[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4656":3,"related-4656":133},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":50,"search_phrases":105,"slug":108,"view_count":109,"doi":110,"paper":111,"created_at":132},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。",null,"Intelligent Agriculture","2026-10-10T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,20,18,13,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","农业大模型","数字孪生","气候韧性农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[39,45],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,67,75,82,90,98],{"code":33,"score":57,"supported":58,"title":59,"abstract":61,"tags":66},19,true,[60],"artificial intelligence",[62,63,64,65,60],"深度学习","机器学习","machine learning","deep learning",[28,29],{"code":68,"score":69,"supported":58,"title":70,"abstract":71,"tags":74},"I1",11,[],[72,30,73],"精准农业","precision agriculture",[30,27],{"code":40,"score":13,"supported":58,"title":76,"abstract":77,"tags":81},[],[78,79,80],"气候变化","气候适应","climate change",[31],{"code":46,"score":83,"supported":58,"title":84,"abstract":85,"tags":89},9,[],[86,87,88],"物联网","internet of things","IoT",[],{"code":91,"score":92,"supported":58,"title":93,"abstract":94,"tags":97},"I3",6,[],[95,96],"遥感","remote sensing",[],{"code":99,"score":100,"supported":58,"title":101,"abstract":102,"tags":104},"A7",3,[],[103],"间作",[],[106,107],"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",2,"10.54963\u002Fia.v2i2.103039",{"doi":110,"openalex_id":112,"authors":113,"venue":10,"cited_by_count":123,"oa_url":6,"card":124,"direction":130,"ingested_from":131},"W7221321372",[114,117,120],{"name":115,"orcid":116},"Krishan Kumar","https:\u002F\u002Forcid.org\u002F0009-0005-7569-4590",{"name":118,"orcid":119},"Annu Khatri","https:\u002F\u002Forcid.org\u002F0009-0000-3103-9520",{"name":121,"orcid":122},"Indu Shekhar Thakur","https:\u002F\u002Forcid.org\u002F0000-0003-2382-1218",0,{"tldr":125,"method":126,"finding":127,"direction":128,"opportunity":129},"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。","农业人工智能与决策模型","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","openalex","2026-10-11T23:30:07.465569Z",{"total":92,"page":22,"page_size":92,"items":134},[135,220,278,352,455,536],{"id":136,"title":137,"url":138,"summary":139,"summary_zh":140,"content":9,"source_name":141,"source_url":138,"published_at":142,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":143,"sources":146,"tags":148,"primary_topic":150,"secondary_topics":151,"classification":154,"search_phrases":201,"slug":204,"view_count":109,"doi":205,"paper":206,"created_at":219},4213,"Agri digital twin–driven intelligent computing framework for real-time crop yield forecasting and sustainable decision support","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.116418","In the context of climate change, resource constraints, and increasing food demand, sustainable agricultural systems must become more productive and efficient in their use of resources. The Internet of Things (IoT) sensing and Artificial Intelligence (AI) technologies are allowing data-driven agriculture, but a common platform for real-time prediction of crop yield and sustainable agricultural decision-making is still not available. We propose an Agri Digital Twin (ADT) framework that combines a Gated Recurrent Unit (GRU) model with an IoT-based sensing system for temporal crop yield prediction and climate-resilient farm management. The framework constantly updates soil and weather parameters in real time, such as soil moisture, air temperature, humidity, atmospheric pressure, wind speed, wind gust, and