[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3439":3,"related-3439":45},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"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":8,"paper":35,"created_at":44},3439,"《Agentic Artificial Intelligence in Agriculture: A Systematic Mapping Review of Reported Architectures, Applications, Challenges, and Future Directions》","https:\u002F\u002Fwww.mdpi.com\u002F2227-7080\u002F14\u002F9\u002F591","作者按PRISMA 2020方案从4111项记录筛选至181项研究，对2020—2026年农业代理式人工智能文献做系统映射综述：领域跨度30多年但LLM子集非常年轻（2024年才出现、46\u002F47项发表于2025—2026年）；82%研究报告合作能力，计划和推理分别仅31%、记忆6%、反思4%；只有33项研究报告现场或实际部署，68项仍停留在概念性阶段，仅11项报告了一个季度以上的评估。",null,"《Technologies》2026, 14(9), 591 \u002F MDPI","2026-09-22T00:00:00Z","论文",10,false,80,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,23,13,8,1,"基于PRISMA的农业代理式AI系统映射综述，量化揭示LLM应用年轻化与落地不足，信息增量与专业深度突出，值得进入每日精选。",[23],{"name":9,"url":6},[25,26,27,28,29],"数字农业","智慧农业","农业人工智能","农业大模型","智能体",[31,32],"农业代理式人工智能 系统映射综述","农业人工智能 农业大模型 数字农业 智慧农业","农业代理式人工智能系统映射综述-3439",0,{"doi":8,"openalex_id":8,"authors":36,"venue":8,"cited_by_count":34,"oa_url":8,"card":37,"direction":41,"ingested_from":43},[],{"tldr":38,"method":39,"finding":40,"direction":41,"opportunity":42},"系统映射181项研究，梳理农业代理式AI的架构、应用、挑战与未来方向。","按PRISMA 2020筛选4111项记录至181项，做系统映射综述。","LLM代理2024年才出现，多具合作能力但规划、记忆、反思薄弱，实际部署少。","农业人工智能与决策模型","农业LLM代理的长期田间部署、记忆与反思机制及跨季度评估仍是明显空白。","agent","2026-09-25T00:09:33.995675Z",{"total":46,"page":20,"page_size":46,"items":47},6,[48,89,142,166,190,211],{"id":49,"title":50,"url":51,"summary":52,"summary_zh":53,"content":8,"source_name":54,"source_url":51,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":55,"score_detail":56,"sources":61,"tags":63,"search_phrases":66,"slug":69,"view_count":34,"doi":70,"paper":71,"created_at":88},3367,"Digital Technology Adoption Conditioning Analysis Model in Agriculture","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.1880.v1","Technological advancements have been responsible for a significant part of the growth in agricultural productivity in recent years. Digital technologies have a high potential to enable the development of the agricultural sector, reshape value chains, and significantly contribute to more productive, resilient, and transparent food systems; however, their adoption in Brazil remains uneven due to regional disparities and structural bottlenecks. The research investigated