[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4124":3,"related-4124":90},{"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":84,"slug":87,"view_count":88,"doi":8,"paper":8,"created_at":89},4124,"农民日报《对话》栏目「用算法种好地 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）。",null,"中国农网（农民日报）·2026年9月30日","2026-09-30T00:00:00Z","报道",10,false,83,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},24,22,18,14,5,1,"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。",[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","农业机器人","智能农机",{"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":46},[44,54,61,66,71,78],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":53},11,true,[],[49,50,51,52],"大模型","深度学习","机器学习","计算机视觉",[27,28],{"code":55,"score":56,"supported":46,"title":57,"abstract":58,"tags":60},"F5",4,[],[59],"农机",[30],{"code":62,"score":56,"supported":46,"title":63,"abstract":64,"tags":65},"F6",[],[29],[29],{"code":67,"score":56,"supported":46,"title":68,"abstract":69,"tags":70},"I1",[],[26],[26],{"code":72,"score":73,"supported":46,"title":74,"abstract":75,"tags":77},"H2",3,[],[76],"土壤",[],{"code":79,"score":73,"supported":46,"title":80,"abstract":81,"tags":83},"I3",[],[82],"作物表型",[],[85,86],"中化 iMAP 大模型","AI 作物表型 识别精度","中化iMAP大模型-4124",7,"2026-10-04T00:09:05.162758Z",{"total":91,"page":21,"page_size":91,"items":92},6,[93,137,202,262,340,422],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":8,"content":98,"source_name":99,"source_url":8,"published_at":100,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":101,"score_detail":102,"sources":109,"tags":111,"primary_topic":113,"secondary_topics":114,"classification":115,"search_phrases":131,"slug":134,"view_count":135,"doi":8,"paper":8,"created_at":136},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":103,"substance":104,"depth":105,"authority":106,"freshness":107,"relevant":21,"comment":108},16,20,15,13,8,"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。",[110],{"name":99,"url":96},[26,27,28,30,112],"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":116},{"candidates":117,"agricultural_evidence":46},[118,123,127],{"code":67,"score":119,"supported":46,"title":120,"abstract":121,"tags":122},12,[26],[26],[26],{"code":32,"score":20,"supported":46,"title":124,"abstract":125,"tags":126},[],[49],[27,28],{"code":55,"score":21,"supported":13,"title":128,"abstract":129,"tags":130},[],[],[30],[132,133],"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",2,"2026-10-11T00:09:35.817480Z",{"id":138,"title":139,"url":140,"summary":141,"summary_zh":142,"content":8,"source_name":143,"source_url":140,"published_at":144,"category":145,"cover_url":8,"hotness":12,"is_selected":13,"score":146,"score_detail":147,"sources":149,"tags":151,"primary_topic":154,"secondary_topics":155,"classification":156,"search_phrases":182,"slug":185,"view_count":20,"doi":186,"paper":187,"created_at":201},4798,"Adaptability of Lightweight AI Models for Embedded Deployment: A Case Study of an Agricultural Monitoring Robot","https:\u002F\u002Fdoi.org\u002F10.54254\u002F2755-2721\u002F2026.ld37676","Many artificial intelligence programs require powerful computing hardware, whereas agricultural robots, drones, and smart cameras rely on small embedded devices with limited computing power, battery capacity, and heat dissipation. Lightweight models reduce parameters and computational costs while maintaining acceptable accuracy, enabling AI deployment on resource-constraint network devices. This paper reviews key lightweighting methods, including