wind direction, to create a real-time digital model of the crop field. The data collected is used to derive temporal features to predict yield, which are then categorized as low, medium, and high, and then further into sustainable, warning, and unsustainable to enable irrigation scheduling and crop stress management. The experimental results show that the proposed framework outperforms the others by achieving the Mean Absolute Error (MAE) of 0.0243, Root Mean Square Error (RMSE) of 0.0459, and Coefficient of Determination ( R 2 ) of 0.9254. Furthermore, it achieves an average prediction accuracy of 97.41% (95% CI: 95.03%, 99.80%), representing improvements of 4.09%, 6.32%, and 8.66% over BiLSTM, LSTM, and 1D-CNN, respectively ( p \u003C 0.05 ). The decision-support module also achieves a classification accuracy of 94.03%, showing its potential for sustainable and climate-resilient smart agriculture.","在气候变化、资源约束和粮食需求不断增长的背景下，可持续农业系统必须提高生产力并更高效地利用资源。物联网（IoT）传感与人工智能（AI）技术正在推动数据驱动型农业发展，但目前仍缺乏一个用于作物产量实时预测和可持续农业决策的通用平台。我们提出了一种农业数字孪生（Agri Digital Twin, ADT）框架，该框架将门控循环单元（Gated Recurrent Unit, GRU）模型与基于物联网的传感系统相结合，用于时序作物产量预测和气候适应型农场管理。该框架实时持续更新土壤和气象参数，如土壤湿度、气温、湿度、大气压、风速、阵风和风向，从而构建作物田地的实时数字模型。所采集的数据用于提取时序特征以预测产量，随后将产量划分为低、中、高，并进一步划分为可持续、警告和不可持续，以实现灌溉调度和作物胁迫管理。实验结果表明，所提框架优于其他方法，其平均绝对误差（MAE）为0.0243，均方根误差（RMSE）为0.0459，决定系数（R²）为0.9254。此外，其平均预测准确率达到97.41%（95% CI：95.03%，99.80%），相较于BiLSTM、LSTM和1D-CNN分别提升了4.09%、6.32%和8.66%（p \u003C 0.05）。决策支持模块的分类准确率也达到94.03%，显示出其在可持续且气候适应型智慧农业中的应用潜力。","Engineering Applications of Artificial Intelligence","2026-10-04T00:00:00Z",{"impact":19,"substance":17,"depth":19,"authority":144,"freshness":83,"relevant":22,"comment":145},14,"提出农业数字孪生与GRU融合的实时产量预测与决策支持框架，方法新颖、指标扎实，对智慧农业研究有参考价值。",[147],{"name":141,"url":138},[27,28,86,30,149],"作物产量预测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[152,153],{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":155},{"candidates":156,"agricultural_evidence":58},[157,163,169,173,177,184,191,197],{"code":68,"score":57,"supported":58,"title":158,"abstract":160,"tags":162},[159],"digital twin",[27,30,161,159],"smart agriculture",[30,27],{"code":33,"score":164,"supported":58,"title":165,"abstract":167,"tags":168},12,[166],"decision support",[60],[28],{"code":46,"score":13,"supported":58,"title":170,"abstract":171,"tags":172},[],[86,87,88],[86],{"code":40,"score":83,"supported":58,"title":174,"abstract":175,"tags":176},[],[78,79,80],[],{"code":178,"score":92,"supported":58,"title":179,"abstract":180,"tags":183},"H2",[],[181,182],"土壤","soil",[],{"code":185,"score":92,"supported":58,"title":186,"abstract":187,"tags":190},"H3",[],[188,189],"灌溉","irrigation",[],{"code":192,"score":100,"supported":58,"title":193,"abstract":194,"tags":196},"H8",[],[195],"可持续农业",[],{"code":91,"score":22,"supported":14,"title":198,"abstract":199,"tags":200},[],[],[149],[202,203],"Agri Digital Twin GRU 产量预测","物联网 数字孪生 智慧农业","AgriDigitalTwinGRU产量预测-4213","10.1016\u002Fj.engappai.2026.116418",{"doi":205,"openalex_id":207,"authors":208,"venue":141,"cited_by_count":123,"oa_url":138,"card":214,"direction":130,"ingested_from":131},"W7219849006",[209,212],{"name":210,"orcid":211},"Rohit Kumar","https:\u002F\u002Forcid.org\u002F0009-0000-8484-6588",{"name":213,"orcid":9},"Munish Bhatia",{"tldr":215,"method":216,"finding":217,"direction":130,"opportunity":218},"提出农业数字孪生框架，结合GRU与物联网实现实时产量预测与可持续决策支持。","基于物联网传感实时采集土壤气象数据，用GRU模型做时序产量预测与分类。","预测MAE 0.0243、R² 