this problem to build and validate the Digital Technology Adoption Conditioning Analysis Model (MAC-AgriTech), through a case study with Brazilian agricultural data, encompassing the identification of conditioning factors, their territorial evaluation, and the proposition of actions, while providing structured data collection and analysis instruments. The spatial analysis revealed deep territorial asymmetries, concentrating resources and infrastructure in the South and Southeast regions. Econometric modeling demonstrated that digital adoption is primarily driven by the producer’s digital familiarity, connectivity quality, and property scale, with 77% of producers identifying acquisition and maintenance costs as the primary barrier. The transition to digital agriculture in Brazil requires targeted, multidimensional public policies—such as expanded rural connectivity, technical training, and subsidized credit—to overcome regional gaps, and to increase agricultural competitiveness, efficiency, and sustainability.","近年来，技术进步对农业生产力增长贡献显著。数字技术具有巨大潜力，能够推动农业部门发展、重塑价值链，并为构建更高产、更具韧性且更透明的粮食体系作出重要贡献；然而，由于区域差异和结构性瓶颈，其在巴西的采用仍不均衡。本研究针对这一问题，通过一项基于巴西农业数据的案例研究，构建并验证了数字技术采用条件分析模型（MAC-AgriTech），涵盖条件因素的识别、其区域性评估以及行动建议的提出，同时提供了结构化的数据收集与分析工具。空间分析揭示了深刻的区域不对称性，资源和基础设施集中在南部和东南部地区。计量经济建模表明，数字采用主要受生产者数字熟悉度、连接质量和财产规模的驱动，其中77%的生产者将购置和维护成本视为主要障碍。巴西向数字农业的转型需要有针对性的、多维度的公共政策——如扩大农村连接、技术培训和补贴信贷——以克服区域差距，并提高农业竞争力、效率和可持续性。","Preprints.org",67,{"impact":57,"substance":58,"depth":59,"authority":46,"freshness":19,"relevant":20,"comment":60},16,20,17,"基于巴西农业数据的数字技术采纳条件分析模型研究，方法系统、结论有实证支撑，但属预印本且聚焦巴西，对国内参考价值有限。",[62],{"name":54,"url":51},[25,26,27,64,65],"巴西农业","农村数字化",[67,68],"巴西 数字农业 技术采纳","MAC-AgriTech 模型","巴西数字农业技术采纳-3367","10.20944\u002Fpreprints202609.1880.v1",{"doi":70,"openalex_id":72,"authors":73,"venue":54,"cited_by_count":34,"oa_url":51,"card":81,"direction":85,"ingested_from":87},"W7214109425",[74,77,79],{"name":75,"orcid":76},"Isabela Santos","https:\u002F\u002Forcid.org\u002F0009-0002-3659-2020",{"name":78,"orcid":8},"Eduardo Dias",{"name":80,"orcid":8},"Lidia