[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3564":3,"related-3564":113},{"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":81,"slug":84,"view_count":69,"doi":85,"paper":86,"created_at":112},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。",null,"Agronomy","2026-09-25T00:00:00Z","论文",10,true,82,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,21,18,13,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","智能农机","精准农业","智能播种",{"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":41,"version":42,"method":43,"evidence":44},"classified",false,"2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":14},[46,52,59,67,74],{"code":47,"score":21,"supported":14,"title":48,"abstract":49,"tags":51},"I1",[],[30,50],"precision agriculture",[27,30],{"code":33,"score":53,"supported":14,"title":54,"abstract":55,"tags":58},7,[],[56,57],"农机","agricultural machinery",[29],{"code":60,"score":61,"supported":14,"title":62,"abstract":63,"tags":66},"H2",6,[],[64,65],"土壤","soil",[],{"code":68,"score":69,"supported":14,"title":70,"abstract":71,"tags":73},"I2",4,[],[72],"artificial intelligence",[28],{"code":75,"score":76,"supported":14,"title":77,"abstract":78,"tags":80},"B7",3,[],[79],"温室",[],[82,83],"智能播种 装备","Agronomy 智能播种 装备","智能播种装备-3564","10.3390\u002Fagronomy16191884",{"doi":85,"openalex_id":87,"authors":88,"venue":10,"cited_by_count":103,"oa_url":6,"card":104,"direction":110,"ingested_from":111},"W7214297818",[89,91,93,95,97,100],{"name":90,"orcid":9},"Yuting Dong",{"name":92,"orcid":9},"Yapeng Wu",{"name":94,"orcid":9},"Shiguo Wang",{"name":96,"orcid":9},"Xiaohu Guo",{"name":98,"orcid":99},"Xin Lu","https:\u002F\u002Forcid.org\u002F0000-0003-4462-3472",{"name":101,"orcid":102},"Zhong Tang","https:\u002F\u002Forcid.org\u002F0000-0002-2724-115X",0,{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"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。","智慧农业 \u002F 农业物联网","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。","农业人工智能与决策模型","openalex","2026-09-26T23:30:47.821171Z",{"total":61,"page":22,"page_size":61,"items":114},[115,159,198,294,353,408],{"id":116,"title":117,"url":118,"summary":119,"summary_zh":9,"content":9,"source_name":120,"source_url":9,"published_at":9,"category":121,"cover_url":9,"hotness":13,"is_selected":14,"score":122,"score_detail":123,"sources":128,"tags":130,"primary_topic":133,"secondary_topics":134,"classification":135,"search_phrases":153,"slug":156,"view_count":157,"doi":9,"paper":9,"created_at":158},5117,"中国农科院南京农机所发布农机智能引擎AMES1000 破解智能农机底层核心技术瓶颈","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fmtxw\u002F152411b343ff453d841275f2d2a3999b.htm","农业农村部南京农业机械化研究所发布拥有完全自主知识产权的农机智能引擎AMES1000新产品,破解我国智能农机底层核心技术瓶颈。南京农机所创新提出农机智能引擎AMES概念,联合中农国科(南京)智能装备科技有限公司完成产品落地,实现自动驾驶、处方图精准作业、多机协同、故障诊断等六大核心功能,具备高稳定、高可靠、易扩展等优势,可广泛适配各类农机机型,覆盖耕、种、管、收全农业生产场景,目前已在拖拉机、插秧机、田间管理机器人等装备上开展田间验证与熟化。","新华社 中国农业科学院 2026 年 10 月","报道",89,{"impact":124,"substance":17,"depth":125,"authority":126,"freshness":21,"relevant":22,"comment":127},27,17,15,"国家级科研机构发布完全自主知识产权的农机智能引擎，破解智能农机底层核心技术瓶颈，属产业级重大突破，信息增量与权威度俱佳，值得进入每日精选。",[129],{"name":120,"url":118},[27,28,29,131,132],"自主可控","农机装备",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":136},{"candidates":137,"agricultural_evidence":14},[138,145,149],{"code":33,"score":139,"supported":14,"title":140,"abstract":141,"tags":144},19,[56],[56,142,143],"农业机械","机械化",[132,29],{"code":47,"score":22,"supported":41,"title":146,"abstract":147,"tags":148},[],[],[27],{"code":68,"score":22,"supported":41,"title":150,"abstract":151,"tags":152},[],[],[28],[154,155],"南京农机所 