[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2366":3,"related-2366":71},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":65,"slug":68,"view_count":69,"doi":8,"paper":8,"created_at":70},2366,"江苏省农科院信息所所长任妮参加中国农学会计算机农业应用分会学术年会并调研","https:\u002F\u002Fjaas.ac.cn\u002Fxww\u002Fcsxw\u002Fart\u002F2026\u002Fart_9ea75f7d0f54473e91b59c0af3740142.html","9月4-6日第31届中国农学会计算机农业应用分会学术年会在哈尔滨召开，江苏农科院信息所所长任妮作《高质量数据集驱动的'人工智能+'农业应用场景构建》报告，期间赴哈工大、中国移动黑龙江、北大荒信息、黑龙江省农科院等调研'天工开悟'大模型场景应用与未来农场数字化建设。",null,"江苏省农业科学院 | 2026-09-09","2026-09-09T00:00:00Z","报道",10,false,61,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,15,12,13,5,1,"省级科研机构在全国性学术年会分享高质量数据集驱动农业AI应用，并实地调研大模型与未来农场建设，专业信息量尚可，但属会议调研通稿，时效偏旧，适合主题聚合而非每日精选。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","农业大模型","未来农场","农业数据集",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,50,55,60],{"code":32,"score":20,"supported":45,"title":46,"abstract":47,"tags":49},true,[],[48],"大模型",[27,28],{"code":51,"score":21,"supported":13,"title":52,"abstract":53,"tags":54},"F6",[],[],[29],{"code":56,"score":21,"supported":13,"title":57,"abstract":58,"tags":59},"I1",[],[],[26],{"code":61,"score":21,"supported":13,"title":62,"abstract":63,"tags":64},"I6",[],[],[30],[66,67],"农业人工智能 农业大模型 农业数据集 智慧农业","农业人工智能 农业大模型","农业人工智能农业大模型农业数据集智慧农业-2366",2,"2026-09-14T00:06:28.707561Z",{"total":72,"page":21,"page_size":72,"items":73},6,[74,141,240,279,329,384],{"id":75,"title":76,"url":77,"summary":78,"summary_zh":79,"content":8,"source_name":80,"source_url":77,"published_at":81,"category":82,"cover_url":8,"hotness":12,"is_selected":13,"score":83,"score_detail":84,"sources":90,"tags":92,"primary_topic":95,"secondary_topics":96,"classification":97,"search_phrases":112,"slug":115,"view_count":116,"doi":117,"paper":118,"created_at":140},4695,"AgroNLP-SimAssist: a multilingual transformer-based decision support simulator for crop disease diagnosis, advisory generation, and market intelligence","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12870-026-10040-8","Abstract There are three major shortcomings of agricultural NLP and need addressing, regardless of the recent developments in the field: (i) use of proprietary or small-scale datasets that affect reproducibility; (ii) one-task models that only focus on intent recognition but lack integration for crop disease diagnosis, advice, and market information; and (iii) the absence of simulators for evaluating the system’s performance during actual deployment. The application of precision agriculture is constrained by limitations in diverse farming environments. The study introduces AgroNLP-SimAssist, which represents a unified multilingual transformer architecture augmented by an easily deployable agricultural decision support simulator, as the proposed solution to overcome the challenges. The five major components constituting the proposed framework are: (1) domain-specific multilingual transformer embedding; (2) data collection and cleaning from publicly available agricultural datasets; (3) multiple prediction tasks including trend price estimation, crop disease detection, and advisory recommendation; (4) SHAP value explainability for expert