[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4491":3,"related-4491":103},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":43,"search_phrases":83,"slug":86,"view_count":21,"doi":87,"paper":88,"created_at":102},4491,"INTEGRATING \"SMART AGRICULTURE\" TECHNOLOGIES INTO THE EDUCATIONAL PROCESS IN AGRICULTURAL EDUCATION","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23242369","This thesis discusses the pedagogical potential of integrating “Smart Agriculture” technologies into the teaching of specialized subjects at higher agricultural education institutions. Internet of Things (IoT), sensors, digital monitoring, Geographic Information Systems (GIS), remote sensing, and artificial intelligence tools enable students to connect theoretical knowledge with real-world agrotechnological tasks. It is proposed that these technologies should be used in lessons not merely as demonstration tools, but as means of identifying problems, collecting and analyzing data, and making practical decisions.","本论文探讨了将“智慧农业”技术融入高等农业院校专业课程教学的教学潜力。物联网（IoT）、传感器、数字监测、地理信息系统（GIS）、遥感以及人工智能工具能够帮助学生将理论知识与现实农业技术任务相联系。本文提出，这些技术不应仅在课堂上作为演示工具使用，而应作为发现问题、收集与分析数据以及做出实际决策的手段。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-08T00:00:00Z","论文",10,false,64,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,16,15,9,1,"探讨将智慧农业技术融入高等农业教学的论文，属细分领域教育研究，有方法性内容但影响面有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","物联网","遥感","农业教育",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,56,62,69,76],{"code":50,"score":19,"supported":51,"title":52,"abstract":54,"tags":55},"I1",true,[53],"smart agriculture",[26,53],[26],{"code":32,"score":13,"supported":51,"title":57,"abstract":58,"tags":61},[],[28,59,60],"internet of things","IoT",[28],{"code":39,"score":20,"supported":51,"title":63,"abstract":64,"tags":68},[],[65,66,67],"地理信息","GIS","geographic information",[],{"code":70,"score":71,"supported":51,"title":72,"abstract":73,"tags":75},"I3",7,[],[29,74],"remote sensing",[29],{"code":77,"score":78,"supported":51,"title":79,"abstract":80,"tags":82},"I2",4,[],[81],"artificial intelligence",[27],[84,85],"智慧农业 农业教育 课程","农业人工智能 农业教育 智慧农业 物联网","智慧农业农业教育课程-4491","10.5281\u002Fzenodo.23242369",{"doi":87,"openalex_id":89,"authors":90,"venue":10,"cited_by_count":93,"oa_url":6,"card":94,"direction":100,"ingested_from":101},"W7221022747",[91],{"name":92,"orcid":9},"Shamsiyeva Ogiloy",0,{"tldr":95,"method":96,"finding":97,"direction":98,"opportunity":99},"探讨将智慧农业技术融入高等农业教育，作为学生解决实际农艺问题的教学工具。","整合物联网、传感器、GIS、遥感与AI工具于专业课程教学。","技术不应仅作演示，而应成为发现问题、采集分析数据并决策的手段。","数字乡村与农业信息化","可研究智慧农业教学对学生数据决策能力的量化影响及课程设计模式。","智慧农业 \u002F 农业物联网","openalex","2026-10-09T23:30:08.054996Z",{"total":104,"page":21,"page_size":104,"items":105},6,[106,190,298,403,492,553],{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":9,"source_name":112,"source_url":109,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":114,"score_detail":115,"sources":120,"tags":122,"primary_topic":124,"secondary_topics":125,"classification":131,"search_phrases":171,"slug":174,"view_count":78,"doi":175,"paper":176,"created_at":189},1724,"Integrating Artificial Intelligence and IoT for Precision Farming: A Review of Agriculture 4.0 Technologies, Applications, and Challenges","https:\u002F\u002Fdoi.org\u002F10.67564\u002Firassjms.v3.i9.0186","Agriculture 4.0 marks a decisive shift from mechanized and chemically intensive farming toward a data-driven, automated, and connected model of food production. At the center of this shift lies the convergence of artificial intelligence (AI) and the Internet of Things (IoT), which together allow farms to sense, interpret, and act on field conditions with a precision that manual methods cannot match. This review synthesizes the current body