[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3604":3,"related-3604":52},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":35,"paper":36,"created_at":51},3604,"HEART-Net: An ultra-lightweight multimodal neural network for on-device cattle live-weight estimation and precision phytogenic dosing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102587","HEART-Net: An ultra-lightweight multimodal neural network for on-device cattle live-weight estimation and precision phytogenic dosing。Smart Agricultural Technology",null,"Smart Agricultural Technology","2026-09-25T00:00:00Z","论文",10,false,77,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,20,13,8,1,"提出超轻量多模态网络实现端侧肉牛活重估计与精准植物源饲料投喂，方法新颖且面向实际养殖场景，具备智慧畜牧领域参考价值。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","智能装备","畜牧养殖","精准饲喂",[31,32],"HEART-Net 肉牛 体重估计","肉牛 活重估计 植物源饲料","HEART-Net肉牛体重估计-3604",0,"10.1016\u002Fj.atech.2026.102587",{"doi":35,"openalex_id":37,"authors":38,"venue":9,"cited_by_count":34,"oa_url":6,"card":8,"direction":8,"ingested_from":50},"W7214389961",[39,42,44,46,48],{"name":40,"orcid":41},"Ashif Shuvo","https:\u002F\u002Forcid.org\u002F0009-0003-5734-1519",{"name":43,"orcid":8},"Sakibul Hasan",{"name":45,"orcid":8},"Abir Anjum",{"name":47,"orcid":8},"Asaduzzaman Shahed",{"name":49,"orcid":8},"Mohammad Al-Mamun","openalex","2026-09-27T23:30:05.905976Z",{"total":53,"page":20,"page_size":53,"items":54},6,[55,105,158,199,224,253],{"id":56,"title":57,"url":58,"summary":59,"summary_zh":60,"content":8,"source_name":61,"source_url":58,"published_at":62,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":63,"score_detail":64,"sources":67,"tags":69,"search_phrases":72,"slug":75,"view_count":34,"doi":76,"paper":77,"created_at":104},3478,"Closed-loop artificial intelligence agents for animal epidemic prediction and decision support in livestock farming: a review","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffvets.2026.1968632","Animal diseases continuously threaten livestock production and public health, yet current surveillance approaches remain largely passive and fragmented. Conventional artificial intelligence models typically function as open-loop predictors, delivering static outputs that fail to accommodate the dynamic operational needs of veterinarians in real-world farm settings. This review explores an emerging paradigm of AI agents for animal disease monitoring, emphasizing how such agents can transition from passive observation to active intervention through a closed-loop perception–decision–feedback architecture. We first classify multi-scale sensing technologies: at the micro-scale, automated molecular diagnostics and biosensors; at the macro-scale, computer-vision-based phenotyping. These technologies collectively provide the data streams required for continuous surveillance. The review then discusses how multi-modal inputs, time-series