Scoton",{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"构建并验证MAC-AgriTech模型，分析巴西农业数字技术采纳的条件因素与区域差异。","巴西农业数据案例研究，空间分析与计量经济建模。","采纳主要由数字熟悉度、连接质量和农场规模驱动，77%生产者视成本为首要障碍。","数字乡村与农业信息化","可延伸至中国等发展中国家，探究数字素养、基础设施与政策组合对技术采纳的因果效应。","openalex","2026-09-24T23:30:27.046035Z",{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":8,"source_name":95,"source_url":92,"published_at":96,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":97,"score_detail":98,"sources":100,"tags":102,"search_phrases":105,"slug":108,"view_count":34,"doi":109,"paper":110,"created_at":141},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":57,"authority":12,"freshness":12,"relevant":20,"comment":99},"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。",[101],{"name":95,"url":92},[25,26,27,103,104],"农业物联网","畜牧养殖",[106,107],"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":109,"openalex_id":111,"authors":112,"venue":95,"cited_by_count":34,"oa_url":92,"card":135,"direction":139,"ingested_from":87},"W7214075234",[113,115,117,119,121,123,126,129,131,133],{"name":114,"orcid":8},"Nicholas Renaldo",{"name":116,"orcid":8},"Sulaiman Musa",{"name":118,"orcid":8},"Jaswar Koto",{"name":120,"orcid":8},"Kristy Veronica",{"name":122,"orcid":8},"Umar Faruq",{"name":124,"orcid":125},"Yulvia Nora Marlim","https:\u002F\u002Forcid.org\u002F0009-0007-8624-5023",{"name":127,"orcid":128},"Rangga Rahmadian Yuliendi","https:\u002F\u002Forcid.org\u002F0000-0003-2288-3580",{"name":130,"orcid":8},"Wilda Susanti",{"name":132,"orcid":8},"Achmad Tavip Junaedi",{"name":134,"orcid":8},"Nabila Wahid",{"tldr":136,"method":137,"finding":138,"direction":139,"opportunity":140},"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。","智慧农业 \u002F 农业物联网","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":143,"title":144,"url":145,"summary":146,"summary_zh":8,"content":147,"source_name":148,"source_url":8,"published_at":10,"category":149,"cover_url":8,"hotness":12,"is_selected":13,"score":150,"score_detail":151,"sources":156,"tags":158,"search_phrases":161,"slug":164,"view_count":34,"doi":8,"paper":8,"created_at":165},3215,"秋分逢丰收节 机器人成主角！浙江田野正被AI\"接管\"——浙江农科院数字农业研究所研发AI眼镜+害虫识别小程序","https:\u002F\u002Fwww.cztv.com\u002FnewsDetail\u002F904714","9-22 新蓝网专题报道：在湖州德清县农博家庭农场，种植大户王菊仙戴上一副AI眼镜，对着诱杀害虫的黄板轻轻一扫，\"镜片上、手机端，种类、数量、位置等数据瞬间显现\"。这套由浙江省农科院数字农业研究所研发的设备，正将虫害防控从\"事后补救\"推向\"提前预警\"。在湖州吴兴丰盛湾水产种业，\"云眸\"沼虾养殖AI系统能在3-5秒内捕捉沼虾触须末端细微影像，自动生成比对图谱，一旦发现活动异常即刻标记预警，自动投料机器人与水下传感器联动精准计算投喂量，饲料利用率提升12%-15%、巡塘人力节省六成、养殖效益整体提高10%以上。在杭州余杭区径山镇，无人驾驶拖拉机搭载北斗导航系统自主作业。在杭州临平区田立方未来农场，450亩无人智慧农场示范区配套200余个田间传感器和4个物联网微基站，可根据土壤饱和度和实时水位自动确定浇灌量，一亩地一季油菜花可节水约1000吨。