of literature on AI-IoT integration in precision farming, covering the core enabling technologies — smart sensors, unmanned aerial vehicles (UAVs), geographic information systems (GIS), edge and cloud computing, and block chain-based traceability — and the layered architecture through which they combine into functioning smart-farming systems. Drawing on peer-reviewed studies published largely between 2019 and 2025, the review examines representative applications in smart irrigation, crop-disease detection, yield prediction, and supply-chain traceability, consolidates the recurring barriers reported across this literature, and provides a detailed, yearordered comparison of technique, dataset, performance metrics, and reported limitations across ten representative studies. The review concludes by outlining research gaps — particularly around affordable edge-AI models, interoperable data standards, and region-specific validation in smallholder contexts such as India — that merit attention in future work.","农业4.0标志着从机械化、化学密集型农业向数据驱动、自动化、互联的食品生产模式的决定性转变。这一转变的核心在于人工智能（AI）与物联网（IoT）的融合，二者共同使农场能够以人工方法无法企及的精度感知、解读并响应田间条件。本综述综合了当前关于精准农业中AI-IoT整合的文献体系，涵盖核心使能技术——智能传感器、无人机（UAVs）、地理信息系统（GIS）、边缘与云计算，以及基于区块链的可追溯性——以及它们组合成功能性智慧农业系统的分层架构。基于主要发表于2019年至2025年间的同行评审研究，本综述考察了智能灌溉、作物病害检测、产量预测和供应链可追溯性中的代表性应用，整合了文献中反复出现的障碍，并对十项代表性研究进行了按年份排序的技术、数据集、性能指标及报告局限性的详细比较。综述最后指出了研究空白——特别是围绕经济实惠的边缘AI模型、互操作性数据标准，以及在印度等小农情境下的区域特定验证——这些值得在未来工作中予以关注。","IRASS Journal of Multidisciplinary Studies","2026-09-04T00:00:00Z",70,{"impact":116,"substance":117,"depth":116,"authority":13,"freshness":118,"relevant":21,"comment":119},18,22,2,"综述AI与IoT在精准农业中的集成应用，涵盖技术、应用与挑战，对行业有参考价值，但时效性较低。",[121],{"name":112,"url":109},[26,27,28,123],"精准农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[126,130],{"code":77,"slug":127,"name":128,"path":129,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":132},{"candidates":133,"agricultural_evidence":51},[134,138,142,146,151,158,165],{"code":32,"score":116,"supported":51,"title":135,"abstract":136,"tags":137},[60],[28,59,60],[28],{"code":77,"score":17,"supported":51,"title":139,"abstract":140,"tags":141},[81],[81],[27],{"code":39,"score":20,"supported":51,"title":143,"abstract":144,"tags":145},[],[65,66,67],[],{"code":50,"score":147,"supported":51,"title":148,"abstract":149,"tags":150},8,[],[26,123],[26,123],{"code":152,"score":104,"supported":51,"title":153,"abstract":154,"tags":157},"H3",[],[155,156],"灌溉","irrigation",[],{"code":159,"score":160,"supported":51,"title":161,"abstract":162,"tags":164},"F5",3,[],[163],"机械化",[],{"code":166,"score":160,"supported":51,"title":167,"abstract":168,"tags":170},"G2",[],[169],"smallholder",[],[172,173],"农业人工智能 智慧农业 精准农业 物联网","农业人工智能 智慧农业","农业人工智能智慧农业精准农业物联网-1724","10.67564\u002Firassjms.v3.i9.0186",{"doi":175,"openalex_id":177,"authors":178,"venue":112,"cited_by_count":93,"oa_url":183,"card":184,"direction":100,"ingested_from":101},"W7208758888",[179,181],{"name":180,"orcid":9},"G Swetha",{"name":182,"orcid":9},"Prof T Anuradha","https:\u002F\u002Firasspublisher.com\u002Fassets\u002Farticles\u002F1788493097.pdf",{"tldr":185,"method":186,"finding":187,"direction":100,"opportunity":188},"综述AI与IoT在精准农业中的集成技术、应用与挑战，提出未来研究方向。","系统综述2019-2025年文献，分析架构、应用案例及十项代表性研究。","AI-IoT集成提升农业效率，但存在成本、互操作性和小农验证等挑战。","研究空白包括低成本边缘AI模型、数据标准互操作及小农场景的区域验证，可探索适应性方案。","2026-09-05T23:30:09.434649Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":197,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":204,"tags":206,"primary_topic":209,"secondary_topics":210,"classification":213,"search_phrases":273,"slug":276,"view_count":93,"doi":277,"paper":278,"created_at":297},4302,"BIG DATA IN AGRONOMY: TECHNOLOGIES, APPLICATIONS, BENEFITS AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.4238\u002Fk6hcgw77","Big