forecasting, resource-aware decision-making, and reinforcement learning can be integrated into an agent-based workflow. Importantly, most current veterinary research evidence supports only the individual components of this framework, rather than the deployment of fully autonomous, iteratively evolving closed-loop agents in actual field environments. Translating closed-loop AI from a conceptual framework into a reliable veterinary decision-support tool will therefore require further advances in validation, standardization, governance, and human oversight.","动物疫病持续威胁着畜牧生产与公共卫生，然而当前的监测手段在很大程度上仍是被动且碎片化的。传统人工智能模型通常作为开环预测器运行，输出静态结果，无法满足兽医在真实养殖场景中动态的操作需求。本文综述了动物疫病监测中新兴的人工智能代理（AI agents）范式，重点探讨此类代理如何通过“感知—决策—反馈”的闭环架构，从被动观察转向主动干预。我们首先对多尺度传感技术进行分类：在微观尺度上，包括自动化分子诊断与生物传感器；在宏观尺度上，包括基于计算机视觉的表型分析。这些技术共同提供了持续监测所需的数据流。随后，本文讨论了多模态输入、时间序列预测、资源感知决策以及强化学习如何整合到基于代理的工作流程中。重要的是，当前大多数兽医研究证据仅支持该框架的各个独立组件，而非在实际现场环境中部署完全自主、迭代演化的闭环代理。因此，要将闭环人工智能从概念框架转化为可靠的兽医决策支持工具，还需在验证、标准化、治理和人工监督方面取得进一步进展。","Frontiers in Veterinary Science","2026-09-23T00:00:00Z",78,{"impact":16,"substance":17,"depth":16,"authority":65,"freshness":19,"relevant":20,"comment":66},14,"系统梳理闭环AI智能体在动物疫病监测与决策中的应用框架，指出落地瓶颈，对智慧畜牧有参考价值。",[68],{"name":61,"url":58},[25,26,70,28,71],"动物疫病防控","智能决策",[73,74],"动物疫病 AI 预测","闭环智能体 畜牧","动物疫病AI预测-3478","10.3389\u002Ffvets.2026.1968632",{"doi":76,"openalex_id":78,"authors":79,"venue":61,"cited_by_count":34,"oa_url":96,"card":97,"direction":103,"ingested_from":50},"W7214118204",[80,83,85,87,90,93],{"name":81,"orcid":82},"Yuzhi Wang","https:\u002F\u002Forcid.org\u002F0009-0001-8566-7810",{"name":84,"orcid":8},"Yingtong Zhou",{"name":86,"orcid":8},"Liyu Li",{"name":88,"orcid":89},"Huiling Xu","https:\u002F\u002Forcid.org\u002F0000-0001-8195-8648",{"name":91,"orcid":92},"Wangze Ni","https:\u002F\u002Forcid.org\u002F0000-0003-1438-1345",{"name":94,"orcid":95},"Xiaoliang Li","https:\u002F\u002Forcid.org\u002F0000-0002-2410-7545","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fveterinary-science\u002Farticles\u002F10.3389\u002Ffvets.2026.1968632\u002Fpdf",{"tldr":98,"method":99,"finding":100,"direction":101,"opportunity":102},"综述闭环AI智能体用于畜禽疫病预测与决策支持，提出感知-决策-反馈架构。","综述多尺度传感、多模态输入、时序预测与强化学习集成。","现有证据仅支持框架组件，闭环智能体尚未在真实农场部署。","农业人工智能与决策模型","可研究闭环智能体在真实农场的验证、标准化与人机协同治理。","智慧农业 \u002F 农业物联网","2026-09-25T23:30:12.980548Z",{"id":106,"title":107,"url":108,"summary":109,"summary_zh":110,"content":8,"source_name":111,"source_url":108,"published_at":112,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":113,"score_detail":114,"sources":117,"tags":119,"search_phrases":122,"slug":125,"view_count":34,"doi":126,"paper":127,"created_at":157},3347,"Integrating Material Flow Cost Accounting and IoT-Based Monitoring for Eco-Efficient Goat Farm Management","https:\u002F\u002Fdoi.org\u002F10.35145\u002F6e5wnv18","Goat farming plays an important role in supporting rural livelihoods, food production, and