validation; and (5) advisory simulation engine for evaluating latency and scalability. Multilingually available data sources, which include multilingual agricultural reports from the FAO organization, crop-disease descriptions from the PlantVillage knowledge base, and price-market narratives from the Agmarknet website, have been used for training and validation purposes. Among the 3,200 multilingual farmer queries dataset (in English, Hindi, and Kannada), there was an assignment of 70%, 15%, and 15% to the training, validation, and testing sets, respectively. In terms of accuracy compared to the existing classifiers, results from experiments yielded 96.1% disease classification, 94.3% advisory relevance, and 92.7% named entity recognition with F1-measure. The simulation system scaled up to 10,000 concurrent users with an inference latency of 0.35 s\u002Fquery. The proposed data-driven AgroNLP-SimAssist framework facilitates extension services and precision agriculture through providing a scalable, reproducible, and interpretable solution for intelligent agricultural advisory systems.","尽管近年来农业自然语言处理（NLP）领域取得了进展，但仍存在三个主要不足亟需解决：(i) 使用专有或小规模数据集，影响可复现性；(ii) 单任务模型仅关注意图识别，缺乏对作物病害诊断、建议和市场信息的整合；(iii) 缺乏用于评估系统实际部署性能的模拟器。精准农业的应用受到多样化农业环境的限制。本研究提出了AgroNLP-SimAssist，一种统一的多语言Transformer架构，并辅以易于部署的农业决策支持模拟器，作为克服上述挑战的解决方案。该框架由五个主要组件构成：(1) 领域特定的多语言Transformer嵌入；(2) 从公开农业数据集中进行数据收集与清洗；(3) 多预测任务，包括趋势价格估计、作物病害检测和咨询推荐；(4) SHAP值可解释性用于专家验证；(5) 咨询模拟引擎用于评估延迟和可扩展性。多语言可用数据源已用于训练和验证，包括联合国粮农组织（FAO）的多语言农业报告、PlantVillage知识库的作物病害描述以及Agmarknet网站的价格市场叙述。在包含3200条多语言农民查询数据集（英语、印地语和卡纳达语）中，训练集、验证集和测试集的分配比例分别为70%、15%和15%。与现有分类器相比，实验结果显示病害分类准确率达96.1%，咨询相关性达94.3%，命名实体识别的F1值为92.7%。模拟系统可扩展至10000名并发用户，推理延迟为0.35秒\u002F查询。所提出的数据驱动AgroNLP-SimAssist框架通过为智能农业咨询系统提供可扩展、可复现且可解释的解决方案，促进了推广服务和精准农业的发展。","BMC Plant Biology","2026-10-10T00:00:00Z","论文",81,{"impact":85,"substance":86,"depth":85,"authority":87,"freshness":88,"relevant":21,"comment":89},18,22,14,9,"多语言农业大模型整合病害诊断、农技咨询与市场情报，数据规模与仿真验证扎实，对智慧农业信息化有参考价值。",[91],{"name":80,"url":77},[26,27,28,93,94],"农业决策支持","作物病害诊断",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":98},{"candidates":99,"agricultural_evidence":45},[100,105],{"code":32,"score":19,"supported":45,"title":101,"abstract":103,"tags":104},[102],"decision support",[102],[27,28],{"code":56,"score":106,"supported":45,"title":107,"abstract":108,"tags":111},7,[],[109,110],"精准农业","precision agriculture",[26],[113,114],"AgroNLP-SimAssist 作物病害诊断","多语言农业大模型 农业决策支持","AgroNLP-SimAssist作物病害诊断-4695",0,"10.1186\u002Fs12870-026-10040-8",{"doi":117,"openalex_id":119,"authors":120,"venue":80,"cited_by_count":116,"oa_url":77,"card":133,"direction":137,"ingested_from":139},"W7221339938",[121,123,125,127,129,131],{"name":122,"orcid":8},"Priya Thirugnanam",{"name":124,"orcid":8},"Sandeep kumar Mathivanan",{"name":126,"orcid":8},"Sundara Kumar M R",{"name":128,"orcid":8},"Sangeetha Ramaswamy",{"name":130,"orcid":8},"Shruthi T S",{"name":132,"orcid":8},"Sangeetha S.K.B",{"tldr":134,"method":135,"finding":136,"direction":137,"opportunity":138},"提出多语言Transformer农业决策支持模拟器，统一实现作物病害诊断、农技建议与市场情报。","基于FAO、PlantVillage、Agmarknet多语数据训练Transf","病害分类96.1%、建议相关性94.3%、NER F1 