Data is becoming an important component of modern agronomy by enabling the integration, processing and analysis of large, diverse and continuously generated datasets for improved agricultural decision-making. Agricultural Big Data is generated from multiple sources, including remote sensing satellites, Unmanned Aerial Vehicles (UAVs), Internet of Things (IoT) sensors, weather stations, farm machinery, field observations and historical databases. The integration of these data sources with Geographic Information Systems (GIS), cloud computing, Artificial Intelligence (AI) and Machine Learning (ML) enables the identification of spatial and temporal patterns that are difficult to detect using conventional approaches. Big Data applications in agronomy include climate and weather prediction, pest and disease management, soil health and nutrient management, irrigation scheduling, crop yield prediction, precision agriculture, crop insurance and weed management. Examples from India and other regions demonstrate the application of satellite imagery, UAV-based sensing, IoT systems, crop-growth models and machine-learning algorithms for field-scale and regional agricultural decision making. Big Data analytics can improve resource-use efficiency, support predictive and real-time monitoring, strengthen agricultural risk assessment and facilitate site-specific management. However, effective implementation is constrained by challenges such as high infrastructure and operational costs, data quality and interoperability issues, data privacy and ownership concerns, limited rural digital infrastructure, inadequate technical skills and increasing requirements for data storage and computing. Therefore, the effective integration of Big Data with agronomic knowledge and decision-support systems can contribute to more precise, efficient and sustainable agricultural production while addressing emerging challenges related to climate variability, resource scarcity and food security.","大数据正成为现代农学的重要组成部分，它能够整合、处理和分析大规模、多样化且持续生成的数据集，从而改进农业决策。农业大数据来源于多种渠道，包括遥感卫星、无人机（UAV）、物联网（IoT）传感器、气象站、农业机械、田间观测和历史数据库。将这些数据源与地理信息系统（GIS）、云计算、人工智能（AI）和机器学习（ML）相结合，能够识别传统方法难以发现的空间和时间模式。大数据在农学中的应用包括气候与天气预测、病虫害管理、土壤健康与养分管理、灌溉调度、作物产量预测、精准农业、作物保险和杂草管理。来自印度及其他地区的案例展示了卫星影像、基于无人机的遥感、物联网系统、作物生长模型和机器学习算法在田块尺度和区域农业决策中的应用。大数据分析可以提高资源利用效率，支持预测性和实时监测，加强农业风险评估，并促进因地制宜的管理。然而，有效实施受到诸多挑战的制约，如高昂的基础设施和运营成本、数据质量和互操作性问题、数据隐私和所有权担忧、农村数字基础设施有限、技术技能不足以及数据存储和计算需求不断增加。因此，将大数据与农学知识和决策支持系统有效整合，有助于实现更精准、高效和可持续的农业生产，同时应对气候变异性、资源稀缺和粮食安全等方面的新兴挑战。","Genetics and Molecular Research","2026-10-05T00:00:00Z",76,{"impact":116,"substance":200,"depth":201,"authority":202,"freshness":147,"relevant":21,"comment":203},20,17,13,"系统综述农业大数据技术、应用与挑战，信息密度高，对智慧农业决策有参考价值，但属综述性论文，非突破性成果。",[205],{"name":196,"url":193},[26,27,207,123,208],"农业大数据","遥感监测",{"code":77,"slug":127,"name":128,"path":129,"parent_code":36},[211,212],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":214},{"candidates":215,"agricultural_evidence":51},[216,222,226,230,235,239,246,253,257,262,268],{"code":77,"score":13,"supported":51,"title":217,"abstract":218,"tags":221},[],[219,220,81],"机器学习","machine learning",[27],{"code":39,"score":20,"supported":51,"title":223,"abstract":224,"tags":225},[],[65,66,67],[],{"code":32,"score":20,"supported":51,"title":227,"abstract":228,"tags":229},[],[28,59,60],[],{"code":50,"score":147,"supported":51,"title":231,"abstract":232,"tags":234},[],[123,233],"precision agriculture",[26,123],{"code":70,"score":71,"supported":51,"title":236,"abstract":237,"tags":238},[],[29,74],[208],{"code":240,"score":104,"supported":51,"title":241,"abstract":242,"tags":245},"A8",[],[243,244],"粮食安全","food