compact network architectures, model pruning, model quantization, and knowledge distillation. It compares common platforms such as Jetson, Raspberry Pi, and microcontrollers. Using an agricultural monitoring robot as an example, this study proposes a system combining a camera, a lightweight YOLO model, Jetson, and a microcontroller for real-time crop monitoring. Experiments are designed to evaluate accuracy, speed, power consumption, temperature, and battery life. Rather than pursuing maximum model complexity, this paper focuses on explaining how to select a model that is truly suitable for the actual device. For agricultural robots, effective deployment requires sufficient accuracy, real-time performance, and low energy consumption.","许多人工智能程序需要强大的计算硬件，而农业机器人、无人机和智能摄像头则依赖于计算能力、电池容量和散热能力均有限的小型嵌入式设备。轻量级模型在保持可接受精度的同时减少参数和计算成本，使人工智能能够部署在资源受限的网络设备上。本文综述了关键的轻量化方法，包括紧凑网络架构、模型剪枝、模型量化和知识蒸馏。本文比较了Jetson、树莓派和微控制器等常见平台。以农业监测机器人为例，本研究提出了一种结合摄像头、轻量级YOLO模型、Jetson和微控制器的系统，用于实时作物监测。实验旨在评估精度、速度、功耗、温度和电池寿命。本文并非追求最大化的模型复杂度，而是着重解释如何选择真正适合实际设备的模型。对于农业机器人而言，有效部署需要足够的精度、实时性能和低能耗。","Applied and Computational Engineering","2026-10-13T00:00:00Z","论文",69,{"impact":119,"substance":18,"depth":103,"authority":106,"freshness":12,"relevant":21,"comment":148},"系统梳理轻量化模型在农业嵌入式设备上的部署路径，并以监测机器人案例给出可复现方案，对智慧农业工程落地有参考价值。",[150],{"name":143,"url":140},[26,27,29,152,153],"边缘计算","作物监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":157},{"candidates":158,"agricultural_evidence":46},[159,165,169,173,177],{"code":32,"score":88,"supported":46,"title":160,"abstract":161,"tags":164},[],[162,163],"artificial intelligence","YOLO",[27],{"code":62,"score":56,"supported":46,"title":166,"abstract":167,"tags":168},[],[29],[29],{"code":67,"score":21,"supported":13,"title":170,"abstract":171,"tags":172},[],[],[26],{"code":79,"score":21,"supported":13,"title":174,"abstract":175,"tags":176},[],[],[153],{"code":178,"score":21,"supported":13,"title":179,"abstract":180,"tags":181},"I5",[],[],[152],[183,184],"农业监测机器人 轻量YOLO Jetson","嵌入式AI 模型轻量化","农业监测机器人轻量YOLOJetson-4798","10.54254\u002F2755-2721\u002F2026.ld37676",{"doi":186,"openalex_id":188,"authors":189,"venue":143,"cited_by_count":192,"oa_url":193,"card":194,"direction":198,"ingested_from":200},"W7221561538",[190],{"name":191,"orcid":8},"Jingen Zhou",0,"https:\u002F\u002Face.ewapub.com\u002Farticle\u002Fview\u002F37676.pdf",{"tldr":195,"method":196,"finding":197,"direction":198,"opportunity":199},"综述轻量化AI方法，并以农业监测机器人为例，提出相机+轻量YOLO+Jetson+单片机的嵌入式部署","综述网络压缩、剪枝、量化、蒸馏，对比Jetson、树莓派、单片机，设计农业机器人","农业机器人部署需兼顾足够精度、实时性与低能耗，而非追求最大模型复杂度。","智慧农业 \u002F 农业物联网","可研究面向田间多变环境的轻量化模型自适应选择与能效-精度联合优化部署策略。","openalex","2026-10-13T23:30:08.379081Z",{"id":203,"title":204,"url":205,"summary":206,"summary_zh":207,"content":8,"source_name":208,"source_url":205,"published_at":209,"category":145,"cover_url":8,"hotness":12,"is_selected":13,"score":210,"score_detail":211,"sources":214,"tags":216,"primary_topic":219,"secondary_topics":220,"classification":221,"search_phrases":235,"slug":238,"view_count":192,"doi":239,"paper":240,"created_at":261},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",81,{"impact":18,"substance":17,"depth":18,"authority":19,"freshness":212,"relevant":21,"comment":213},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。",[215],{"name":208,"url":205},[26,27,28,217,218],"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":222},{"candidates":223,"agricultural_evidence":46},[224,229],{"code":32,"score":106,"supported":46,"title":225,"abstract":227,"tags":228},[226],"decision