92.7%，万级并发延迟0.35秒。","农业人工智能与决策模型","可扩展至更多语种与作物，并引入真实田间部署反馈闭环优化模拟器。","openalex","2026-10-11T23:30:56.064196Z",{"id":142,"title":143,"url":144,"summary":145,"summary_zh":146,"content":8,"source_name":147,"source_url":144,"published_at":81,"category":82,"cover_url":8,"hotness":12,"is_selected":13,"score":83,"score_detail":148,"sources":152,"tags":154,"primary_topic":157,"secondary_topics":158,"classification":170,"search_phrases":216,"slug":219,"view_count":69,"doi":220,"paper":221,"created_at":239},4656,"Artificial Intelligence, Foundation Models, and Digital Twins for Climate-Resilient Agriculture: A Review","https:\u002F\u002Fdoi.org\u002F10.54963\u002Fia.v2i2.103039","Climate change, increasing food demand, and resource constraints are accelerating the transition from conventional precision agriculture toward smart, autonomous, and climate-resilient agricultural systems. Artificial intelligence (AI), foundation models, digital twins (DTs), the Internet of Things (IoT), remote sensing, edge–cloud computing, and big data analytics are emerging as key technologies for data-driven agricultural management. This review critically evaluates recent advances in AI-driven intelligent agriculture, with emphasis on machine learning, deep learning, explainable AI (XAI), generative and agentic AI, multimodal foundation models, and digital twins, together with the sensing and computing infrastructures required for their deployment. Relevant literature was identified using keywords related to AI, foundation models, digital twins, IoT, precision agriculture, and climate-resilient agriculture, with emphasis on recent peer-reviewed studies addressing technological applications, performance, limitations, challenges, and future prospects. The selected literature was critically synthesized to identify technological trends, knowledge gaps, and emerging research opportunities. The review highlights that the greatest potential of intelligent agriculture lies in integrating heterogeneous data from sensors, UAVs (unmanned aerial vehicles), satellites, weather systems, field operations, and agricultural knowledge to support context-aware prediction, decision-making, and autonomous action. However, data quality, model generalizability, explainability, interoperability, cybersecurity, computational requirements, and large-scale field validation remain major challenges. This review further develops an integrated perspective connecting AI, foundation models, multimodal data infrastructures, digital twins, and autonomous agricultural systems within a unified cyber-physical ecosystem. Future priorities include trustworthy AI, agentic decision-making, semantic interoperability, federated learning, continuous learning, and self-learning digital twins toward Agriculture 6.0.","气候变化、粮食需求增长与资源约束正加速推动传统精准农业向智能、自主且气候适应型的农业系统转型。人工智能（AI）、基础模型、数字孪生（DT）、物联网（IoT）、遥感、边缘—云计算和大数据分析正成为数据驱动农业管理的关键技术。本综述批判性地评估了人工智能驱动智能农业的最新进展，重点关注机器学习、深度学习、可解释人工智能（XAI）、生成式与代理式人工智能、多模态基础模型和数字孪生，以及其部署所需的传感与计算基础设施。相关文献通过人工智能、基础模型、数字孪生、物联网、精准农业和气候适应型农业等关键词检索获得，重点纳入近期同行评审研究，涵盖技术应用、性能、局限性、挑战与未来前景。对所选文献进行了批判性综合，以识别技术趋势、知识空白和新兴研究机遇。本综述强调，智能农业的最大潜力在于整合来自传感器、无人机（UAV）、卫星、气象系统、田间作业和农业知识的异构数据，以支持情境感知预测、决策和自主行动。