security",[],{"code":247,"score":104,"supported":51,"title":248,"abstract":249,"tags":252},"H2",[],[250,251],"土壤","soil",[],{"code":152,"score":104,"supported":51,"title":254,"abstract":255,"tags":256},[],[155,156],[],{"code":258,"score":78,"supported":51,"title":259,"abstract":260,"tags":261},"I6",[],[207],[207],{"code":263,"score":160,"supported":51,"title":264,"abstract":265,"tags":267},"F3",[],[266],"养分管理",[],{"code":159,"score":160,"supported":51,"title":269,"abstract":270,"tags":272},[],[271],"农业机械",[],[274,275],"农业大数据 遥感 物联网","机器学习 作物产量预测","农业大数据遥感物联网-4302","10.4238\u002Fk6hcgw77",{"doi":277,"openalex_id":279,"authors":280,"venue":196,"cited_by_count":93,"oa_url":193,"card":291,"direction":100,"ingested_from":101},"W7220374836",[281,283,285,287,289],{"name":282,"orcid":9},"Kodavali Aswini",{"name":284,"orcid":9},"Raju Marimuthu",{"name":286,"orcid":9},"Guru Gurusamy",{"name":288,"orcid":9},"Kannan Pandian",{"name":290,"orcid":9},"Anandham Rangasamy",{"tldr":292,"method":293,"finding":294,"direction":295,"opportunity":296},"综述农学大数据的技术、应用、效益与挑战，强调多源数据融合支撑精准农业决策。","整合遥感、无人机、物联网、GIS、云计算与AI\u002FML的多源农业数据分析。","大数据可提升资源效率与实时监测，但受成本、数据质量、隐私和农村数字基建制约。","农业人工智能与决策模型","可研究低成本边缘计算与数据互操作标准，破解小农户场景下大数据落地瓶颈。","2026-10-06T23:30:11.880721Z",{"id":299,"title":300,"url":301,"summary":302,"summary_zh":303,"content":9,"source_name":304,"source_url":301,"published_at":305,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":306,"score_detail":307,"sources":309,"tags":311,"primary_topic":314,"secondary_topics":318,"classification":321,"search_phrases":373,"slug":376,"view_count":93,"doi":377,"paper":378,"created_at":402},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",79,{"impact":116,"substance":117,"depth":116,"authority":17,"freshness":20,"relevant":21,"comment":308},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[310],{"name":304,"url":301},[26,27,29,312,313],"作物水分胁迫","精准灌溉",{"code":152,"slug":156,"name":315,"path":316,"parent_code":317},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[319,320],{"code":77,"slug":127,"name":128,"path":129,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":322},{"candidates":323,"agricultural_evidence":51},[324,328,332,336,342,346,353,360,367],{"code":152,"score":19,"supported":51,"title":325,"abstract":326,"tags":327},[156],[155,156],[313],{"code":77,"score":13,"supported":51,"title":329,"abstract":330,"tags":331},[],[219,220,81],[27],{"code":32,"score":20,"supported":51,"title":333,"abstract":334,"tags":335},[],[28,59,60],[],{"code":50,"score":71,"supported":51,"title":337,"abstract":338,"tags":341},[],[339,340],"数字孪生","digital twin",[26],{"code":70,"score":71,"supported":51,"title":343,"abstract":344,"tags":345},[],[29,74],[29],{"code":347,"score":104,"supported":51,"title":348,"abstract":349,"tags":352},"A2",[],[350,351],"小麦","wheat",[],{"code":354,"score":104,"supported":51,"title":355,"abstract":356,"tags":359},"A3",[],[357,358],"玉米","maize",[],{"code":361,"score":104,"supported":51,"title":362,"abstract":363,"tags":366},"A5",[],[364,365],"马铃薯","potato",[],{"code":368,"score":160,"supported":51,"title":369,"abstract":370,"tags":372},"B2",[],[371],"葡萄",[],[374,375],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":377,"openalex_id":379,"authors":380,"venue":304,"cited_by_count":93,"oa_url":301,"card":396,"direction":100,"ingested_from":101},"W7223088921",[381,384,387,390,393],{"name":382,"orcid":383},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":385,"orcid":386},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":388,"orcid":389},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":391,"orcid":392},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":394,"orcid":395},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":397,"method":398,"finding":399,"direction":400,"opportunity":401},