support",[226],[27,28],{"code":67,"score":88,"supported":46,"title":230,"abstract":231,"tags":234},[],[232,233],"精准农业","precision agriculture",[26],[236,237],"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","10.1186\u002Fs12870-026-10040-8",{"doi":239,"openalex_id":241,"authors":242,"venue":208,"cited_by_count":192,"oa_url":205,"card":255,"direction":259,"ingested_from":200},"W7221339938",[243,245,247,249,251,253],{"name":244,"orcid":8},"Priya Thirugnanam",{"name":246,"orcid":8},"Sandeep kumar Mathivanan",{"name":248,"orcid":8},"Sundara Kumar M R",{"name":250,"orcid":8},"Sangeetha Ramaswamy",{"name":252,"orcid":8},"Shruthi T S",{"name":254,"orcid":8},"Sangeetha S.K.B",{"tldr":256,"method":257,"finding":258,"direction":259,"opportunity":260},"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。","2026-10-11T23:30:56.064196Z",{"id":263,"title":264,"url":265,"summary":266,"summary_zh":267,"content":8,"source_name":268,"source_url":265,"published_at":269,"category":145,"cover_url":8,"hotness":12,"is_selected":13,"score":270,"score_detail":271,"sources":273,"tags":275,"primary_topic":278,"secondary_topics":284,"classification":290,"search_phrases":319,"slug":322,"view_count":192,"doi":8,"paper":323,"created_at":339},4670,"SMART FERTILIZER DISTRIBUTION SYSTEM: A COMPREHENSIVE REVIEW","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F334","Agriculture is an important sector for food production and economic development, and the efficient use of fertilizers is essential for achieving better crop growth and productivity. Conventional fertilizer distribution methods largely depend on manual application, which can lead to uneven distribution, excessive fertilizer usage, increased labor requirements, and wastage of agricultural resources. The development of automation and Internet of Things (IoT) technologies has provided new opportunities for improving fertilizer distribution and management in agricultural fields. This review paper presents a comprehensive study of Smart Fertilizer Distribution Systems, focusing on the technologies, control methods, communication techniques, and approaches reported in existing research. Various systems using microcontrollers, IoT devices, wireless communication, web-based platforms, sensors, motor or pump control, driver circuits, and solenoid valves are reviewed. The working principles of automated fertilizer distribution systems and their role in controlling fertilizer flow and reducing manual intervention are discussed. Different communication technologies, including Wi-Fi and other wireless methods, are compared based on their applicability, cost, operating range, reliability, and control capability. The review also examines the major advantages and limitations of existing systems, such as automation, remote operation, improved fertilizer management, reduced labor, network dependency, power requirements, sensor accuracy, and system complexity. A comparative analysis of selected research works is presented to identify the techniques used, their contributions, and the limitations that remain to be addressed. The review further identifies research gaps and discusses future developments such as automatic fertilizer scheduling, multi-zone fertilizer distribution, real-time monitoring, cloud-based data storage, artificial intelligence, machine learning, and integration with renewable energy sources. The study concludes that smart fertilizer distribution can improve the efficiency, accuracy, and sustainability of fertilizer application and has significant potential for future precision agriculture applications.","农业是粮食生产和经济发展的重要部门，而肥料的高效利用对于实现作物更好生长和生产力提升至关重要。