然而，数据质量、模型泛化能力、可解释性、互操作性、网络安全、计算需求和大规模田间验证仍是主要挑战。本综述进一步提出了一种整合视角，将人工智能、基础模型、多模态数据基础设施、数字孪生和自主农业系统连接于统一的网络物理生态系统中。未来优先方向包括可信人工智能、代理式决策、语义互操作性、联邦学习、持续学习以及面向农业6.0的自学习数字孪生。","Intelligent Agriculture",{"impact":86,"substance":149,"depth":85,"authority":19,"freshness":150,"relevant":21,"comment":151},20,8,"系统综述AI、基础模型与数字孪生在气候韧性农业中的集成路径与挑战，学术价值高，对智慧农业方向有较强参考意义。",[153],{"name":147,"url":144},[26,27,28,155,156],"数字孪生","气候韧性农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[159,165],{"code":160,"slug":161,"name":162,"path":163,"parent_code":164},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":166,"slug":167,"name":168,"path":169,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":13,"version":40,"method":41,"evidence":171},{"candidates":172,"agricultural_evidence":45},[173,183,188,195,202,209],{"code":32,"score":174,"supported":45,"title":175,"abstract":177,"tags":182},19,[176],"artificial intelligence",[178,179,180,181,176],"深度学习","机器学习","machine learning","deep learning",[27,28],{"code":56,"score":184,"supported":45,"title":185,"abstract":186,"tags":187},11,[],[109,155,110],[155,26],{"code":160,"score":12,"supported":45,"title":189,"abstract":190,"tags":194},[],[191,192,193],"气候变化","气候适应","climate change",[156],{"code":166,"score":88,"supported":45,"title":196,"abstract":197,"tags":201},[],[198,199,200],"物联网","internet of things","IoT",[],{"code":203,"score":72,"supported":45,"title":204,"abstract":205,"tags":208},"I3",[],[206,207],"遥感","remote sensing",[],{"code":210,"score":211,"supported":45,"title":212,"abstract":213,"tags":215},"A7",3,[],[214],"间作",[],[217,218],"农业基础模型 数字孪生","气候韧性农业 人工智能","农业基础模型数字孪生-4656","10.54963\u002Fia.v2i2.103039",{"doi":220,"openalex_id":222,"authors":223,"venue":147,"cited_by_count":116,"oa_url":144,"card":233,"direction":238,"ingested_from":139},"W7221321372",[224,227,230],{"name":225,"orcid":226},"Krishan Kumar","https:\u002F\u002Forcid.org\u002F0009-0005-7569-4590",{"name":228,"orcid":229},"Annu Khatri","https:\u002F\u002Forcid.org\u002F0009-0000-3103-9520",{"name":231,"orcid":232},"Indu Shekhar Thakur","https:\u002F\u002Forcid.org\u002F0000-0003-2382-1218",{"tldr":234,"method":235,"finding":236,"direction":137,"opportunity":237},"综述AI、基础模型与数字孪生如何支撑气候韧性农业，并指出关键挑战与未来方向。","文献综述，聚焦机器学习、深度学习、可解释AI、多模态基础模型与数字孪生。","最大潜力在于融合多源异构数据实现情境感知预测与自主决策，但数据质量与可解释性等仍是瓶颈。","可探索可信AI、联邦学习与自学习数字孪生在真实农田的规模化验证与语义互操作。","智慧农业 \u002F 农业物联网","2026-10-11T23:30:07.465569Z",{"id":241,"title":242,"url":243,"summary":244,"summary_zh":8,"content":245,"source_name":246,"source_url":8,"published_at":247,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":248,"score_detail":249,"sources":251,"tags":253,"primary_topic":256,"secondary_topics":257,"classification":258,"search_phrases":274,"slug":277,"view_count":69,"doi":8,"paper":8,"created_at":278},4615,"杭州诚邀全市创新力量共筑智慧农业——启动人工智能+农业应用场景征集","https:\u002F\u002Fwww.hangzhou.gov.cn\u002Fcol\u002Fcol812262\u002Fart\u002F2026\u002Fart_61fd9b1bd08e4756a87e25e3f296e645.html","根据《杭州市大力发展智慧农业打造人工智能+农业创新实践高地实施方案(2026—2030年)》部署,杭州启动人工智能+农业应用场景征集,聚焦农业大模型和智能体、智能农业设施装备、智慧管理服务平台三大方向。