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","农业遥感与作物表型","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","2026-10-22T11:20:24.526683Z",{"id":404,"title":405,"url":406,"summary":407,"summary_zh":408,"content":9,"source_name":409,"source_url":406,"published_at":305,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":410,"score_detail":411,"sources":413,"tags":415,"primary_topic":417,"secondary_topics":418,"classification":421,"search_phrases":468,"slug":471,"view_count":93,"doi":472,"paper":473,"created_at":491},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)",80,{"impact":116,"substance":117,"depth":116,"authority":202,"freshness":20,"relevant":21,"comment":412},"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[414],{"name":409,"url":406},[26,27,28,123,416],"气候韧性",{"code":77,"slug":127,"name":128,"path":129,"parent_code":36},[419,420],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":152,"slug":156,"name":315,"path":316,"parent_code":317},{"status":44,"locked":14,"version":45,"method":46,"evidence":422},{"candidates":423,"agricultural_evidence":51},[424,432,436,442,447,454,458,464],{"code":77,"score":116,"supported":51,"title":425,"abstract":426,"tags":431},[81],[427,219,428,220,429,430],"深度学习","计算机视觉","deep learning","computer vision",[27],{"code":50,"score":18,"supported":51,"title":433,"abstract":434,"tags":435},[233],[123,233],[26,123],{"code":32,"score":13,"supported":51,"title":437,"abstract":438,"tags":441},[],[28,439,60,440],"边缘计算","edge computing",[28],{"code":152,"score":20,"supported":51,"title":443,"abstract":444,"tags":446},[],[155,445,156],"水资源",[],{"code":448,"score":71,"supported":51,"title":449,"abstract":450,"tags":453},"H7",[],[451,452],"气候变化","climate change",[416],{"code":247,"score":104,"supported":51,"title":455,"abstract":456,"tags":457},[],[250,251],[],{"code":459,"score":160,"supported":51,"title":460,"abstract":461,"tags":463},"A7",[],[462],"间作",[],{"code":263,"score":160,"supported":51,"title":465,"abstract":466,"tags":467},[],[266],[],[469,470],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":472,"openalex_id":474,"authors":475,"venue":409,"cited_by_count":93,"oa_url":406,"card":486,"direction":100,"ingested_from":101},"W7223121456",[476,479,481,483],{"name":477,"orcid":478},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":480,"orcid":9},"Oluwadare OYEBODE",{"name":482,"orcid":9},"Iretiayo Adelaiye",{"name":484,"orcid":485},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":487,"method":488,"finding":489,"direction":295,"opportunity":490},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":493,"title":494,"url":495,"summary":496,"summary_zh":497,"content":9,"source_name":498,"source_url":495,"published_at":499,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":500,"score_detail":501,"sources":504,"tags":506,"primary_topic":508,"secondary_topics":509,"classification":510,"search_phrases":537,"slug":540,"view_count":93,"doi":541,"paper":542,"created_at":552},5159,"Integration of IoT And Satellite Data for Real-Time Agricultural Crop Stress Monitoring Using Spatio-Temporal Transformers","https:\u002F\u002Fdoi.org\u002F10.31127\u002Ftuje.1871227","This research work presents an integrated framework for real-time crop stress monitoring that combines ground-based Internet of Things (IoT) sensors and multi-temporal satellite remote sensing data. Due to growing environmental uncertainty and limited resources, the early identification of crop stress is crucial for maximizing agricultural productivity. IoT sensors yield localized, real-time measurements of key variables like soil moisture, temperature, and humidity, whereas satellite images yield wide-area vegetation and thermal indicators (e.g., NDVI, EVI, LST). The