agricultural sustainability. However, conventional goat farm management often separates environmental monitoring, financial accounting, and livestock management, limiting the ability to identify resource inefficiencies and associated environmental impacts. This study aims to develop and implement GEMBALA (Green Eco-smart Management-Based Automation for Livestock and Accounting), an integrated digital platform that combines Internet of Things (IoT)-based environmental monitoring, Material Flow Cost Accounting (MFCA), emission analysis, artificial intelligence-based livestock management, and analytical reporting. The research employed a research and development approach in collaboration with CV Cahaya Firdaus (Fathur Farm). An IoT sensor prototype was developed, installed, and tested in a real goat farming environment to monitor temperature, humidity, Heat Index (THI), ammonia gas, and dust density. The platform also incorporates MFCA, emission, AI Estrus, AI Health, and analytical reporting modules. The results demonstrate progress toward integrating environmental, economic, and livestock management information within a unified digital platform. However, further validation is required to improve sensor data transmission, synchronization, emission calculations, MFCA data consistency, and AI performance evaluation. The study provides a foundation for eco-economic decision support, sustainable livestock management, and future commercialization of digital livestock technologies.","山羊养殖在支撑农村生计、粮食生产和农业可持续性方面发挥着重要作用。然而，传统的山羊养殖场管理往往将环境监测、财务核算和畜牧管理相互分离，限制了识别资源低效利用及相关环境影响的能力。本研究旨在开发并实施GEMBALA（基于绿色生态智能管理的畜牧与会计自动化平台），这是一个集成了基于物联网（IoT）的环境监测、物料流成本会计（MFCA）、排放分析、基于人工智能的畜牧管理以及分析报告的综合数字平台。研究采用研发方法，与CV Cahaya Firdaus（Fathur Farm）合作开展。研究开发了物联网传感器原型，并在真实山羊养殖环境中进行安装和测试，用于监测温度、湿度、热指数（THI）、氨气和粉尘密度。该平台还整合了MFCA、排放、AI发情检测、AI健康和分析报告模块。结果表明，在将环境、经济和畜牧管理信息整合到统一数字平台方面取得了进展。然而，仍需进一步验证，以改进传感器数据传输、同步、排放计算、MFCA数据一致性以及AI性能评估。本研究为生态经济决策支持、可持续畜牧管理以及数字畜牧技术的未来商业化提供了基础。","Journal of Applied Business and Technology","2026-09-24T00:00:00Z",62,{"impact":19,"substance":16,"depth":115,"authority":12,"freshness":12,"relevant":20,"comment":116},16,"论文提出融合物联网监测与物料流成本核算的山羊养殖数字平台，方法有创新但尚处原型验证阶段，产业影响有限。",[118],{"name":111,"url":108},[120,25,26,121,28],"数字农业","农业物联网",[123,124],"GEMBALA 山羊养殖 物联网","MFCA 畜牧 环境监测","GEMBALA山羊养殖物联网-3347","10.35145\u002F6e5wnv18",{"doi":126,"openalex_id":128,"authors":129,"venue":111,"cited_by_count":34,"oa_url":108,"card":152,"direction":103,"ingested_from":50},"W7214075234",[130,132,134,136,138,140,143,146,148,150],{"name":131,"orcid":8},"Nicholas Renaldo",{"name":133,"orcid":8},"Sulaiman Musa",{"name":135,"orcid":8},"Jaswar Koto",{"name":137,"orcid":8},"Kristy Veronica",{"name":139,"orcid":8},"Umar Faruq",{"name":141,"orcid":142},"Yulvia Nora Marlim","https:\u002F\u002Forcid.org\u002F0009-0007-8624-5023",{"name":144,"orcid":145},"Rangga Rahmadian