场景要求具备合规性、创新性、实效性、示范性、开放性,优先支持向中小企业、民营企业开放测试应用合作;入选场景将有机会在第二十三届中国国际农产品交易会智慧农业展区专题展示。","杭州智慧农业正呼唤更多“最强大脑”——根据《杭州市大力发展智慧农业 打造“人工智能+农业”创新实践高地实施方案（2026—2030年）》部署，杭州日前已启动“人工智能+农业”应用场景征集，面向全市发出“英雄帖”。\n\n本次征集聚焦三大方向：农业大模型和智能体，包括生物育种、生产管理、病虫害监测、疫病防治、产量预测，以及病虫害智能诊断、农事决策支持、智能植保作业规划、农情问答、农业知识智能检索等；智能农业设施装备，包括智能农机、无人驾驶作业、智能采摘、精准施肥施药、水肥一体化智能控制、物联网感知、卫星遥感与无人机监测等；智慧管理服务平台，覆盖智慧种植、智慧畜牧、智慧渔业和综合管理服务，如植物工厂、智慧大田、智慧茶园果园、畜禽体征监测、精准饲喂、疫病AI辅助诊断、陆基工厂化养殖、质量安全溯源、乡村数字治理等。\n\n场景要“上榜”，需满足合规性、创新性、实效性、示范性和开放性。比如，场景建设要符合国家、省相关产业政策、安全规范和行业标准；依托人工智能核心技术，具备技术创新、模式创新或应用创新；已落地或具备明确落地计划，能解决行业痛点、治理难点、民生堵点，降本增效成效可量化、可验证；具备较强的复制性、推广性和通用性；优先支持向中小企业、民营企业开放测试、应用、合作的场景项目。\n\n征集主体须为在杭州市注册或主要应用场景在杭州的企事业单位、科研院所、高等院校、新型农业经营主体、农业社会化服务组织等。可单一报送，也可联合报送，联合报送须明确1家牵头单位，参与单位原则上不超过5家。有意向的主体可先向所在区、县（市）农业农村部门提交材料，经区、县（市）初审推荐，由市级组织专家开展路演，确定首批应用场景名单。","杭州市政府门户网站 2026-10-09","2026-10-09T00:00:00Z",72,{"impact":16,"substance":149,"depth":17,"authority":19,"freshness":150,"relevant":21,"comment":250},"地市级政策落地动作，条款与征集方向具体，具备行业示范与可复制价值，值得进入每日精选。",[252],{"name":246,"url":243},[26,27,28,254,255],"智能农机","应用场景征集",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":259},{"candidates":260,"agricultural_evidence":45},[261,265,269],{"code":56,"score":18,"supported":45,"title":262,"abstract":263,"tags":264},[26],[26],[26],{"code":32,"score":20,"supported":45,"title":266,"abstract":267,"tags":268},[],[48],[27,28],{"code":270,"score":21,"supported":13,"title":271,"abstract":272,"tags":273},"F5",[],[],[254],[275,276],"杭州 人工智能 应用场景征集","杭州 智慧农业 实施方案","杭州人工智能应用场景征集-4615","2026-10-11T00:09:35.817480Z",{"id":280,"title":281,"url":282,"summary":283,"summary_zh":8,"content":284,"source_name":285,"source_url":8,"published_at":286,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":287,"score_detail":288,"sources":292,"tags":294,"primary_topic":297,"secondary_topics":301,"classification":303,"search_phrases":324,"slug":327,"view_count":20,"doi":8,"paper":8,"created_at":328},4464,"山东省农科院发布舜耕粮安农业遥感大模型 精度95%效率提升超100%","http:\u002F\u002Fwww.md2we.com\u002Fitems\u002F4263","山东省农科院发布舜耕粮安农业遥感大模型，基于20年来积累的2000万份人工样本的海量数据，覆盖山东全域全地貌、全物候周期，构建耕地智能提取—肥力墒情预测—作物精准识别—长势动态监测—病虫草害预警—作物智能估产全链条智能监测体系，精度高达95%，效率提升超100%；山东启动40个实数融合数字农业发展县建设，培育智慧农业应用场景近1000个。","每日热点 大众新闻 2026-10-04·10月4日 · 周日 报道\n\n阅读 0\n\nAI 评分\n\n精选\n\n影响力 24\n\n信息增量 22\n\n专业深度 18\n\n信源权威 10\n\n时效性 8\n\n省级“人工智能+农业”行动部署叠加舜耕·粮安遥感大模型95%精度等实质数据，信息增量与专业深度俱佳，值得进入每日精选。\n\n摘要\n\n山东省农科院发布舜耕粮安农业遥感大模型，基于20年2000万份人工样本，覆盖山东全域全地貌全物候周期，构建耕地智能提取-肥力墒情预测-作物精准识别-长势动态监测-病虫草害预警-作物智能估产全链条智能监测体系，精度95%、效率提升超100%。山东启动40个实数融合数字农业发展县建设，培育智慧农业应用场景近1000个。\n\n文 全文\n\n### 山东实施“人工智能+农业”行动：数据“种”进泥土，云端守护丰收\n\n2026-10-06 02:39:49\n\n![Image 1: Image 1](https:\u002F\u002Fwsrv.nl\u002F?url=https%3A%2F%2Fappimg.dzwww.com%2Fshare%2F2026%2F08%2F02%2Ff9c9a29336dd947_750.JPEG)\n\n麦田上空，无人机低空掠过，哪片麦苗缺氮、哪处有蚜虫，屏幕上即时标红；大棚内，人工智能（AI）算法值守，缺水、缺肥时，系统立刻报警；分拣车间里，AI视觉系统每秒“端详”上百颗苹果，按色泽、果径、瑕疵精准分级，误判率不到百分之一……\n\n从“靠天吃饭”到“知天而作”，当前人工智能正成为助力农业稳产提质、带动农民增收致富的“好帮手”，为农业农村现代化提供全新范式。\n\n2026年中央一号文件明确提出“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”，将AI技术从单点应用升级为贯穿农业全产业链的战略引擎。多家行业机构预测，2026年中国智慧农业市场规模将突破1340亿元，年复合增长率保持在15%以上。\n\n刚刚召开的省委十二届十一次全会，着眼把农业建成现代化大产业，推动农业数智化转型升级，明确提出实施“人工智能+农业”行动。何谓“人工智能+农业”？简言之，就是以人工智能技术为核心，驱动传统农业向智能化、高效化转型的新型生产体系。与会同志认为，从更深层次看，“人工智能+农业”不是简单的技术叠加，而是农业生产方式、组织形式和价值链条的系统性重塑。