0.9254，平均准确率97.41%，优于BiLSTM、LSTM和1","可探索多源遥感与数字孪生融合、跨区域迁移学习及决策闭环验证等空白。","2026-10-05T23:30:09.119891Z",{"id":221,"title":222,"url":223,"summary":224,"summary_zh":225,"content":9,"source_name":226,"source_url":223,"published_at":227,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":228,"score_detail":229,"sources":231,"tags":233,"primary_topic":234,"secondary_topics":235,"classification":238,"search_phrases":260,"slug":263,"view_count":109,"doi":264,"paper":265,"created_at":277},4058,"Sustainability intelligence: Integrating Artificial Intelligence, the Internet of Things, and Digital Twins for resource-efficient and climate-resilient systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.nxsust.2026.100535","Sustainability has become one of the defining scientific and societal challenges of the twenty-first century. Climate change, rapid urbanization, population growth, increasing energy demand, and declining natural resources require decision-making that is continuous, adaptive, and supported by reliable environmental intelligence. Conventional monitoring systems, which often rely on periodic measurements and isolated analytical tools, are no longer sufficient for managing highly dynamic environmental systems. Recent advances in Artificial Intelligence (AI), the Internet of Things (IoT), edge computing, and Digital Twins provide unprecedented opportunities to transform sustainability management from reactive monitoring into intelligent, predictive, and autonomous decision-making. IoT enables continuous environmental sensing, AI extracts knowledge from large heterogeneous datasets, edge intelligence supports low-latency analytics, while Digital Twins create dynamic virtual representations of physical systems capable of simulation and optimization. This critical integrative review examines recent developments in AI-enabled sustainability across smart energy systems, intelligent water management, precision agriculture, transportation, smart buildings, circular economy, and climate resilience. Rather than reviewing these technologies independently, the paper analyzes their interactions within an integrated digital ecosystem and discusses emerging topics including Green AI, carbon-aware computing, explainable AI, federated learning, cybersecurity, and sustainability governance. The review method combines a structured multi-database search with targeted evidence and standards updates through June 2026. Building upon the reviewed literature, the paper introduces the Sustainability Intelligence Framework (SIF), a conceptual seven-layer architecture integrating IoT sensing, edge intelligence, AI analytics, Digital Twins, decision intelligence, and sustainability assessment within a continuous feedback cycle. The proposed framework provides a systems-level perspective for designing adaptive, resilient, and resource-efficient infrastructures capable of supporting future sustainable development. The synthesis identifies continuous, auditable sustainability assessment (Layer 7), rather than the underlying six-layer IoT\u002Fcyber–physical stack, as the framework’s distinct contribution. It also shows that reported benefits remain difficult to compare because field validation, system boundaries, rebound effects, and lifecycle digital burdens are inconsistently reported.","可持续性已成为二十一世纪具有决定性意义的科学与社会挑战之一。气候变化、快速城市化、人口增长、能源需求上升以及自然资源减少，要求决策必须持续、自适应，并由可靠的环境智能提供支撑。传统监测系统往往依赖周期性测量和孤立分析工具，已不足以管理高度动态的环境系统。人工智能（AI）、物联网（IoT）、边缘计算和数字孪生（Digital Twins）的最新进展，为将可持续性管理从被动监测转变为智能、预测性和自主决策提供了前所未有的机遇。物联网能够实现持续环境感知，人工智能可从大规模异构数据集中提取知识，边缘智能支持低时延分析，而数字孪生则创建物理系统的动态虚拟表示，以支持仿真与优化。这篇批判性综合综述考察了AI赋能可持续性在智能能源系统、智能水管理、精准农业、交通、智能建筑、循环经济和气候韧性等领域的最新进展。本文并未孤立地评述这些技术，而是分析它们在集成数字生态系统中的相互作用，并讨论绿色AI、碳感知计算、可解释AI、联邦学习、网络安全和可持续性治理等新兴议题。综述方法结合了结构化多数据库检索，并通过截至2026年6月的针对性证据与标准更新加以补充。