solution utilizes a Spatio-Temporal Transformer (ST-Transformer) model to integrate these heterogeneous data streams to make early crop disease and irrigation stress detection. By integrating spatial satellite imagery with temporal IoT sequences, STT leverages attention mechanisms to offer a more holistic analysis, which translates to an impressive accuracy of 88% and a solid F1-Score of 0.855. This dual capacity to concurrently handle spatial and temporal dimensions allows STT to effectively generalize across diverse crop stress categories, providing insights that are both timely and actionable","本研究工作提出了一种集成框架，用于结合地面物联网（IoT）传感器与多时相卫星遥感数据实现作物胁迫的实时监测。由于环境不确定性日益加剧且资源有限，作物胁迫的早期识别对于最大化农业生产力至关重要。物联网传感器可提供土壤湿度、温度和湿度等关键变量的局部实时测量，而卫星图像则可提供广域植被与热指标（如NDVI、EVI、LST）。该方案利用时空Transformer（ST-Transformer）模型整合这些异构数据流，以实现作物病害与灌溉胁迫的早期检测。通过将空间卫星影像与时间物联网序列相结合，STT借助注意力机制提供更全面的分析，从而实现了88%的显著准确率和0.855的稳健F1分数。这种同时处理空间与时间维度的双重能力使STT能够有效泛化于多种作物胁迫类别，提供既及时又可操作的洞察。","Turkish Journal of Engineering","2026-10-16T00:00:00Z",75,{"impact":18,"substance":502,"depth":201,"authority":17,"freshness":20,"relevant":21,"comment":503},21,"该研究将物联网与多时相卫星数据融合，用时空Transformer实现作物胁迫早期识别，方法新颖、指标明确，属智慧农业细分领域有实质增量的技术进展，值得入选。",[505],{"name":498,"url":495},[26,27,28,507,208],"作物胁迫",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":44,"locked":14,"version":45,"method":46,"evidence":511},{"candidates":512,"agricultural_evidence":51},[513,517,521,525,529,533],{"code":32,"score":116,"supported":51,"title":514,"abstract":515,"tags":516},[60],[28,59,60],[28],{"code":70,"score":71,"supported":51,"title":518,"abstract":519,"tags":520},[],[29,74],[208],{"code":247,"score":104,"supported":51,"title":522,"abstract":523,"tags":524},[],[250,251],[],{"code":152,"score":104,"supported":51,"title":526,"abstract":527,"tags":528},[],[155,156],[],{"code":50,"score":21,"supported":14,"title":530,"abstract":531,"tags":532},[],[],[26],{"code":77,"score":21,"supported":14,"title":534,"abstract":535,"tags":536},[],[],[27],[538,539],"ST-Transformer 作物胁迫","IoT 卫星 遥感 作物监测","ST-Transformer作物胁迫-5159","10.31127\u002Ftuje.1871227",{"doi":541,"openalex_id":543,"authors":544,"venue":498,"cited_by_count":93,"oa_url":495,"card":547,"direction":100,"ingested_from":101},"W7222730658",[545],{"name":546,"orcid":9},"Sunitha Thankappan",{"tldr":548,"method":549,"finding":550,"direction":100,"opportunity":551},"融合IoT传感器与多时相卫星数据，用时空Transformer实现作物胁迫实时监测。","IoT土壤温湿度数据+卫星NDVI\u002FEVI\u002FLST，构建ST-Transform","模型准确率达88%、F1为0.855，可早期识别病害与灌溉胁迫。","可探索多源异构数据在真实田块尺度上的轻量化部署与跨区域迁移泛化。","2026-10-17T23:30:13.663599Z",{"id":554,"title":555,"url":556,"summary":557,"summary_zh":558,"content":9,"source_name":559,"source_url":556,"published_at":560,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":561,"score_detail":562,"sources":565,"tags":567,"primary_topic":569,"secondary_topics":570,"classification":571,"search_phrases":601,"slug":604,"view_count":160,"doi":605,"paper":606,"created_at":624},4877,"IoT-Enabled Autonomous Agriculture Monitoring Rover for Smart Farming: A Comprehensive Literature Review","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85108","The rapid development of the Internet of Things (IoT), autonomous robotics, artificial intelligence (AI), embedded systems, wireless communication, and precision agriculture has created new opportunities for improving agricultural productivity and resource utilization. Conventional agricultural monitoring relies extensively on manual field inspection, which is time-consuming, labor-intensive, and often unable to provide continuous information about crop and soil conditions. Autonomous agricultural rovers provide an alternative approach by enabling mobile sensing, real-time environmental monitoring, crop observation, navigation, and