，全省各地也在积极探索适应智能农机操作的高素质农民培养新途径。朔州市举办的农机手技能提升培训班专门增设北斗导航辅助驾驶、作业质量监测等智能终端操作课程，帮助农机手适应现代农业发展需求。\n\n交口县组织技术人员将无人机驾驶技术培训直接送到田间地头，围绕农业无人机的核心应用场景，从设备构造、飞行原理、安全规范到植保作业参数设置，进行从理论到实操的全流程教学。乡宁县培育30名持证飞手，组建本土专业技术队伍，可熟练操控植保无人机开展梯田智能化作业。\n\n从单点设备示范突破，到全链条技术赋能，我省逐步探索出一条“科研攻关+政策托举+服务赋能”的智慧农业发展路径。曾经依赖人力、看天吃饭的传统农业，正被数据驱动、智能管控的现代化农业重塑。智能农机不再是大型示范基地的专属配置，而是持续下沉千家万户田块的普惠工具，持续释放农业新质生产力效能，为黄土高原旱作农业现代化、三晋乡村全面振兴注入源源不断的数字动能。\n\n![Image 1](https:\u002F\u002Fnginx-sxrb.app.sxrbw.com\u002Fsxrb\u002Fepaper\u002Fpc\u002Fpic\u002F202610\u002F16\u002F49417_0ab510f3-011f-4e35-afe4-86c9214ec13f_l.jpg)\n\n大棚专用自主水肥药农情监测机器人正在进行喷药作业。本报记者 崔如意摄\n\n![Image 2](https:\u002F\u002Fnginx-sxrb.app.sxrbw.com\u002Fsxrb\u002Fepaper\u002Fpc\u002Fpic\u002F202610\u002F16\u002F49417_2321f7b6-ed58-493b-9175-92dbb5561970_l.jpg)\n\n丘陵山区自走式马铃薯种植机正在作业。本报记者 崔如意摄\n\n**相关链接**\n\n《山西省智慧农业行动计划（2026—2030年）》提出，我省将：\n\n1.推进智慧农业先行先试。开展“人工智能+”行动，打造以新技术集群和大模型为支撑的大田种植、设施农业、规模养殖等应用场景，示范推广智能化农机装备。鼓励各市、县因地制宜打造1—2个种植业、畜牧业、渔业等智慧农业应用场景。鼓励建设智慧农业应用引领区，推进种植方案与物联网智能装备、智能灌溉系统、智能农机设备、智慧管理平台等耦合应用。用好线上线下宣传渠道，总结推广智慧农业发展经验做法。\n\n2.推进智慧农（牧、渔）场建设。实施农（牧、渔）场智慧赋能计划，培育一批不同技术模式的智慧农（牧、渔）场。发挥规模主体带动作用，培育一批智慧农业发展社会化服务主体，集成推广智慧农业新技术、新装备。\n\n3.推进智慧农业技术创新。支持科研院校开展智慧农业技术模式研究、集成与推广，利用农业生产过程的数字化仿真、推演，形成最优种植、养殖管理方案。加快智能农机装备技术攻关，加强丘陵山区智能农机装备研发、引进，开展丘陵山区杂粮农机具研究。推进国家级智慧农业重点实验室建设。组建省级智慧农业产业技术体系，为智慧农业发展提供技术支撑。培育一批智慧农业领域科技领军企业，推进智慧农业技术研发、成果转化和产业化应用。促进应用场景需求和研发服务能力对接，推动新技术新产品应用推广。","中国经济网扶贫频道 2026-10-16","2026-10-16T00:00:00Z","报道",{"impact":18,"substance":100,"depth":19,"authority":439,"freshness":53,"relevant":22,"comment":481},"省级智慧农业综合报道，智能农机研发与全链应用案例扎实、信源多元，但时效偏旧，适合作为主题聚合素材而非当日精选。",[483],{"name":477,"url":474},[27,194,351,485,486],"丘陵山区","农业社会化服务",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":490},{"candidates":491,"agricultural_evidence":47},[492,496,500,504],{"code":33,"score":17,"supported":47,"title":493,"abstract":494,"tags":495},[366],[366],[351],{"code":46,"score":53,"supported":47,"title":497,"abstract":498,"tags":499},[],[27],[27],{"code":254,"score":22,"supported":14,"title":501,"abstract":502,"tags":503},[],[],[194],{"code":505,"score":22,"supported":14,"title":506,"abstract":507,"tags":508},"G2",[],[],[486],[510,511],"山西 智能农机 丘陵山区","山西 农业新质生产力 智慧农业","山西智能农机丘陵山区-5504","2026-10-22T00:06:58.505233Z"]