\n\n**AI种地，田野阡陌释放出更多可能**\n\n技术的生命力在于解决实际问题。放眼全国，农业面临着劳动力老龄化、气候变化风险加剧等压力，也存在特色产业附加值低、品牌力不强等问题。人工智能的介入推动农业从“靠经验”转向“靠数据”，为端牢“中国饭碗”、塑强农业产业注入确定性。在不少人看来，人工智能的应用推广，恰逢其时。\n\n“山东是农业大省，也是农业产业化发源地，产业规模大、业态全，生产者队伍素质高、接受新事物能力强。与此同时，山东工业基础雄厚，人工智能技术发展迅速。新技术在农业方面的转化推广具有显著的成本和效率优势。”省委党校（山东行政学院）教授、山东乡村振兴研究院院长刘岳认为，山东有着发展“人工智能+农业”的坚实基础，在完整的产业体系、可观市场规模、丰富应用场景等优势加持下，田野阡陌将释放出更多可能。\n\n夏日的鲁北平原，玉米拔节生长，一片生机盎然。在齐河县金穗粮食种植专业合作社的示范田里，一套黑色管网发挥着大作用。“这是我们铺设的水肥一体化设施。每30厘米有一个出水孔，能精准滴灌到玉米根底部位置。相比大水漫灌，它的灌溉速度快3倍，可省水30%，一人一天能管理200亩地。”合作社理事长袁本刚说。依托大数据平台，合作社还可在“云端”实施全程监管，动动手指就能依据检测数据制订分地块水肥管理方案，为各村玉米田定制个性化基肥配方，同步开展测土配方、作物叶绿素检测等服务。\n\n今年山东夏粮逆势丰收，连续4年实现面积、单产、总产“三增”，这离不开水肥一体化技术在大田的推广。数据显示，2025年，全省应用水肥一体化技术的粮油作物面积已达到630万亩。“水肥一体化与减垄增地、‘大小行’密植等结合，加速了良种、良法、良机、良田深度融合，有效提升了农田规模化、标准化种植水平。”袁本刚体会深切，水肥一体化的落地，让丰收有了更充实的底气。\n\n![Image 2: Image 2](https:\u002F\u002Fwsrv.nl\u002F?url=https%3A%2F%2Fappimg.dzwww.com%2Fshare%2F2026%2F08%2F02%2Ff9c9a2a30f6bab0_750.JPEG)\n\n种子是农业的“芯片”，也是保障粮食安全的关键，智能育种已成为育种科研人员聚焦的热点领域。\n\n今年4月，由中国农业科学院作物科学研究所与山东极智生物科技有限公司联合研发的“iWheat小麦智能设计育种平台”发布。对青岛农业大学小麦遗传育种团队来说，iWheat的应用正在帮助其攻克小麦育种“周期长、效率低、成本高”等行业难题。\n\n这一平台整合3万余份小麦种质资源的基因型、442个功能基因、6000余个功能位点及40余万条表型数据，为每一份种质建立起“数字身份证”，实现资源的精准检索与高效利用。“结合快速繁育体系，能将传统8年的育种周期压缩2至3年。”团队负责人张玉梅说，加速新品种的培育进程、降低育种成本，意味着更能提升整个种业行业的创新能力和竞争力，进而赋能小麦产业链上下游实现高质量发展。\n\n**让“技术盆景”真正落地为“产业风景”**\n\n农业大模型能够整合气象、土壤、作物生长等多维度数据，显著提升农业生产的精准性和预见性。我省明确，要加强农业大模型与人工智能体落地应用。\n\n在“中国蔬菜之乡”寿光，蔬菜大棚迎来了一批特殊“员工”。一群脚步灵活的机器狗，沿着田垄灵活穿梭，搭载的高清摄像头与传感器如同“火眼金睛”，精准识别病虫害迹象。巡检结束，数据实时传回后台，人工智能模型迅速给出种植决策。如今，寿光新建大棚已迭代升级至第七代智能物联网“云棚”，物联网应用率超过85%，劳动生产效率较以往翻倍。\n\n![Image 3: Image 3](https:\u002F\u002Fwsrv.nl\u002F?url=https%3A%2F%2Fappimg.dzwww.com%2Fshare%2F2026%2F08%2F02%2Ff9c9a2a2155c1ff_750.JPEG)\n\n就在一个多月前，省农科院发布“舜耕·粮安”农业遥感大模型。省农科院党委书记梁金光介绍，该模型基于20年来积累的2000万份人工样本的海量数据，覆盖山东全域全地貌、全物候周期，构建起“耕地智能提取—肥力墒情预测—作物精准识别—长势动态监测—病虫草害预警—作物智能估产”全链条智能监测体系，精度高达95%，效率提升超100%。\n\n在中游加工环节，大模型也能有效提升产品附加值与标准化水平。在烟台莱山区，由浪潮云洲旗下数农云（山东）信息科技有限公司联合中国农业大学计算中心、烟台葡乡农业开发有限公司共同打造的“莱山葡萄种植和酿造大模型”，一方面在种植端自动生成气候适应性调控方案，实现水肥精准配施、病虫害智能预警与防治，葡萄产量与品质同步提升；另一方面在酿造端用AI模拟发酵、陈酿全过程，动态优化关键参数，完成原料分级、发酵控制和酒体风味数字化建模。以此，让每一批葡萄酒的口感更稳定、品质更可控。\n\n当然，让人工智能与广袤乡村实现更深层次的双向赋能，还有很长的路要走——\n\n一套AI系统，传感终端、边缘计算、决策模型全算上，初始投入比较大。普通农户能不能用得起、用得好？\n\n农业生产周期长、环境复杂、区域差异大，怎样才能收集利用一批高质量农业数据，推动AI模型迭代？\n\n下沉到村、懂技术懂农业的青年人才较为短缺，农业人工智能技术人才怎样引育？\n\n让“技术盆景”真正落地为“产业风景”，刘岳认为，关键是鼓励龙头企业、高校院所与新型农业经营主体协同攻关，围绕粮食、果蔬、畜牧、水产等优势产业，打造一批“听得懂农事、算得清账本、管得了全程”的行业大模型。\n\n此外，刘岳还建议，要更加重视构建与农村新质生产力相适应的农村新型生产关系，盘活整合农村各类资产资源，为充分发挥数字化、智能化技术进步作用提供保障。“推动农村数字化从‘局域网’向‘互联网’转化，要破除长期存在的‘信息孤岛’，构建乡村振兴的数据底座。”\n\n（大众新闻记者 于新悦 刘兵）\n\n免责声明：以上数据来源于天眼查等平台，相关内容仅为传递更多信息之目的，不代表本网观点，亦不代表本网站赞同其观点或证实其内容的真实性。如有侵权请联系0531-85193563。  \n 本文来源：大众新闻·大众日报\n\n查看更多\n\n相关检索","大众新闻 2026-10-06","2026-10-06T00:00:00Z",76,{"impact":86,"substance":289,"depth":290,"authority":12,"freshness":72,"relevant":21,"comment":291},21,17,"省级农科院发布农业遥感大模型，含95%精度、2000万份样本等实质数据，信息增量与专业深度俱佳，值得进入每日精选。",[293],{"name":285,"url":282},[295,26,27,28,296],"数字乡村","遥感大模型",{"code":203,"slug":298,"name":299,"path":300,"parent_code":36},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[302],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":304},{"candidates":305,"agricultural_evidence":45},[306,311,315,320],{"code":203,"score":87,"supported":45,"title":307,"abstract":308,"tags":310},[206],[206,309],"估产",[],{"code":32,"score":19,"supported":45,"title":312,"abstract":313,"tags":314},[48],[48],[27,28],{"code":56,"score":106,"supported":45,"title":316,"abstract":317,"tags":319},[],[26,318],"数字农业",[26],{"code":61,"score":21,"supported":13,"title":321,"abstract":322,"tags":323},[],[],[295],[325,326],"山东省农科院 