传统的肥料分配方法在很大程度上依赖人工施用，这可能导致分配不均、肥料过量使用、劳动力需求增加以及农业资源浪费。自动化和物联网（IoT）技术的发展为改善农田肥料分配与管理提供了新的机遇。本文综述对智能肥料分配系统进行了全面研究，重点关注现有研究中报道的技术、控制方法、通信技术和方法。本文回顾了使用微控制器、物联网设备、无线通信、基于网络的平台、传感器、电机或泵控制、驱动电路和电磁阀的各种系统。讨论了自动化肥料分配系统的工作原理及其在控制肥料流量和减少人工干预方面的作用。基于适用性、成本、工作范围、可靠性和控制能力，比较了包括Wi-Fi和其他无线方法在内的不同通信技术。本综述还考察了现有系统的主要优势和局限性，如自动化、远程操作、改善肥料管理、减少劳动力、网络依赖、功率需求、传感器精度和系统复杂性。本文对选定的研究工作进行了比较分析，以识别所采用的技术、其贡献以及仍有待解决的局限性。本综述进一步指出了研究空白，并讨论了未来发展方向，如自动肥料调度、多区域肥料分配、实时监测、基于云的数据存储、人工智能、机器学习以及与可再生能源的集成。该研究得出结论，智能肥料分配可以提高肥料施用的效率、准确性和可持续性，并在未来精准农业应用中具有巨大潜力。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",61,{"impact":119,"substance":103,"depth":105,"authority":119,"freshness":91,"relevant":21,"comment":272},"综述性论文系统梳理智能施肥系统的技术路线与研究空白，对智慧农业领域有参考价值，但属文献综述而非原创突破，时效性一般。",[274],{"name":268,"url":265},[26,27,276,277,30],"精准施肥","农业物联网",{"code":279,"slug":280,"name":281,"path":282,"parent_code":283},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[285,286],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":178,"slug":287,"name":288,"path":289,"parent_code":36},"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":291},{"candidates":292,"agricultural_evidence":46},[293,299,304,311,315],{"code":279,"score":105,"supported":46,"title":294,"abstract":296,"tags":298},[295],"fertilizer",[297,295],"肥料",[276],{"code":32,"score":12,"supported":46,"title":300,"abstract":301,"tags":303},[],[51,302,162],"machine learning",[27],{"code":178,"score":12,"supported":46,"title":305,"abstract":306,"tags":310},[],[307,308,309],"物联网","internet of things","IoT",[277],{"code":67,"score":88,"supported":46,"title":312,"abstract":313,"tags":314},[],[232,233],[26],{"code":55,"score":21,"supported":13,"title":316,"abstract":317,"tags":318},[],[],[30],[320,321],"智能施肥系统 物联网","精准施肥 自动化","智能施肥系统物联网-4670",{"doi":8,"openalex_id":324,"authors":325,"venue":268,"cited_by_count":192,"oa_url":265,"card":334,"direction":198,"ingested_from":200},"W7221394563",[326,328,330,332],{"name":327,"orcid":8},"Anjali Manoj Kumbhar",{"name":329,"orcid":8},"Shruti Prashant Kulkarni",{"name":331,"orcid":8},"Snehal Santosh Khune",{"name":333,"orcid":8},"B. B. Godbole",{"tldr":335,"method":336,"finding":337,"direction":198,"opportunity":338},"综述智能施肥系统的技术、控制与通信方法，并指出研究空白与未来方向。","综述基于微控制器、物联网、无线通信、传感器与电磁阀的自动施肥系统研究。","智能施肥可提升施肥效率、精度与可持续性，但存在网络依赖、功耗与传感器精度等局限。","可探索AI驱动的自动施肥调度、多区域分配与云边协同的实时精准施肥系统。","2026-10-11T23:30:16.128775Z",{"id":341,"title":342,"url":343,"summary":344,"summary_zh":345,"content":8,"source_name":346,"source_url":343,"published_at":209,"category":145,"cover_url":8,"hotness":12,"is_selected":13,"score":210,"score_detail":347,"sources":349,"tags":351,"primary_topic":354,"secondary_topics":355,"classification":363,"search_phrases":399,"slug":402,"view_count":135,"doi":403,"paper":404,"created_at":421},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":17,"substance":104,"depth":18,"authority":106,"freshness":107,"relevant":21,"comment":348},"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。",[350],{"name":346,"url":343},[26,27,28,352,353],"数字孪生","气候韧性农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[356,362],{"code":357,"slug":358,"name":359,"path":360,"parent_code":361},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":178,"slug":287,"name":288,"path":289,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":364},{"candidates":365,"agricultural_evidence":46},[366,372,376,383,387,393],{"code":32,"score":367,"supported":46,"title":368,"abstract":369,"tags":371},19,[162],[50,51,302,370,162],"deep