Yuliendi","https:\u002F\u002Forcid.org\u002F0000-0003-2288-3580",{"name":147,"orcid":8},"Wilda Susanti",{"name":149,"orcid":8},"Achmad Tavip Junaedi",{"name":151,"orcid":8},"Nabila Wahid",{"tldr":153,"method":154,"finding":155,"direction":103,"opportunity":156},"开发集成物联网监测与物料流成本核算的山羊养殖数字平台GEMBALA。","研发方法，在真实羊场部署物联网传感器并集成MFCA、排放分析与AI模块。","平台初步实现环境、经济与养殖信息整合，但传感器传输与数据一致性仍需验证。","可延伸研究物联网数据与MFCA实时耦合的算法优化及AI模块的长期性能验证。","2026-09-24T23:30:09.863218Z",{"id":159,"title":160,"url":161,"summary":162,"summary_zh":163,"content":8,"source_name":164,"source_url":161,"published_at":165,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":166,"score_detail":167,"sources":170,"tags":172,"search_phrases":175,"slug":178,"view_count":34,"doi":179,"paper":180,"created_at":198},3065,"A MULTIMODAL FRAMEWORK FOR LIVESTOCK DETECTION AND EPIZOOTIC MONITORING","https:\u002F\u002Fdoi.org\u002F10.67048\u002Fqxju2026ai128iss7m1055","This study proposes an automated computer vision framework to detect and monitor mixed herds of cattle and sheep. By integrating unmanned aerial vehicle (UAV) imagery with ground-level perspectives, a multimodal dataset was curated. Using this data, a lightweight, single-stage object detector (YOLOv8n), tailored for resource-constrained edge devices, was trained and optimised. The integration of computer vision and UAV technology offers agricultural agencies a scalable tool for proactive epizootic surveillance.","本研究提出了一种自动化计算机视觉框架，用于检测和监测牛与羊的混合畜群。通过将无人机（UAV）影像与地面视角相结合，构建了一个多模态数据集。利用该数据，训练并优化了一个轻量级单阶段目标检测器（YOLOv8n），专为资源受限的边缘设备而设计。计算机视觉与无人机技术的融合为农业机构提供了一种可扩展的工具，用于主动进行动物流行病监测。","Agro ilm","2026-09-18T00:00:00Z",71,{"impact":16,"substance":17,"depth":168,"authority":19,"freshness":19,"relevant":20,"comment":169},17,"多模态无人机视觉框架用于牛羊混群检测与疫病预警，方法具体、面向边缘设备，对智慧畜牧有实用参考价值。",[171],{"name":164,"url":161},[25,26,173,28,174],"无人机遥感","疫病监测",[176,177],"YOLOv8n 牲畜检测","无人机 畜牧 疫病监测","YOLOv8n牲畜检测-3065","10.67048\u002Fqxju2026ai128iss7m1055",{"doi":179,"openalex_id":181,"authors":182,"venue":164,"cited_by_count":34,"oa_url":192,"card":193,"direction":103,"ingested_from":50},"W7213668410",[183,186,189],{"name":184,"orcid":185},"R. N. Khadzhaev","https:\u002F\u002Forcid.org\u002F0009-0001-8124-2399",{"name":187,"orcid":188},"Sa’dulla Avezbayev","https:\u002F\u002Forcid.org\u002F0009-0008-8896-7078",{"name":190,"orcid":191},"Sayfuddin Sharipov","https:\u002F\u002Forcid.org\u002F0000-0002-4232-1369","https:\u002F\u002Fqxjurnal.uz\u002Findex.php\u002Fai\u002Farticle\u002Fdownload\u002F1055\u002F936",{"tldr":194,"method":195,"finding":196,"direction":103,"opportunity":197},"提出多模态计算机视觉框架，用无人机和地面图像检测牛羊并监测疫情。","融合无人机与地面视角构建多模态数据集，训练轻量级YOLOv8n边缘检测器。","轻量级模型可在资源受限设备上实现混合畜群检测，支持主动疫情监测。","可探索多物种、复杂地形下的实时边缘检测与疫情早期预警模型优化。","2026-09-21T23:30:13.271856Z",{"id":65,"title":200,"url":201,"summary":202,"summary_zh":8,"content":203,"source_name":204,"source_url":8,"published_at":205,"category":206,"cover_url":8,"hotness":12,"is_selected":13,"score":207,"score_detail":208,"sources":213,"tags":215,"search_phrases":218,"slug":221,"view_count":222,"doi":8,"paper":8,"created_at":223},"经济日报评论员文章：合力抢占农业\"智\"高点","http:\u002F\u002Ftuopin.ce.cn\u002Fnews\u002F202607\u002Ft20260724_3106294.shtml","经济日报评论指出，《加快农业农村现代化\"十五五\"规划》对\"人工智能+\"农业行动作出专项部署，标志着智慧农业进入加速推进新阶段。