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让AI扛农活」——杜志雄、李想、孔德栋三位专家谈农业AI落地","https:\u002F\u002Fwww.farmer.com.cn\u002F2026\u002F09\u002F30\u002F991030099.html","农民日报全媒体记者李克欣对话中国社科院杜志雄、中国农业大学李想、雄安集团孔德栋三位专家。专家指出当前我国智慧农业正从早期试点示范迈向规模化、体系化推广应用新阶段，人工智能赋能农业的关键技术包括机器学习与深度学习、计算机视觉与智能感知（如200万张高清图像训练的AI作物表型模型识别精度达95%以上）、农业大模型（如中化iMAP节约水肥10%~20%、增产8%~10%）和智能农机与农业机器人（如土壤高通量检测机器人实现42项指标检测误差控制在5%以内）。","中国农网（农民日报）·2026年9月30日","2026-09-30T00:00:00Z",83,{"impact":338,"substance":86,"depth":85,"authority":87,"freshness":20,"relevant":21,"comment":339},24,"央媒多方专家对话，含AI作物表型、农业大模型、农业机器人等具体数据与结论，信息增量足，但发布已过一周，时效性略降。",[341],{"name":334,"url":332},[26,27,28,343,254],"农业机器人",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":347},{"candidates":348,"agricultural_evidence":45},[349,354,360,364,368,374],{"code":32,"score":184,"supported":45,"title":350,"abstract":351,"tags":353},[],[48,178,179,352],"计算机视觉",[27,28],{"code":270,"score":355,"supported":45,"title":356,"abstract":357,"tags":359},4,[],[358],"农机",[254],{"code":51,"score":355,"supported":45,"title":361,"abstract":362,"tags":363},[],[343],[343],{"code":56,"score":355,"supported":45,"title":365,"abstract":366,"tags":367},[],[26],[26],{"code":369,"score":211,"supported":45,"title":370,"abstract":371,"tags":373},"H2",[],[372],"土壤",[],{"code":203,"score":211,"supported":45,"title":375,"abstract":376,"tags":378},[],[377],"作物表型",[],[380,381],"中化 iMAP 大模型","AI 作物表型 识别精度","中化iMAP大模型-4124","2026-10-04T00:09:05.162758Z",{"id":385,"title":386,"url":387,"summary":388,"summary_zh":389,"content":8,"source_name":390,"source_url":387,"published_at":391,"category":82,"cover_url":8,"hotness":12,"is_selected":13,"score":392,"score_detail":393,"sources":395,"tags":397,"primary_topic":399,"secondary_topics":400,"classification":401,"search_phrases":429,"slug":432,"view_count":211,"doi":433,"paper":434,"created_at":448},4059,"Smart Agriculture Technology: A Review of Robotic Platforms, Sensing Modalities, Artificial Intelligence, Datasets, Evaluation Metrics, and Future Research Directions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16192137","Smart agriculture has moved beyond isolated precision-farming tools toward integrated cyber–physical systems that combine robotics, multimodal sensing, connectivity, artificial intelligence, and data-driven decision support. This review synthesizes recent developments in the field using a systematic critical review approach. It proposes a systems-level framework for analyzing smart agriculture across robotic platforms, sensing and digital infrastructure, AI methods, public datasets and benchmarks, evaluation metrics, application domains, and deployment maturity. The review covers ground robots, UAVs, harvesting manipulators, multi-robot systems, RGB and spectral sensing, LiDAR, IoT