浙江省农业农村厅数据显示，截至目前浙江已累计建成数字农业工厂729家、未来农场63家。","![Image 2](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FloginBg-OXMHhVd9.png)\n\n验证码登录\n\n获取验证码\n\n 一键登录 \n\n- [x]  \n\n登录代表同意 《用户协议》及 《隐私政策》\n\n扫码登录\n\n![Image 3](https:\u002F\u002Fwww.cztv.com\u002FnewsDetail\u002F904714)\n\n鼠标悬浮刷新二维码\n\n打开Z视介扫码登录\n\n![Image 4](blob:http:\u002F\u002Flocalhost\u002F08f361b6a75bd12fbead3ead9d0ae42d)\n\n[![Image 5](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Flogo-DNzgtwnp.png)](https:\u002F\u002Fwww.cztv.com\u002F)\n\n[首页](https:\u002F\u002Fwww.cztv.com\u002F)\n\n[新闻](https:\u002F\u002Fwww.cztv.com\u002Fheadlines)\n\n[文化](https:\u002F\u002Fwww.cztv.com\u002Fculture)\n\n 电视 \n\n 广播 \n\n[专区](https:\u002F\u002Fwww.cztv.com\u002Fzone)\n\n![Image 6](blob:http:\u002F\u002Flocalhost\u002F0062bfa43494c79fb356b384bfc51bc8)\n\n![Image 7: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Faibtn_icon-Dqb11ejT.png)\n\n![Image 8](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FQRcode1-C1Z3XA1v.png)\n\n![Image 9: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Fdownload_lxw-BTIOt7tT.png)更多精彩 中国蓝新闻\n\n![Image 10](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FQRcode2-CU2IzmPJ.png)\n\n![Image 11: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Fdownload_zsj-C7ZTF9AF.png)更多精彩 下载Z视介\n\n[![Image 12](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Fdownload_more_btn-DzzjA4z1.png)](https:\u002F\u002Fzmtv.cztv.com\u002Fcmsh5-share\u002Fprod\u002FcommonDownload\u002Findex.html)\n\n登录\n\n![Image 13: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FcreateCenter_btn-CP1dJyCT.png)\n\n![Image 14: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Faiblue-BYHqI38h.png)\n\nNaN-NaN-NaN NaN:NaN\n\n编辑：\n\n作者：\n\n[](javascript:; \"分享到微信\") \n\n![Image 15](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Flogo-DNzgtwnp.png)\n\nCopyright 2009-2024 cztv.com [浙ICP备05052141号-1](https:\u002F\u002Fbeian.miit.gov.cn\u002F#\u002FIntegrated\u002Findex) | [浙公网安备 33010602002235号](http:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=33010602002235) | 信息网络传播视听节目许可证号：1107197\n\n[互联网新闻信息服务许可证号：33120170002](https:\u002F\u002Fwww.cztv.com\u002Fxuke) | 网络文化经营许可证号：浙网文(2023)1495-044号\n\n 地址：浙江省杭州市莫干山路111号 邮政编码：310005 邮箱：cztv@zmg.com.cn\n\n 网上有害信息举报专区 12321网络不良与垃圾信息举报受理中心 网络违法犯罪举报网站 网络举报APP下载 \n\n 违法和不良信息公开举报电话：12377、0571-81089789 有害信息举报邮箱：jubao@12377.cn 涉未成年人有害信息举报电话: 0571-81089789 \n\n[点播](https:\u002F\u002Fwww.cztv.com\u002Fvideo)\n\n[直播](https:\u002F\u002Fwww.cztv.com\u002FliveTV)\n\n[点播](https:\u002F\u002Fwww.cztv.com\u002Fradio)\n\n[直播](https:\u002F\u002Fwww.cztv.com\u002FliveRadio)","新蓝网","报道",60,{"impact":57,"substance":152,"depth":153,"authority":154,"freshness":154,"relevant":20,"comment":155},14,12,9,"省级官媒报道浙江农科院数字农业研究所AI眼镜与害虫识别小程序落地，属智慧农业细分进展，时效性强但正文信息量有限。",[157],{"name":148,"url":145},[25,26,27,159,160],"智能农机","害虫识别",[162,163],"浙江农科院 数字农业研究所 AI眼镜","浙江 害虫识别 小程序","浙江农科院数字农业研究所AI眼镜-3215","2026-09-23T00:04:29.218317Z",{"id":167,"title":168,"url":169,"summary":170,"summary_zh":8,"content":171,"source_name":172,"source_url":8,"published_at":173,"category":149,"cover_url":8,"hotness":12,"is_selected":13,"score":174,"score_detail":175,"sources":179,"tags":181,"search_phrases":185,"slug":188,"view_count":34,"doi":8,"paper":8,"created_at":189},3100,"秋实满田 智赋丰年——中国移动(成都)产业研究院\"谷丰登\"农业大模型赋能山西大同玉米单产提升试点亩产 2481 斤较非试点增产 15.5%","https:\u002F\u002Fiot.youth.cn\u002Fyw\u002F202609\u002Ft20260921_16881000.htm","9-21 中国青年网物联网频道报道中国移动(成都)产业研究院秋收赋能：以\"九天大模型\"为底座自主研发\"谷丰登\"农业大模型、构建覆盖土壤分析\u002F品种优选\u002F精准水肥药方案\u002F产量预估全生命周期服务体系；联合中储粮打造行业首个储粮场景大模型\"稷元\"已落地 3 个中央储备粮库，粮食储存损失降至 0.2% 以下、化学药剂年使用量减少 1-2 次；牵头制定《农业大模型高质量数据集》《农业种植大模型应用测评规范》两项团体标准。山西大同玉米单产提升标杆项目依托空天地一体化监测+北斗高精定位+农业大模型实现玉米耕种管收全流程无人化作业，试点田亩产 2481 斤较非试点增产 15.5%，被农业农村部评为\"全国智慧农业建设典型案例\"。","九月的田野，稻菽飘香，又是一年丰收时。\n\n随着“十五五”规划的全面实施，农业农村现代化建设蹄疾步稳。传统“看天吃饭”的农业生产模式正在被改写，数字技术驱动下，丰收迎来科技赋能、全链提质、数智增效的全新发展阶段。\n\n当前正值金秋丰收的关键时期，中国移动（成都）产业研究院（以下简称“成研院”）充分发挥科技引领作用，贯通智慧种养、智能储粮、数字治理、产销对接全业务链路，推动数字技术与农业生产融合走深走实，为国家粮食安全与乡村全面振兴注入强劲数字动能。\n\n**科技强农：打造从田间到粮仓的“智慧大脑”**\n\n成研院聚焦AI育种创新、种植生产管理与粮食仓储等关键环节，以中国移动“九天大模型”为底座，自主研发谷丰登农业大模型，构建起全周期、智能化的农业生产服务体系，打造扎根田野的“身边的农业知识专家”。\n\n“藏粮于技”的关键，在于让技术真正下沉到田间地头。成研院打造国内首个种植综合农业大模型，覆盖土壤分析、品种优选、精准水肥药方案、产量预估等全生命周期服务，实现“信息随手握、种养全程导、在线有人帮”。在山西大同玉米单产提升标杆项目中，成研院构建“良田、良种、良机、良法”融合体系，依托空天地一体化监测、北斗高精定位与农业大模型三大核心能力，实现玉米耕、种、管、收全流程无人化作业。当年秋收，试点田亩产达2481斤，较非试点区域增产15.5％，被农业农村部评为“全国智慧农业建设典型案例”。\n\n丰产更要保收，储粮减损是守住丰收成果、保障粮食安全的关键一环。成研院联合中储粮打造行业首个储粮场景大模型“稷元”，内置二十余万条储粮知识单元及千余本专业文献，结合智能语音助手“粮小助”，实时采集、动态监测各类粮情核心指标，智能预判粮情变化趋势，提前预警仓储虫害风险，自动生成仓储调控最优方案，同时采用一体化集成设计，适配粮库现有系统，实现核心业务在库内闭环处理，严守粮食仓储信息安全底线。目前，“稷元”大模型已在中储粮三个中央储备粮库落地，粮食储存损失降至0.2%以下，化学药剂年使用量减少1—2次，大幅提升粮食仓储安全水平，守住丰收成果、筑牢储粮安全防线。\n\n此外，成研院牵头制定《农业大模型高质量数据集》《农业种植大模型应用测评规范》两项团体标准，填补行业规范空白，为大模型规范化、规模化应用筑牢基础。\n\n![Image 1](https:\u002F\u002Fiot.youth.cn\u002Fyw\u002F202609\u002FW020260921554681104915.png)\n\n![Image 2](https:\u002F\u002Fiot.youth.cn\u002Fyw\u002F202609\u002FW020260921554681314335.png)\n\n**数字惠农：激活基层治理民生温度**\n\n成研院以数字技术创新破解基层治理痛点、赋能民生服务、激活乡村发展动能，推动数字技术真正扎根田野、服务农民。