农业农村部数据显示：病虫害多模态识别算法等104项关键核心技术、大田作物表型机器人等62项整机智能装备实现突破；270万台（套）农用北斗终端、30余万架植保无人机投入使用；累计建成116个国家智慧农业创新应用项目、推介发布16个\"人工智能+\"行动典型场景。评论呼吁从强化产业顶层设计、激发企业创新活力、加强市场品牌建设等方面合力抢占\"智\"高地，并提出建立智慧农产品区块链溯源认证体系、推广可视化认养和智慧农文旅等具体路径。","![Image 1](http:\u002F\u002Fm.ce.cn\u002Fimages\u002Fcecn-icon.jpg)\n\n[![Image 2](http:\u002F\u002Fwww.ce.cn\u002Fmaterial\u002Fimages\u002Fmdy2025_waplogo.jpg)](http:\u002F\u002Fm.ce.cn\u002F \"中国经济网\")\n\n[要闻](http:\u002F\u002Fm.ce.cn\u002Fyw\u002F)[产业](http:\u002F\u002Fm.ce.cn\u002Fcy\u002F)[金融](http:\u002F\u002Fm.ce.cn\u002Fcj\u002F)[证券](http:\u002F\u002Fm.ce.cn\u002Fgp\u002F)[评论](http:\u002F\u002Fm.ce.cn\u002Fxpl\u002F)[国际](http:\u002F\u002Fm.ce.cn\u002Fgj\u002F)\n\n[地方](http:\u002F\u002Fm.ce.cn\u002Fdistrict\u002F)[三农](http:\u002F\u002Fm.ce.cn\u002Fvillage\u002F)[生态](http:\u002F\u002Fm.ce.cn\u002Fecology\u002F)[食品](http:\u002F\u002Fm.ce.cn\u002Fxsp\u002F)[汽车](http:\u002F\u002Fm.ce.cn\u002Fqc\u002F)[生活](http:\u002F\u002Fm.ce.cn\u002Flv\u002F)\n\n[首页](http:\u002F\u002Ftuopin.ce.cn\u002F \"首页\")>[滚动资讯](http:\u002F\u002Ftuopin.ce.cn\u002Fnews\u002F \"滚动资讯\")\n## 合力抢占农业“智”高点\n\n2026-07-24 14:19 来源：中国经济网-《经济日报》苏 昕\n\n国务院印发的《加快农业农村现代化“十五五”规划》明确提出“推进人工智能运用和智慧农业发展”，并对“人工智能+”农业行动开展了专项部署。这标志着我国智慧农业建设进入加速推进的新阶段。要抓好规划任务落实，必须激活智慧农业新引擎。\n\n近年来，数智技术与农业产业不断融合，我国智慧农业在核心技术创新、智能装备普及以及应用场景拓展等多个方面，都取得了显著发展成效。根据农业农村部的统计数据，病虫害多模态识别算法等104项关键核心技术、大田作物表型机器人等62项整机智能装备相继实现突破。目前270万台（套）农用北斗终端设备投入田间生产，30余万架植保无人机服务耕地面积达4.6亿亩。我国分区域、分品种建设完成116个国家智慧农业创新应用项目，并累计推介发布16个“人工智能+”行动典型场景，覆盖育种、种养、加工和销售等各环节，智慧农业应用场景全链拓宽，发展模式在全国遍地开花。\n\n从北国黑土地到江南水乡，从华北平原到西南山地，智慧农业在各地展现了颇具特色的生动实践。黑龙江北大荒八五二分公司引进全天时智能激光除草机器人，作业效率可达人工除草的4倍至8倍，有效破解了人工除草用工难等痛点。在江苏，作为全国数字乡村试点区的南京市高淳区，率先发布省级《河蟹产业数字化建设指南》，当地虾蟹产业数字化水平已达76.8%。有着“蔬菜之都”之称的山东寿光，新建大棚的物联网应用占比超85%，搭载环境传感器与AI机器狗，农业生产效率提升2倍。贵州省山地智能农机全省重点实验室的“刺梨收获机”“智能灌溉系统”等成果，在省内实现成熟应用，刺梨收获机采净率达94.42%，智能灌溉系统推广超50万亩。\n\n必须认识到，智慧农业要实现长效发展，还面临不少现实瓶颈。比如专用芯片、作物生长模型的核心算法等技术，目前仍较多依赖进口，关键核心技术的自主可控能力还有待提升。涉农数据方面，标准、接口不一，流通共享机制不健全，数据要素的价值还未充分得到释放。智慧农业专业人才缺口明显，智能装备成本又较高，中小农户面临“用不起、不会用”困境，规模化发展受到阻碍。破除这些瓶颈、激活智慧农业新引擎，需要多管齐下，合力抢占农业“智”高点。\n\n强化产业顶层设计，创造良好产业环境。实施智慧农业核心技术攻坚行动，立足农业生产重大实际需求设立专项基金，突破“卡脖子”关键技术。加快统一农业数据采集、接口、交换标准，推动各地农业云平台互联互通。探索农业数据产权登记与交易试点，健全数据确权与流通制度。完善财税金融支持政策，对从事智慧农业的经营主体给予适当税收优惠和信贷倾斜。创新“政府购买服务+合作社托管”模式和农业保险政策。扩大智能装备购置补贴范围，对服务小农户的专业化组织给予作业补贴。实施智慧农业人才培育计划，支持高校设立交叉学科，建立梯度新型职业农民培养体系，定向培养复合型“新农人”。\n\n激发企业创新活力，发展可持续商业模式。培育农业科技领军企业，打造产学研科技创新联合体，推动科研成果实现工程化、产品化落地。鼓励传统农机企业与人工智能企业跨界合作，针对小地块、特色作物等不同场景，开发低成本、易操作的智能终端产品。引导科技企业拓展“AI+农业服务”新业态，探索从产品销售向服务订阅转型。支持企业建立“共享农机”平台，以分时租赁、以租代购的方式降低农户一次性投入，对租赁覆盖农户数量多的企业给予税收优惠等政策激励。\n\n加强市场品牌建设，畅通良性供需循环。