AMES1000","农机智能引擎 AMES","南京农机所AMES1000-5117",2,"2026-10-17T00:13:39.688195Z",{"id":160,"title":161,"url":162,"summary":163,"summary_zh":9,"content":164,"source_name":165,"source_url":9,"published_at":166,"category":121,"cover_url":9,"hotness":13,"is_selected":41,"score":167,"score_detail":168,"sources":171,"tags":173,"primary_topic":176,"secondary_topics":177,"classification":178,"search_phrases":193,"slug":196,"view_count":76,"doi":9,"paper":9,"created_at":197},4768,"江苏省农科院与巴西Embrapa等共建中国—巴西人工智能应用合作中心","https:\u002F\u002Fwww.nongye31.com\u002Fnews\u002F2596.html","江苏省农业科学院与巴西农牧业研究公司Embrapa、南京农业大学、国机数科、国际民众合作协会签署合作谅解备忘录,共建中国—巴西人工智能应用合作中心。中心聚焦中小型智能农机、农业AI平台、作物生长模型等方向,依托巴西家庭农业场景开展技术验证,打造智慧农业南南合作标杆。","2026-10-09 09:09:27来源：江苏省农科院国合处  关键词：智慧农业中巴农业合作农业人工智能阅读量：35\n\n导读：江苏省农科院联合巴西 Embrapa 等多家单位签约共建中国 — 巴西人工智能应用合作中心，聚焦中小型智能农机、农业 AI 平台等联合攻关，依托巴西家庭农业场景开展技术验证与成果转化，打造智慧农业南南合作标杆。\n\n近日，江苏省农业科学院与巴西农牧业研究公司(Embrapa)、南京农业大学、国机数科、国际民众合作协会共同签署合作谅解备忘录，携手共建“中国—巴西人工智能应用合作中心”，以科技创新赋能全球粮食安全与农业现代化。江苏省农科院党委委员、副院长孙洪武出席签约仪式并作主旨发言。\n\n孙洪武重点介绍了江苏省农科院在科技服务与国际合作方面的工作成效。他建议合作各方应落实好人员互访、专家考察、联合研讨等任务，聚焦中小型智能农机、农业AI平台、作物生长模型等方向开展联合攻关，建设好智慧农机服务中心示范点，搭建好人员交流桥梁，携手推进合作中心建设，为深化中巴农业科技合作、保障全球粮食安全、推动农业现代化贡献力量。\n\n巴西农牧业研究公司总裁席尔维娅·马苏鲁哈介绍，Embrapa是拉美地区最大的国家级公立农业科研机构，巴西家庭农户占比超七成，热带农业种植与生产场景丰富，拥有成熟的农业试点研发体系。她期待深度依托合作中心，与包括江苏省农业科学院在内的中方单位深化协作，开展田间技术验证、农业数据共享与技术成果转化，打造金砖农业合作标杆案例。\n\n中国—巴西人工智能应用合作中心将深度整合产学研优势资源，以巴西家庭农业实际应用场景为需求牵引，以中方智慧农业先进技术为重要支撑，推进中巴双方在联合科研攻关、示范基地建设、人才交流培养等方面实现双向赋能、双向落地，探索形成可复制、可推广的智慧农业南南合作新模式。\n\n引用： 共建中国—巴西人工智能应用合作中心 助力智慧农业南南合作 江苏省农业科学院 [引用日期：2026年10月8日]\n\n本文系转载，转发此文目的在于促进信息交流，不存在盈利性目的，此文观点与本站立场无关，不承担任何责任，不做任何投资和交易根据。如无意侵犯媒体或个人知识产权请告知我们，我们将在第一时间做删除处理。\n\n上一篇：混动争锋、沃野竞技！200马力及以上混合动力拖拉机田间实地试验验证活动即将举行","农业商情网 2026-10-08","2026-10-08T00:00:00Z",73,{"impact":17,"substance":19,"depth":126,"authority":169,"freshness":61,"relevant":22,"comment":170},12,"省级科研机构牵头的中巴智慧农业国际合作，聚焦智能农机与农业AI平台，具行业示范价值，但属签约启动阶段、落地成果尚待观察。",[172],{"name":165,"url":162},[27,28,29,174,175],"南南合作","中巴合作",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":179},{"candidates":180,"agricultural_evidence":14},[181,185,189],{"code":33,"score":69,"supported":14,"title":182,"abstract":183,"tags":184},[],[56],[29],{"code":47,"score":69,"supported":14,"title":186,"abstract":187,"tags":188},[],[27],[27],{"code":68,"score":22,"supported":41,"title":190,"abstract":191,"tags":192},[],[],[28],[194,195],"江苏省农科院 Embrapa 人工智能","农业人工智能 中巴合作 南南合作 智慧农业","江苏省农科院Embrapa人工智能-4768","2026-10-13T00:08:35.638471Z",{"id":199,"title":200,"url":201,"summary":202,"summary_zh":203,"content":9,"source_name":204,"source_url":201,"published_at":205,"category":12,"cover_url":9,"hotness":206,"is_selected":41,"score":207,"score_detail":208,"sources":211,"tags":216,"primary_topic":220,"secondary_topics":225,"classification":232,"search_phrases":278,"slug":281,"view_count":22,"doi":282,"paper":283,"created_at":293},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":17,"substance":209,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":210},20,"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。",[212,213],{"name":204,"url":201},{"name":214,"url":215},"International Bulletin of Engineering and 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16:09\n\n为学习农业领域前沿科技创新成果，加强跨区域科研交流合作，9月17日至19日，2026年华东地区农学会学术年会在江苏扬州中兴天成国际酒店召开。