\n\n在传承着焦裕禄精神的河南兰考葡萄架乡，成研院打造的数字乡村平台全面落地应用。平台贯通乡、村两级业务全链条，整合民政、社保、农业等多部门数据资源，实现“一次采集、多方复用”。平台提供20余项高频服务“掌上办”，让“数据多跑路、群众少跑腿”，并依托AI智能预警，推动基层治理高效化、智能化。目前，平台已覆盖全乡20个行政村，累计收录人口数据超3万条，助力基层干部事务性工作量降低60%以上，实现网格管理一屏总览、AI智能安防实时监测、积分激励激活村民参与，打通基层治理“最后一公里”。\n\n在甘肃积石山县团结村，智慧乡村大脑聚焦民族地区灾后重建与乡村振兴，集成灾后重建、乡村振兴、民族团结、服务治理、综合监测五大模块，一屏统揽耕地分布、帮扶进展、集体经济、群众诉求等村庄“家底”，为村委科学决策提供精准支撑。同时，村里为50户重点关爱家庭免费安装一键呼叫终端，直连紧急救助与亲情通话，让乡村治理更有精度与温度。\n\n**产业富农：搭建产销双向联通桥梁**\n\n从培育乡村产业带头人到打通农产品出村进城通道，成研院以数字技术为纽带，一头连着田间地头的生产者，一头连着广阔市场的消费者，让好产品卖出好价钱，让乡村产业真正活起来、强起来。\n\n作为农业农村部“头雁”项目的技术支撑方，成研院为“头雁”人才培育系统提供的全方位信息化支撑。目前，“头雁”平台已覆盖全国31省份，累计服务7万余名乡村产业带头人，为乡村培育“造血种子”。\n\n如果说头雁工程是为乡村培育“造血种子”，那么“陆海优品公共服务平台”就是为乡村产业打通“出村进城、走向世界”的黄金通道。平台开设免费电商、直播、品控等培训，助力800多万农民转型“新农人”，推动中国移动对口帮扶的五省（区）八县、成渝地区双城经济圈特色农产品实现精准产销对接。目前，平台已带动超2000万元消费帮扶、实现3亿元农产品出口额，服务全国超600万用户，以数智力量畅通国内国际双循环。\n\n丰收，不再只是田野里的金黄稻浪，更是数据流中奔涌的智慧动能。当技术真正扎根泥土，当数字红利真正惠及农民，丰收便有了更坚实的支撑，乡村便有了更持久的活力。面向未来，成研院将继续以数智之力深耕沃土，让“藏粮于技”落地生根，让“数字乡村”可感可及，在广袤田野上书写属于新时代的丰收故事。","中国青年网","2026-09-21T00:00:00Z",84,{"impact":176,"substance":177,"depth":57,"authority":18,"freshness":154,"relevant":20,"comment":178},24,22,"央媒报道的农业大模型落地标杆案例，含亩产2481斤、增产15.5%、储粮损失降至0.2%等硬数据，产业示范价值突出，值得进入每日精选。",[180],{"name":172,"url":169},[182,26,27,28,183,184],"数字乡村","玉米单产提升","粮食仓储",[186,187],"中国移动 谷丰登 农业大模型","山西大同 玉米 亩产2481斤","中国移动谷丰登农业大模型-3100","2026-09-22T00:05:33.912382Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":8,"content":195,"source_name":196,"source_url":8,"published_at":197,"category":149,"cover_url":8,"hotness":12,"is_selected":13,"score":174,"score_detail":198,"sources":201,"tags":203,"search_phrases":206,"slug":209,"view_count":34,"doi":8,"paper":8,"created_at":210},2974,"人民日报海外版：神农大模型3.0+36个专项智能体 服务全国超10万农户","https:\u002F\u002Fnews.cau.edu.cn\u002Fmtndnew\u002Fcbf7a6637ffd4fffb20e26993670895d.htm","中国农业大学王耀君团队2023年12月发布神农大模型1.0版、2024年7月推出2.0版新增多模态识别、2025年10月升级至3.0版采用\"轻量化+多智能体\"架构同时推出36个针对具体农事问题的智能体。神农大模型已构建覆盖90%农业学科、80%农业场景的专属知识体系，核心知识库含1000万条农业知识图谱、2000万张标注图片、5000万条生产数据。辽宁杜连辉600亩玉米地使用后成本从480元\u002F亩降到不足400元\u002F亩，北京怀柔丹辉农业生菜基地育种智能体培育\"雁栖2号\"新品种。","**李晨**\n\n“近几年人工智能底层技术和应用场景发展很快，在农业上已经出现由‘人工智能+智能机械+大模型+规模化生产’集成的无人工厂、智慧农业生产方式。现在我国部分地区，这类技术已经从示范阶段转向了一些大型新型经营主体的主要生产方案。”9月16日，中国工程院院士、中国农业大学教授孙其信在2026世界农业科技创新大会（WAFI）上接受媒体采访时指出，当前全球已形成共识，未来一次重大产业变革可能以新一代人工智能技术为重要引擎，农业同样如此。