建立智慧农产品区块链溯源认证体系，对全程智能管控、数据留痕的产品授权“智慧农业”专属标识，在线上线下渠道设立专柜专区。消费者扫码可查验生长环境、农事记录等“数字身份证”，拓展智慧农产品的溢价空间，提升消费者支付意愿，推动形成“优质优价—农户增收—技术再投入”的良性循环。创新消费业态，推广可视化认养、智慧农文旅等场景。\n\n(责任编辑：高原)\n\n查看余下全文\n\nX\n\n![Image 3](http:\u002F\u002Ftuopin.ce.cn\u002Fnews\u002F202607\u002Ft20260724_3106294.shtml)\n\n推荐阅读\n\n[![Image 4](http:\u002F\u002Fi.ce.cn\u002Fce\u002F360tj\u002Ftjyd\u002F202609\u002FW020260903549473239515_ORIGIN.png)](http:\u002F\u002Fyun.ce.cn\u002Fcen\u002Fgd\u002F202609\u002Ft20260903_3191644.shtml)\n[中经声量｜黄灯：父母理直气壮“鸡娃” 风险真的很大](http:\u002F\u002Fyun.ce.cn\u002Fcen\u002Fgd\u002F202609\u002Ft20260903_3191644.shtml)\n\n 09-03 \n\n[![Image 5](http:\u002F\u002Fi.ce.cn\u002Fce\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F202609\u002FW020260903545812823349_ORIGIN.png)](http:\u002F\u002Fwww.ce.cn\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F202609\u002Ft20260903_3191610.shtml)\n[别再拿中国贸易顺差说事儿](http:\u002F\u002Fwww.ce.cn\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F202609\u002Ft20260903_3191610.shtml)\n\n 09-03 \n\n[![Image 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10](http:\u002F\u002Fi.ce.cn\u002Fviews\u002Fview\u002Fent\u002F202609\u002FW020260902598698254024.jpg)](http:\u002F\u002Fviews.ce.cn\u002Fview\u002Fent\u002F202609\u002Ft20260902_3188723.shtml)\n[理响中国·讲好“中国式商量”故事丨福建：深耕“为闽协商、为民服务”特色履职品牌](http:\u002F\u002Fviews.ce.cn\u002Fview\u002Fent\u002F202609\u002Ft20260902_3188723.shtml)\n\n 09-02 \n\n[![Image 11](http:\u002F\u002Fi.ce.cn\u002Fce\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F202609\u002FW020260902595602477598_ORIGIN.jpg)](http:\u002F\u002Fwww.ce.cn\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F202609\u002Ft20260902_3188706.shtml)\n[【图解】前7月我国软件业务收入近9万亿元 同比增长9.2%](http:\u002F\u002Fwww.ce.cn\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F202609\u002Ft20260902_3188706.shtml)\n\n 09-02 \n\n中国经济网版权所有\n\n[中国经济网新媒体矩阵](http:\u002F\u002Fwww.ce.cn\u002Fabout\u002Fjjw\u002Fone\u002F202109\u002F22\u002Ft20210922_36934521.shtml)\n\n网络传播视听节目许可证(0107190) (京ICP备18036557号)\n\n[![Image 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[大](javascript:void();)[中](javascript:void();)[小](javascript:void();)]\n\n国务院印发的《加快农业农村现代化“十五五”规划》明确提出“推进人工智能运用和智慧农业发展”，并对“人工智能+”农业行动开展了专项部署。这标志着我国智慧农业建设进入加速推进的新阶段。要抓好规划任务落实，必须激活智慧农业新引擎。\n\n近年来，数智技术与农业产业不断融合，我国智慧农业在核心技术创新、智能装备普及以及应用场景拓展等多个方面，都取得了显著发展成效。根据农业农村部的统计数据，病虫害多模态识别算法等104项关键核心技术、大田作物表型机器人等62项整机智能装备相继实现突破。目前270万台（套）农用北斗终端设备投入田间生产，30余万架植保无人机服务耕地面积达4.6亿亩。我国分区域、分品种建设完成116个国家智慧农业创新应用项目，并累计推介发布16个“人工智能+”行动典型场景，覆盖育种、种养、加工和销售等各环节，智慧农业应用场景全链拓宽，发展模式在全国遍地开花。\n\n从北国黑土地到江南水乡，从华北平原到西南山地，智慧农业在各地展现了颇具特色的生动实践。黑龙江北大荒八五二分公司引进全天时智能激光除草机器人，作业效率可达人工除草的4倍至8倍，有效破解了人工除草用工难等痛点。在江苏，作为全国数字乡村试点区的南京市高淳区，率先发布省级《河蟹产业数字化建设指南》，当地虾蟹产业数字化水平已达76.8%。有着“蔬菜之都”之称的山东寿光，新建大棚的物联网应用占比超85%，搭载环境传感器与AI机器狗，农业生产效率提升2倍。贵州省山地智能农机全省重点实验室的“刺梨收获机”“智能灌溉系统”等成果，在省内实现成熟应用，刺梨收获机采净率达94.42%，智能灌溉系统推广超50万亩。\n\n必须认识到，智慧农业要实现长效发展，还面临不少现实瓶颈。