在所评述文献的基础上，本文提出可持续性智能框架（Sustainability Intelligence Framework, SIF），这是一个概念性七层架构，在持续反馈循环中整合物联网感知、边缘智能、AI分析、数字孪生、决策智能和可持续性评估。所提出的框架为设计能够支持未来可持续发展的自适应、韧性和资源高效型基础设施提供了系统层面视角。该综合指出，框架的独特贡献在于持续、可审计的可持续性评估（第7层），而非底层六层物联网\u002F网络—物理技术栈。研究还表明，由于现场验证、","Next Sustainability","2026-10-02T00:00:00Z",78,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":83,"relevant":22,"comment":230},"该综述提出七层可持续智能框架，系统整合AI、物联网与数字孪生，对智慧农业与气候韧性基础设施具有较高参考价值，但偏概念性、缺乏实证数据。",[232],{"name":226,"url":223},[27,28,86,30,72],{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},[236,237],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":239},{"candidates":240,"agricultural_evidence":58},[241,247,251,255],{"code":46,"score":19,"supported":58,"title":242,"abstract":243,"tags":246},[87],[86,244,87,88,245],"边缘计算","edge computing",[86],{"code":68,"score":164,"supported":58,"title":248,"abstract":249,"tags":250},[],[72,30,73],[30,27,72],{"code":33,"score":164,"supported":58,"title":252,"abstract":253,"tags":254},[60],[60],[28],{"code":40,"score":83,"supported":58,"title":256,"abstract":257,"tags":259},[],[78,80,258],"climate resilience",[],[261,262],"农业人工智能 数字孪生 智慧农业 精准农业","农业人工智能 数字孪生","农业人工智能数字孪生智慧农业精准农业-4058","10.1016\u002Fj.nxsust.2026.100535",{"doi":264,"openalex_id":266,"authors":267,"venue":226,"cited_by_count":123,"oa_url":223,"card":271,"direction":130,"ingested_from":131},"W7216004668",[268],{"name":269,"orcid":270},"Wael Maged Badawy","https:\u002F\u002Forcid.org\u002F0000-0003-0251-9124",{"tldr":272,"method":273,"finding":274,"direction":275,"opportunity":276},"综述AI、物联网与数字孪生融合的可持续性智能，提出七层SIF框架。","多数据库结构化检索，整合IoT、边缘计算、AI与数字孪生文献。","持续可审计的可持续性评估是框架核心贡献，但实地验证仍难比较。","其他","农业场景下SIF七层框架的田间验证与可审计碳\u002F资源评估方法尚属空白。","2026-10-03T23:30:09.950820Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":283,"content":9,"source_name":284,"source_url":281,"published_at":285,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":286,"score_detail":287,"sources":290,"tags":292,"primary_topic":295,"secondary_topics":296,"classification":298,"search_phrases":327,"slug":330,"view_count":92,"doi":331,"paper":332,"created_at":351},1405,"Digital ergonomics and AI-based evaluation in agricultural work systems","https:\u002F\u002Fdoi.org\u002F10.33545\u002F2618060x.2026.v9.i9sa.6457","Agriculture remains one of the most physically demanding occupations, with workers regularly exposed to repetitive movements, awkward postures, manual material handling, vibration, heat stress, and other environmental hazards that results in a high prevalence of work-related musculoskeletal disorders (WMSDs). Conventional ergonomic assessment methods, such as Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), and Ovako Working Posture Assessment System (OWAS), are mainly based on manual observation and are often limited by subjectivity, time consumption, and poor applicability under dynamic field conditions. Recent advances in Agriculture 4.0 have accelerated the integration of digital technologies, enabling objective, continuous, and predictive ergonomic evaluation. This review synthesizes recent developments in digital ergonomics and artificial intelligence (AI)-based ergonomic assessment for agricultural work systems. It examines the application of wearable sensors, Internet of Things (IoT) platforms, computer vision, motion capture systems, Digital Human Modeling (DHM), digital twins, robotics, and machine learning techniques for real-time monitoring of worker posture, physiological responses, and environmental exposures. The review further discusses AI-driven approaches, including machine learning, deep learning, and decision-support systems, for