人工智能引导下一轮农业生产方式变革，已经不是一个梦想，而是变成了现实。\n\n![Image 1](https:\u002F\u002Fnews.cau.edu.cn\u002Fimages\u002F2026-09\u002Fb8dbe90d2f9144beb4ace03f32463a25.jpg)\n\n孙其信接受媒体采访。中国农大供图\n\n孙其信指出，智能育种可视为第四代育种技术方向——用智能体和大模型训练，承接过去依靠育种家下地、拿尺子、记本子的部分工作。\n\n以小麦为例，中国农业大学相关团队正在研发人工智能小麦大模型。首先是积累田间数据，用无人机、地面多光谱设备记录作物从出苗到收获全过程，预计三五年后大宗粮食和畜禽的基础数据可满足智能育种模型需要。\n\n其次是积累基因数据，随着测序技术的快速发展，现在获取小麦高质量基因组信息的成本大幅降低，效率也显著提升。\n\n然后用人工智能关联田间表型与基因信息，例如输入地块表现可推测基因、输入基因可预测田间表现，从而大部分替代育种家从成千上万个体中筛选少数优良材料的工作，效率提升几十到几百倍。\n\n孙其信还提到地下根系识别。他说，根系在地下，过去育种后期才知道产量和品质，现在用新设备加人工智能算法，可测算每个品种的根系构型，设计少施肥、抗干旱、高产的根型。他对其规模化应用给出了保守判断：部分专项小模型已经起步，整体5到10年可看到大规模应用，真正在田间替代多数人工筛选还需要约5年数据积累。\n\n如今，中国农业在人工智能方面的实践越来越多的与国际接轨。孙其信介绍，去年WAFI发布中国农业大学神农大模型，盖茨基金会曾将该模型与世界一流模型对比，其在农业问题解决方案方面位居参评模型首位。\n\n中国农大团队开发了服务于非洲农业的“神农ZAO”智能体，并首先落地肯尼亚——用肯尼亚当地语言提问，模型输出适应当地农业生产的技术方案，而不是直接套用中国方案。“ZAO”在肯尼亚当地语言斯瓦西里语是种植管理的意思。\n\n孙其信说，世界需要这样一个平台，WAFI的目标不是一年开一次会，而是持续聚合全球顶尖科技资源、研发机构、投资金融机构和农业企业，产生“乘法效应”，服务中国农业发展，也服务全球农食系统绿色健康转型。\n\n[科学网2026年9月19日](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571737.shtm)","中国农业大学·人民日报海外版","2026-09-18T01:00:00Z",{"impact":176,"substance":58,"depth":59,"authority":199,"freshness":19,"relevant":20,"comment":200},15,"院士在WAFI大会披露神农大模型3.0与智能育种进展，权威性与信息增量俱佳，值得进入每日精选。",[202],{"name":196,"url":193},[26,27,28,204,205],"神农大模型","智能育种",[207,208],"神农大模型 智能体 农户","中国农业大学 智能育种 小麦","神农大模型智能体农户-2974","2026-09-20T00:03:00.349769Z",{"id":212,"title":213,"url":214,"summary":215,"summary_zh":8,"content":8,"source_name":216,"source_url":8,"published_at":217,"category":149,"cover_url":8,"hotness":12,"is_selected":13,"score":218,"score_detail":219,"sources":222,"tags":224,"search_phrases":226,"slug":229,"view_count":34,"doi":8,"paper":8,"created_at":230},2899,"佳木斯国家农高区伏羲大脑智慧农服APP累计服务600多万亩:覆盖6万多户农民","https:\u002F\u002Fwww.gskjb.cn\u002Fgskjb\u002F20260914\u002Fhtml\u002Fcontent_20260914003003.htm","在黑龙江佳木斯国家农业高新技术产业示范区,中国科学院研发的伏羲大脑农业大模型搭建的普惠式智慧农业服务体系正在改变传统农耕模式。农事综合服务手机APP成为农户的新农具,涵盖农事指导、农资直供、农机呼叫、卖粮、补贴、培训、金融保险等10项功能。农户上传地块信息后系统自动地块智能体检;系统每3-7天更新一次遥感数据。去年服务佳木斯100多万亩农田,一年半时间扩大到600多万亩,覆盖佳木斯6万多户农民。","甘肃科技报\u002F农民日报","2026-09-14T00:00:00Z",74,{"impact":177,"substance":58,"depth":199,"authority":153,"freshness":220,"relevant":20,"comment":221},5,"国家级科研机构大模型落地农业社会化服务的规模化案例，数据具体、可复制性强，值得进入每日精选。",[223],{"name":216,"url":214},[182,26,27,28,225],"农业社会化服务",[227,228],"佳木斯农高区 伏羲大脑 智慧农服APP","中国科学院 伏羲大脑 农业大模型","佳木斯农高区伏羲大脑智慧农服APP-2899","2026-09-19T00:06:07.688523Z"]