比如专用芯片、作物生长模型的核心算法等技术，目前仍较多依赖进口，关键核心技术的自主可控能力还有待提升。涉农数据方面，标准、接口不一，流通共享机制不健全，数据要素的价值还未充分得到释放。智慧农业专业人才缺口明显，智能装备成本又较高，中小农户面临“用不起、不会用”困境，规模化发展受到阻碍。破除这些瓶颈、激活智慧农业新引擎，需要多管齐下，合力抢占农业“智”高点。\n\n强化产业顶层设计，创造良好产业环境。实施智慧农业核心技术攻坚行动，立足农业生产重大实际需求设立专项基金，突破“卡脖子”关键技术。加快统一农业数据采集、接口、交换标准，推动各地农业云平台互联互通。探索农业数据产权登记与交易试点，健全数据确权与流通制度。完善财税金融支持政策，对从事智慧农业的经营主体给予适当税收优惠和信贷倾斜。创新“政府购买服务+合作社托管”模式和农业保险政策。扩大智能装备购置补贴范围，对服务小农户的专业化组织给予作业补贴。实施智慧农业人才培育计划，支持高校设立交叉学科，建立梯度新型职业农民培养体系，定向培养复合型“新农人”。\n\n激发企业创新活力，发展可持续商业模式。培育农业科技领军企业，打造产学研科技创新联合体，推动科研成果实现工程化、产品化落地。鼓励传统农机企业与人工智能企业跨界合作，针对小地块、特色作物等不同场景，开发低成本、易操作的智能终端产品。引导科技企业拓展“AI+农业服务”新业态，探索从产品销售向服务订阅转型。支持企业建立“共享农机”平台，以分时租赁、以租代购的方式降低农户一次性投入，对租赁覆盖农户数量多的企业给予税收优惠等政策激励。\n\n加强市场品牌建设，畅通良性供需循环。建立智慧农产品区块链溯源认证体系，对全程智能管控、数据留痕的产品授权“智慧农业”专属标识，在线上线下渠道设立专柜专区。消费者扫码可查验生长环境、农事记录等“数字身份证”，拓展智慧农产品的溢价空间，提升消费者支付意愿，推动形成“优质优价—农户增收—技术再投入”的良性循环。创新消费业态，推广可视化认养、智慧农文旅等场景。\n\n(责任编辑：高原)\n\n推荐阅读\n\n[![Image 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19](http:\u002F\u002Fi.ce.cn\u002Fviews\u002Fview\u002Fent\u002F202609\u002FW020260902598698254024.jpg)](http:\u002F\u002Fviews.ce.cn\u002Fview\u002Fent\u002F202609\u002Ft20260902_3188723.shtml)\n[理响中国·讲好“中国式商量”故事丨福建：深耕“为闽协商、为民服务”特色履职品牌](http:\u002F\u002Fviews.ce.cn\u002Fview\u002Fent\u002F202609\u002Ft20260902_3188723.shtml)\n\n[![Image 20](http:\u002F\u002Fi.ce.cn\u002Fce\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F202609\u002FW020260902595602477598_ORIGIN.jpg)](http:\u002F\u002Fwww.ce.cn\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F202609\u002Ft20260902_3188706.shtml)\n[【图解】前7月我国软件业务收入近9万亿元 同比增长9.2%](http:\u002F\u002Fwww.ce.cn\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F202609\u002Ft20260902_3188706.shtml)\n\n中国经济网版权及免责声明\n\n*   1、凡本网注明 '来源：中国经济网' 或 '来源：经济日报-中国经济网' 的所有作品，版权均属于中国经济网（本网另有声明的除外）；未经本网授权，任何单位及个人不得转载、摘编或以其它方式使用上述作品；已经与本网签署相关授权使用协议的单位及个人，应注意该等作品中是否有相应的授权使用限制声明，不得违反该等限制声明，且在授权范围内使用时应注明 '来源：中国经济网' 或 '来源：经济日报-中国经济网'。违反前述声明者，本网将追究其相关法律责任。\n*   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[互联网新闻信息服务许可证(10120170008)](http:\u002F\u002Fwww.ce.cn\u002Fabout\u002Fjjw\u002Fone\u002F202310\u002F20\u002Ft20231020_38757422.shtml)[网络传播视听节目许可证(0107190)](http:\u002F\u002Fvr.ce.cn\u002Fvr2017\u002Fnews\u002F202209\u002F02\u002Ft20220902_38080192.shtml)[京ICP备18036557号](http:\u002F\u002Fbeian.miit.gov.cn\u002F)[![Image 23](http:\u002F\u002Fwww.ce.cn\u002F2021sy\u002Fyqlj\u002Fimages\u002FP020210820607213251435.jpg)](http:\u002F\u002Fwww.12377.cn\u002F)[中国互联网举报中心](http:\u002F\u002Fwww.12377.cn\u002F) 举报电话:18510915000 [处置流程](http:\u002F\u002Fwww.ce.cn\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F201801\u002F18\u002Ft20180118_27795972.shtml)\n\n[![Image 24](http:\u002F\u002Fwww.ce.cn\u002Fimg4\u002Fpolice2021.png) 京公网安备 11010202009785号](http:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=11010202009785)\n\n#### [微信扫一扫：分享 ![Image 25](blob:http:\u002F\u002Flocalhost\u002Ff32cb2dde66a51d0284f2896f1ff5f02) 