automated posture recognition, ergonomic risk classification, and early prediction of WMSDs. Practical applications in harvesting, planting, weeding, manual material handling, and tractor operation are highlighted, demonstrating the potential of digital technologies to enhance worker safety, improve ergonomic design, and increase operational efficiency. Current challenges, such as technical limitations, data availability, worker acceptance, economic constraints, and the lack of long-term field validation, are also critically discussed. Future research should focus on field validation with robustness, integration of multiple sensors, lightweight AI models, human-centered system design, and intelligent decision-support frameworks to enable the transition toward predictive, data-driven occupational health management in smart agriculture.","农业仍然是体力要求最高的职业之一，劳动者经常暴露于重复性动作、不良姿势、人工物料搬运、振动、热应激及其他环境危害之中，导致职业性肌肉骨骼疾患（WMSDs）的高发。传统的工效学评估方法，如快速上肢评估（RULA）、快速全身评估（REBA）和奥瓦科工作姿势评估系统（OWAS），主要依赖人工观察，常受主观性、耗时性以及在动态田间条件下适用性差等因素的限制。农业4.0的最新进展加速了数字技术的融合，使客观、连续和预测性的工效学评估成为可能。本综述综合了数字工效学和基于人工智能（AI）的工效学评估在农业作业系统中的最新发展。文章考察了可穿戴传感器、物联网（IoT）平台、计算机视觉、动作捕捉系统、数字人体建模（DHM）、数字孪生、机器人技术及机器学习技术在劳动者姿势、生理反应和环境暴露实时监测中的应用。综述进一步讨论了包括机器学习、深度学习和决策支持系统在内的AI驱动方法，用于自动化姿势识别、工效学风险分级及WMSDs的早期预测。文中重点介绍了这些技术在收获、种植、除草、人工物料搬运和拖拉机操作中的实际应用，展示了数字技术在增强劳动者安全、改进工效学设计和提高作业效率方面的潜力。当前面临的挑战，如技术局限性、数据可用性、劳动者接受度、经济约束以及缺乏长期田间验证等问题，也进行了批判性讨论。未来研究应聚焦于具有鲁棒性的田间验证、多传感器集成、轻量化AI模型、以人为本的系统设计以及智能决策支持框架，以推动智能农业向预测性、数据驱动的职业健康管理转型。","International Journal of Research in Agronomy","2026-09-01T00:00:00Z",75,{"impact":19,"substance":18,"depth":19,"authority":164,"freshness":288,"relevant":22,"comment":289},7,"系统综述AI与数字技术在农业人机工程评估中的应用，具前瞻性与实用价值。",[291],{"name":284,"url":281},[27,28,30,293,294],"人机工程","农业安全",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[297],{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":299},{"candidates":300,"agricultural_evidence":58},[301,307,311,315,321],{"code":33,"score":13,"supported":58,"title":302,"abstract":303,"tags":306},[],[62,63,304,64,65,60,305],"计算机视觉","computer vision",[28],{"code":46,"score":83,"supported":58,"title":308,"abstract":309,"tags":310},[],[86,87,88],[],{"code":68,"score":21,"supported":58,"title":312,"abstract":313,"tags":314},[],[30,161],[30,27],{"code":316,"score":100,"supported":58,"title":317,"abstract":318,"tags":320},"F4",[],[319],"除草",[],{"code":322,"score":100,"supported":58,"title":323,"abstract":324,"tags":326},"F5",[],[325],"tractor",[],[328,329],"农业人工智能 人机工程 农业安全 数字孪生","农业人工智能 人机工程","农业人工智能人机工程农业安全数字孪生-1405","10.33545\u002F2618060x.2026.v9.i9sa.6457",{"doi":331,"openalex_id":333,"authors":334,"venue":284,"cited_by_count":123,"oa_url":345,"card":346,"direction":130,"ingested_from":131},"W7204885056",[335,337,339,341,343],{"name":336,"orcid":9},"Raviraj Jegarkal",{"name":338,"orcid":9},"Sunil Shirwal",{"name":340,"orcid":9},"Anusha S",{"name":342,"orcid":9},"Bhagyashree",{"name":344,"orcid":9},"Raghavendra V","https:\u002F\u002Fwww.agronomyjournals.com\u002Farchives\u002F2026\u002Fvol9issue9S\u002FPartA\u002FS-9-8-77-143.pdf",{"tldr":347,"method":348,"finding":349,"direction":130,"opportunity":350},"综述数字技术与AI在农业工效学评估中的应用，旨在减少肌肉骨骼损伤。","综述可穿戴传感器、IoT、计算机视觉、数字孪生及机器学习等AI技术。","数字工效学可实现客观、连续、预测性评估，但面临技术、数据、接受度等挑战。","未来可研究多传感器融合与轻量级AI模型，实现田间长期验证和智能决策支持系统。","2026-09-02T23:30:10.938110Z",{"id":353,"title":354,"url":355,"summary":356,"summary_zh":357,"content":9,"source_name":358,"source_url":355,"published_at":359,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":360,"score_detail":361,"sources":363,"tags":365,"primary_topic":368,"secondary_topics":371,"classification":374,"search