networks, edge\u002Fcloud platforms, AI-based perception and decision-making, and multimodal vision–language models. The analysis shows that the literature has made clear progress in prototypes, datasets, and task-specific performance, but several gaps still limit practical deployment. These include weak cross-season and cross-region validation, fragmented benchmarking practices, limited deployment-oriented metrics, insufficient techno-economic reporting, and unresolved challenges related to interoperability and human–robot interaction. Public datasets and emerging multimodal benchmarks show that the field is moving toward greater standardization; however, strong benchmark performance does not yet consistently translate into reliable field operation. The review concludes by outlining future research directions in benchmark realism, field-robust AI, edge deployment, digital twins, privacy-preserving learning, human-centered autonomy, and long-term agronomic and economic validation.","智慧农业已超越孤立的精准农业工具，迈向集机器人、多模态感知、连接性、人工智能和数据驱动决策支持于一体的信息物理系统。本综述采用系统性批判性综述方法，梳理了该领域的最新进展，提出了一个系统级框架，从机器人平台、感知与数字基础设施、人工智能方法、公共数据集与基准、评估指标、应用领域和部署成熟度等维度分析智慧农业。综述涵盖地面机器人、无人机、采摘机械臂、多机器人系统、RGB与光谱感知、激光雷达、物联网网络、边缘\u002F云平台、基于人工智能的感知与决策以及多模态视觉-语言模型。分析表明，文献在原型、数据集和特定任务性能方面已取得明显进展，但若干差距仍制约着实际部署，包括跨季节与跨区域验证薄弱、基准测试实践碎片化、面向部署的指标有限、技术经济报告不足，以及互操作性和人机交互方面的挑战尚未解决。公共数据集和新兴多模态基准表明该领域正朝着更高标准化方向迈进；然而，优异的基准性能尚未能一致地转化为可靠的田间作业。综述最后展望了未来研究方向，包括基准现实性、田间鲁棒人工智能、边缘部署、数字孪生、隐私保护学习、以人为中心的自主性以及长期农艺与经济验证。","Agriculture","2026-10-02T00:00:00Z",80,{"impact":85,"substance":86,"depth":174,"authority":19,"freshness":150,"relevant":21,"comment":394},"系统梳理智慧农业机器人、感知、AI与基准评测的综述，指出跨季节验证与部署指标缺口，对农业信息化研究有较高参考价值。",[396],{"name":390,"url":387},[26,27,343,155,398,30],"多模态感知",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":402},{"candidates":403,"agricultural_evidence":45},[404,409,413,417,421,425],{"code":56,"score":174,"supported":45,"title":405,"abstract":407,"tags":408},[406],"smart agriculture",[26,109,155,406],[155,26],{"code":32,"score":16,"supported":45,"title":410,"abstract":411,"tags":412},[176],[176,102],[27,398],{"code":166,"score":72,"supported":45,"title":414,"abstract":415,"tags":416},[],[198,200],[],{"code":210,"score":211,"supported":45,"title":418,"abstract":419,"tags":420},[],[214],[],{"code":51,"score":21,"supported":13,"title":422,"abstract":423,"tags":424},[],[],[343],{"code":61,"score":21,"supported":13,"title":426,"abstract":427,"tags":428},[],[],[30],[430,431],"农业机器人 多模态感知 评测基准","智慧农业 数字孪生 边缘部署","农业机器人多模态感知评测基准-4059","10.3390\u002Fagriculture16192137",{"doi":433,"openalex_id":435,"authors":436,"venue":390,"cited_by_count":116,"oa_url":387,"card":443,"direction":238,"ingested_from":139},"W7216116945",[437,440],{"name":438,"orcid":439},"Ghaith Al-refai","https:\u002F\u002Forcid.org\u002F0000-0002-8505-9719",{"name":441,"orcid":442},"Mahmoud Al-Sarayreh","https:\u002F\u002Forcid.org\u002F0000-0001-8711-0453",{"tldr":444,"method":445,"finding":446,"direction":238,"opportunity":447},"系统综述智慧农业机器人、传感、AI、数据集与评估指标，指出落地瓶颈并展望方向。","系统批判性综述，提出系统级框架，覆盖机器人、多模态传感、AI与基准。","原型与数据集进展明显，但跨季节验证、基准碎片化与部署指标不足阻碍落地。","可研究跨季节跨区域鲁棒AI、部署导向评估指标及数字孪生与隐私保护学习。","2026-10-03T23:30:10.036723Z"]