微信里点“发现”，扫一下 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十五五规划","农业人工智能十五五规划数据要素智慧农业-14",9,"2026-07-27T05:05:33.381906Z",{"id":225,"title":226,"url":227,"summary":228,"summary_zh":8,"content":8,"source_name":229,"source_url":8,"published_at":165,"category":11,"cover_url":8,"hotness":12,"is_selected":230,"score":231,"score_detail":232,"sources":234,"tags":236,"search_phrases":240,"slug":243,"view_count":34,"doi":8,"paper":244,"created_at":252},3653,"面向智慧农业的自主农业机械——观测、异质性与智能基础设施的集成框架","https:\u002F\u002Fwww.jstage.jst.go.jp\u002Fbrowse\u002Frdj\u002F5\u002F0\u002F_contents\u002F-char\u002Fja","由Hongjin Li、Chunjiang Gao等在Resources Data Journal 2026年第5卷发表的综述文章。面向Agriculture 4.0过渡，针对自主农业机械这一核心议题提出'观测-异质性-基础设施'三位一体的集成框架。系统综述了自主农业机械的技术基础、应用领域、性能优势与采纳约束，并围绕多模态感知、AI驱动决策、自主导航与控制、精准执行、多机协同的耦合展开分析，特别关注环境不确定性、实时决策、互操作性与系统级可扩展性的挑战。研究识别出从孤立任务自动化向数据驱动、适应性、网络化农业自主性的渐进转型，并强调单一组件的改进若无配套计算、通信、机械与制度基础设施支撑，未必带来系统级性能提升。","Resources Data Journal 2026 vol.5 p.576-596 (Hongjin Li, Chunjiang Gao)",true,74,{"impact":16,"substance":17,"depth":168,"authority":18,"freshness":53,"relevant":20,"comment":233},"核心期刊综述提出观测-异质性-基础设施三位一体框架，对自主农机系统级落地有参考价值，但属学术综述、时效一般，适合主题聚合而非每日精选头条。",[235],{"name":229,"url":227},[25,26,237,238,239],"多模态感知","自主农机","Agriculture 4.0",[241,242],"自主农业机械 集成框架","Resources Data Journal 智慧农业","自主农业机械集成框架-3653",{"doi":8,"openalex_id":8,"authors":245,"venue":8,"cited_by_count":34,"oa_url":8,"card":246,"direction":103,"ingested_from":251},[],{"tldr":247,"method":248,"finding":249,"direction":103,"opportunity":250},"综述自主农业机械，提出观测-异质性-基础设施三位一体集成框架。","文献综述，围绕多模态感知、AI决策、自主导航与多机协同分析。","单组件改进若无计算、通信与制度基础设施配套，难带来系统级性能提升。","可研究异构农机互操作协议与边缘计算协同，量化基础设施配套对系统级性能的增益。","agent","2026-09-28T00:03:02.555619Z",{"id":254,"title":255,"url":256,"summary":257,"summary_zh":8,"content":8,"source_name":258,"source_url":8,"published_at":259,"category":11,"cover_url":8,"hotness":12,"is_selected":230,"score":260,"score_detail":261,"sources":263,"tags":265,"search_phrases":269,"slug":272,"view_count":34,"doi":8,"paper":273,"created_at":281},3652,"[预印本] 智慧农业平台AI模块的边际绿色贡献模拟——两项蒙特卡洛实验证据","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2609.06740v1","在前期平台级评估基础上，本研究将组件显性化并设计两项受控模拟实验。实验1沿'AI能力→农户行为→农化投入减少'链路建模，将农药\u002F化肥减量建模为可避免盲施份额、处方有效性、决策触达覆盖率与采纳率的乘积；对比经验推广模式与AI模式：AI模式下达成农药减量20%的概率从推广模式下的接近0上升至基线20.7%，并在诊断精度0.95、采纳率0.85下最高达49%；化肥减量15%的概率从接近0升至52.0%。实验2对比现行做法（P0）、IoT工程改造（P1）及P1+AI灌溉调度（P2）：灌溉水节水由7.8%（P0）升至11.0%（P1）与16.0%（P2），AI在工程之外再增加5.0个百分点；稻田CH4在AI调度下减幅达30.5%，碳强度降低27.9%。","arXiv 2609.06740v1 (2026-09)","2026-09-15T00:00:00Z",75,{"impact":16,"substance":210,"depth":16,"authority":222,"freshness":19,"relevant":20,"comment":262},"预印本以两项蒙特卡洛模拟量化AI模块在农化减量与稻田减排上的边际绿色贡献，方法新颖、数据具体，但尚未经同行评审，属细分领域前沿进展。",[264],{"name":258,"url":256},[25,26,266,267,268],"精准灌溉","稻田甲烷","化肥农药减量",[270,271],"AI灌溉调度 稻田甲烷 节水","智慧农业 农药化肥减量 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