_phrases":425,"slug":428,"view_count":123,"doi":429,"paper":430,"created_at":454},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":19,"substance":17,"depth":19,"authority":164,"freshness":83,"relevant":22,"comment":362},"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。",[364],{"name":358,"url":355},[27,28,95,366,367],"作物水分胁迫","精准灌溉",{"code":185,"slug":189,"name":369,"path":370,"parent_code":44},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[372,373],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":375},{"candidates":376,"agricultural_evidence":58},[377,382,386,390,394,398,405,412,419],{"code":185,"score":378,"supported":58,"title":379,"abstract":380,"tags":381},15,[189],[188,189],[367],{"code":33,"score":13,"supported":58,"title":383,"abstract":384,"tags":385},[],[63,64,60],[28],{"code":46,"score":83,"supported":58,"title":387,"abstract":388,"tags":389},[],[86,87,88],[],{"code":68,"score":288,"supported":58,"title":391,"abstract":392,"tags":393},[],[30,159],[27],{"code":91,"score":288,"supported":58,"title":395,"abstract":396,"tags":397},[],[95,96],[95],{"code":399,"score":92,"supported":58,"title":400,"abstract":401,"tags":404},"A2",[],[402,403],"小麦","wheat",[],{"code":406,"score":92,"supported":58,"title":407,"abstract":408,"tags":411},"A3",[],[409,410],"玉米","maize",[],{"code":413,"score":92,"supported":58,"title":414,"abstract":415,"tags":418},"A5",[],[416,417],"马铃薯","potato",[],{"code":420,"score":100,"supported":58,"title":421,"abstract":422,"tags":424},"B2",[],[423],"葡萄",[],[426,427],"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":429,"openalex_id":431,"authors":432,"venue":358,"cited_by_count":123,"oa_url":355,"card":448,"direction":130,"ingested_from":131},"W7223088921",[433,436,439,442,445],{"name":434,"orcid":435},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":437,"orcid":438},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":440,"orcid":441},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":443,"orcid":444},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":446,"orcid":447},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":449,"method":450,"finding":451,"direction":452,"opportunity":453},"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":456,"title":457,"url":458,"summary":459,"summary_zh":460,"content":9,"source_name":461,"source_url":458,"published_at":359,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":462,"score_detail":463,"sources":465,"tags":467,"primary_topic":469,"secondary_topics":470,"classification":473,"search_phrases":512,"slug":515,"view_count":123,"doi":516,"paper":517,"created_at":535},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":19,"substance":17,"depth":19,"authority":20,"freshness":83,"relevant":22,"comment":464},"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。",[466],{"name":461,"url":458},[27,28,86,72,468],"气候韧性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[471,472],{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},{"code":185,"slug":189,"name":369,"path":370,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":474},{"candidates":475,"agricultural_evidence":58},[476,480,485,489,494,498,502,506],{"code":33,"score":19,"supported":58,"title":477,"abstract":478,"tags":479},[60],[62,63,304,64,65,305],[28],{"code":68,"score":481,"supported":58,"title":482,"abstract":483,"tags":484},16,[73],[72,73],[27,72],{"code":46,"score":13,"supported":58,"title":486,"abstract":487,"tags":488},[],[86,244,88,245],[86],{"code":185,"score":83,"supported":58,"title":490,"abstract":491,"tags":493},[],[188,492,189],"水资源",[],{"code":40,"score":288,"supported