learning",[27,28],{"code":67,"score":45,"supported":46,"title":373,"abstract":374,"tags":375},[],[232,352,233],[352,26],{"code":357,"score":12,"supported":46,"title":377,"abstract":378,"tags":382},[],[379,380,381],"气候变化","气候适应","climate change",[353],{"code":178,"score":212,"supported":46,"title":384,"abstract":385,"tags":386},[],[307,308,309],[],{"code":79,"score":91,"supported":46,"title":388,"abstract":389,"tags":392},[],[390,391],"遥感","remote sensing",[],{"code":394,"score":73,"supported":46,"title":395,"abstract":396,"tags":398},"A7",[],[397],"间作",[],[400,401],"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":403,"openalex_id":405,"authors":406,"venue":346,"cited_by_count":192,"oa_url":343,"card":416,"direction":198,"ingested_from":200},"W7221321372",[407,410,413],{"name":408,"orcid":409},"Krishan Kumar","https:\u002F\u002Forcid.org\u002F0009-0005-7569-4590",{"name":411,"orcid":412},"Annu Khatri","https:\u002F\u002Forcid.org\u002F0009-0000-3103-9520",{"name":414,"orcid":415},"Indu Shekhar Thakur","https:\u002F\u002Forcid.org\u002F0000-0003-2382-1218",{"tldr":417,"method":418,"finding":419,"direction":259,"opportunity":420},"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。","2026-10-11T23:30:07.465569Z",{"id":423,"title":424,"url":425,"summary":426,"summary_zh":427,"content":8,"source_name":428,"source_url":425,"published_at":100,"category":145,"cover_url":8,"hotness":429,"is_selected":13,"score":210,"score_detail":430,"sources":432,"tags":437,"primary_topic":439,"secondary_topics":440,"classification":446,"search_phrases":484,"slug":487,"view_count":21,"doi":488,"paper":489,"created_at":499},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)",25,{"impact":17,"substance":104,"depth":18,"authority":106,"freshness":107,"relevant":21,"comment":431},"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。",[433,434],{"name":428,"url":425},{"name":435,"url":436},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[26,27,29,277,232,438],"自动驾驶农机",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[441,445],{"code":55,"slug":442,"name":443,"path":444,"parent_code":283},"machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery",{"code":178,"slug":287,"name":288,"path":289,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":447},{"candidates":448,"agricultural_evidence":46},[449,454,462,466,470,474,480],{"code":32,"score":18,"supported":46,"title":450,"abstract":451,"tags":453},[162],[51,52,302,162,452],"computer vision",[27],{"code":55,"score":455,"supported":46,"title":456,"abstract":458,"tags":461},17,[457],"agricultural machinery",[459,460,457],"农业机械","收割机",[],{"code":178,"score":12,"supported":46,"title":463,"abstract":464,"tags":465},[],[307,308,309],[277],{"code":79,"score":91,"supported":46,"title":467,"abstract":468,"tags":469},[],[390,391],[],{"code":67,"score":20,"supported":46,"title":471,"abstract":472,"tags":473},[],[232],[26,232],{"code":475,"score":73,"supported":46,"title":476,"abstract":477,"tags":479},"F4",[],[478],"除草",[],{"code":62,"score":21,"supported":13,"title":481,"abstract":482,"tags":483},[],[],[29],[485,486],"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":488,"openalex_id":490,"authors":491,"venue":428,"cited_by_count":192,"oa_url":425,"card":494,"direction":198,"ingested_from":200},"W7221138004",[492],{"name":493,"orcid":8},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":495,"method":496,"finding":497,"direction":198,"opportunity":498},"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"]