precision agricultural operations. This paper presents a comprehensive literature review of IoT-enabled autonomous agricultural rovers for smart farming, with particular emphasis on recent developments reported in IEEE literature. Existing research is analyzed according to rover architecture, sensing technologies, navigation techniques, communication technologies, IoT platforms, agricultural applications, AI integration, and autonomy levels. Recent studies have demonstrated the feasibility of ESP32-based IoT rovers, ROS-based agricultural monitoring robots, autonomous navigation systems, precision irrigation platforms, soil-monitoring robots, and AI-enabled agricultural rovers. However, several limitations remain, including restricted autonomous navigation in unstructured environments, limited multi-parameter sensing, communication reliability in rural areas, high computational requirements for AI, energy limitations, and insufficient integration of monitoring with autonomous decision-making. Based on the reviewed literature, a research gap is identified for a low-cost, modular, IoT-enabled autonomous agricultural monitoring rover capable of simultaneously performing soil and environmental sensing, crop monitoring, obstacle avoidance, real-time wireless communication, location-aware operation, and intelligent decision support.","物联网（IoT）、自主机器人、人工智能（AI）、嵌入式系统、无线通信和精准农业的快速发展，为提升农业生产率和资源利用率创造了新的机遇。传统农业监测在很大程度上依赖人工田间巡检，这种方式耗时、费力，且往往无法持续提供作物和土壤状况的信息。自主农业巡田机器人通过实现移动传感、实时环境监测、作物观测、导航和精准农业作业，提供了一种替代方案。本文对面向智慧农业的物联网赋能自主农业巡田机器人进行了全面的文献综述，特别关注IEEE文献中报道的最新进展。现有研究按照巡田机器人架构、传感技术、导航技术、通信技术、物联网平台、农业应用、人工智能集成和自主性水平进行了分析。近期研究已证明基于ESP32的物联网巡田机器人、基于ROS的农业监测机器人、自主导航系统、精准灌溉平台、土壤监测机器人和人工智能赋能农业巡田机器人的可行性。然而，仍存在若干局限，包括非结构化环境中的自主导航受限、多参数传感能力有限、农村地区通信可靠性不足、人工智能的高计算需求、能量限制，以及监测与自主决策的集成不充分。基于所综述的文献，本文识别出一个研究空白，即缺乏一种低成本、模块化、物联网赋能的自主农业监测巡田机器人，能够同时执行土壤与环境传感、作物监测、避障、实时无线通信、位置感知作业和智能决策支持。","International Journal for Research in Applied Science and Engineering Technology","2026-10-13T00:00:00Z",52,{"impact":147,"substance":563,"depth":18,"authority":104,"freshness":147,"relevant":21,"comment":564},14,"综述性论文，系统梳理IoT自主农业巡检机器人架构与瓶颈，但来源期刊影响力有限，属细分领域参考价值。",[566],{"name":559,"url":556},[26,27,568,28,123],"农业机器人",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":44,"locked":14,"version":45,"method":46,"evidence":572},{"candidates":573,"agricultural_evidence":51},[574,578,583,588,592,596],{"code":32,"score":116,"supported":51,"title":575,"abstract":576,"tags":577},[60],[28,59,60],[28],{"code":50,"score":579,"supported":51,"title":580,"abstract":581,"tags":582},11,[],[26,123,233],[26,123],{"code":77,"score":71,"supported":51,"title":584,"abstract":585,"tags":587},[],[81,586],"decision support",[27],{"code":247,"score":104,"supported":51,"title":589,"abstract":590,"tags":591},[],[250,251],[],{"code":152,"score":104,"supported":51,"title":593,"abstract":594,"tags":595},[],[155,156],[],{"code":597,"score":21,"supported":14,"title":598,"abstract":599,"tags":600},"F6",[],[],[568],[602,603],"ESP32 ROS 农业机器人","农业人工智能 农业机器人 智慧农业 精准农业","ESP32ROS农业机器人-4877","10.22214\u002Fijraset.2026.85108",{"doi":605,"openalex_id":607,"authors":608,"venue":559,"cited_by_count":93,"oa_url":556,"card":619,"direction":100,"ingested_from":101},"W7221682750",[609,611,613,615,617],{"name":610,"orcid":9},"Chaitra A S",{"name":612,"orcid":9},"Venugopal Gowda V",{"name":614,"orcid":9},"Vinay H",{"name":616,"orcid":9},"Jeevan T",{"name":618,"orcid":9},"Puneeth M",{"tldr":620,"method":621,"finding":622,"direction":100,"opportunity":623},"综述了物联网赋能的自主农业监测机器人，分析架构、传感、导航与AI集成，指出低成本模块化系统的研究空白","文献综述，聚焦IEEE近期研究，按架构、传感、导航、通信、AI与自主等级分类分析","现有系统在非结构化导航、多参数传感、农村通信、AI算力与能耗上存在局限，缺乏监测与决策一体化。","可研发低成本模块化IoT自主巡田机器人，融合多传感、避障、实时通信与智能决策，填补集成空白。","2026-10-14T23:30:08.717386Z"]