本次年会以“AI赋能农业新质生产力发展”为主题，汇聚华东六省一市农业科研院所、高等院校、农技推广部门及农业科技企业专家代表。江苏沿江地区农科所王顺祥所长、陆兵副所长带队，组织10余名科研人员赴扬州参会交流。\n\n会议由江苏省农学会、扬州大学承办。开幕式后，主旨报告环节大咖云集。中国工程院院士张洪程作《水稻绿色丰产无人化栽培技术》报告；日本工程院院士邓明聪分享人工智能与非线性控制技术融合及其在农业工程领域的应用；南京农业大学陆明洲教授围绕畜禽养殖智能化技术、装备与农业大模型展开交流；南京工业大学徐虹教授介绍基于AI赋能的新型生物刺激素绿色生物制造研究。专家们从智慧种植、智能养殖、生物制造等方面，展现AI技术赋能现代农业的最新进展。\n\n9月18日下午，年会同步开设四大平行专题论坛：智慧种植·丰产提质、智慧养殖·节本增效、智造农机·装备赋能、智驱加工·延链强链。智慧种植专场聚焦AI大田生产管控、农业碳汇、水肥智能决策、数字化育种、大豆抗逆新品种选育等研究；智慧养殖专场研讨畜禽水产智能感知、稻虾生态种养智慧管控、畜禽行为智能识别与养殖环境调控技术；智造农机专场交流名优茶采摘机器人、设施蔬菜装备、智能育种加速器、丘陵山地农机装备研发实践；智驱加工专场围绕食品加工数字孪生、全溶性植物蛋白开发、果蔬及特色农产品精深加工技术开展研讨，覆盖农业生产全链条数字化转型。\n\n参会期间，我所科研人员结合自身研究方向，分赴各专题会场认真聆听报告，积极参与学术讨论，与华东地区同行专家深入交流，重点学习智慧农业装备、绿色高效栽培、农产品加工增值等领域创新成果，探讨科研项目协同合作路径。\n\n此次参会，有效拓宽了科研人员学术视野，进一步搭建起跨区域科研协作桥梁。下一步，江苏沿江地区农科所将充分吸收本次年会的新理念、新技术，立足南通沿江农业产业实际需求，持续推进农业科技创新，加快科技成果示范转化，为培育农业新质生产力、推动区域农业高质量发展贡献科研力量。\n\n[![Image 1: 1.jpeg](https:\u002F\u002Fyj.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F695893685\u002Fpicture\u002F20268\u002FS254e30553383497089ded7cfd802aec4-800.jpeg)](https:\u002F\u002Fyj.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F695893685\u002Fpicture\u002F20268\u002F254e30553383497089ded7cfd802aec4.jpeg)\n\n（图文：唐明霞；责任编辑：陈满峰；审核人：陈国清）","江苏省农业科学院沿江地区农业科学研究所","2026-09-20T10:00:00Z",58,{"impact":169,"substance":306,"depth":169,"authority":363,"freshness":307,"relevant":22,"comment":364},11,"区域性学术年会参会通稿，信息以会议议程与报告概览为主，无新增数据或独家结论，但AI赋能农业主题契合度高、时效新，可作为智慧农业主题页的聚合素材。",[366],{"name":359,"url":356},[27,28,368,29,369],"新质生产力","数字育种",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":373},{"candidates":374,"agricultural_evidence":14},[375,379,385,391,395,399],{"code":33,"score":69,"supported":14,"title":376,"abstract":377,"tags":378},[],[56],[29],{"code":380,"score":76,"supported":14,"title":381,"abstract":382,"tags":384},"A1",[],[383],"水稻",[],{"code":386,"score":76,"supported":14,"title":387,"abstract":388,"tags":390},"G1",[],[389],"农技推广",[],{"code":47,"score":22,"supported":41,"title":392,"abstract":393,"tags":394},[],[],[27],{"code":68,"score":22,"supported":41,"title":396,"abstract":397,"tags":398},[],[],[28],{"code":344,"score":22,"supported":41,"title":400,"abstract":401,"tags":402},[],[],[368],[404,405],"华东地区农学会 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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。",[419],{"name":413,"url":411},[27,28,30,421,422],"土壤耕作","农机作业",{"code":68,"slug":221,"name":222,"path":223,"parent_code":224},[425],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":41,"version":42,"method":43,"evidence":427},{"candidates":428,"agricultural_evidence":14},[429,433,438,442],{"code":68,"score":169,"supported":14,"title":430,"abstract":431,"tags":432},[240],[240],[28],{"code":33,"score":363,"supported":14,"title":434,"abstract":436,"tags":437},[435],"tractor",[435],[],{"code":47,"score":263,"supported":14,"title":439,"abstract":440,"tags":441},[],[50],[27,30],{"code":60,"score":22,"supported":41,"title":443,"abstract":444,"tags":445},[],[],[421],[447,448],"拖拉机 牵引效率 机器学习","耕作 牵引效率 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