":58,"title":495,"abstract":496,"tags":497},[],[78,80],[468],{"code":178,"score":92,"supported":58,"title":499,"abstract":500,"tags":501},[],[181,182],[],{"code":99,"score":100,"supported":58,"title":503,"abstract":504,"tags":505},[],[103],[],{"code":507,"score":100,"supported":58,"title":508,"abstract":509,"tags":511},"F3",[],[510],"养分管理",[],[513,514],"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":516,"openalex_id":518,"authors":519,"venue":461,"cited_by_count":123,"oa_url":458,"card":530,"direction":130,"ingested_from":131},"W7223121456",[520,523,525,527],{"name":521,"orcid":522},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":524,"orcid":9},"Oluwadare OYEBODE",{"name":526,"orcid":9},"Iretiayo Adelaiye",{"name":528,"orcid":529},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":531,"method":532,"finding":533,"direction":128,"opportunity":534},"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":537,"title":538,"url":539,"summary":540,"summary_zh":541,"content":9,"source_name":542,"source_url":539,"published_at":543,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":544,"score_detail":545,"sources":548,"tags":550,"primary_topic":554,"secondary_topics":555,"classification":557,"search_phrases":573,"slug":576,"view_count":123,"doi":577,"paper":578,"created_at":598},5376,"Agri-Synergy: A Hardware–Software Co-Designed Hybrid IoT System for Sensor Interface Standardization and LLM-Driven Interactive Decision Support","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8100446","This paper presents Agri-Synergy, a hybrid Internet of Things (IoT) system featuring a hardware–software co-design to tackle sensor data heterogeneity and the barriers to effective AI-driven decision support in smart agriculture. Unlike software-based data harmonization, we propose a universal sensor printed circuit board (PCB) with RS485\u002FModbus interface for hardware unification, complemented by on-node edge processing that ensures standardized data from source. To lower the entry barrier for farmers, the system integrates the DeepSeek large language model (LLM) API, combining static agricultural knowledge with real-time sensor data, for context-aware, natural language-based decision support, functioning without local model deployment. Field deployment showed reference-verified sensor accuracy predominantly within manufacturer specifications, stable 4G connectivity during the 4-h benchmark but intermittent dropouts during the 20-day deployment, and remote-control success rates of 96% (normal) and 80% (weak signal at 300 m). Multi-node concurrent transmission resulted in average latency of 238.0 ± 27.1 ms and packet loss below 0.5%. The system achieves a 3–5× cost reduction compared to commercial platforms. These results demonstrate that addressing data heterogeneity from source through hardware unification and software edge processing, while leveraging AI to lower the barrier to system adoption, provides a more reliable foundation for data synchronization and accessible AI-driven analytics.","本文提出Agri-Synergy，一种采用软硬件协同设计的混合物联网（IoT）系统，旨在解决智慧农业中传感器数据异构性问题以及有效AI驱动决策支持的障碍。与基于软件的数据协调方式不同，我们提出了一种带有RS485\u002FModbus接口的通用传感器印刷电路板（PCB）以实现硬件统一，并辅以节点端边缘处理，确保从源头获得标准化数据。为降低农民的使用门槛，该系统集成了DeepSeek大语言模型（LLM）API，将静态农业知识与实时传感器数据相结合，提供上下文感知的、基于自然语言的决策支持，且无需本地部署模型即可运行。现场部署表明，经参考验证的传感器精度基本处于制造商规格范围内；在4小时基准测试期间4G连接稳定，但在20天部署期间出现间歇性断连；远程控制成功率分别为96%（正常信号）和80%（300 m处弱信号）。多节点并发传输的平均延迟为238.0 ± 27.1 ms，丢包率低于0.5%。与商业平台相比，该系统实现了3–5倍的成本降低。这些结果表明，通过硬件统一和软件边缘处理从源头解决数据异构性问题，同时利用AI降低系统采用门槛，可为数据同步和可及的AI驱动分析提供更可靠的基础。","AgriEngineering","2026-10-19T00:00:00Z",76,{"impact":481,"substance":546,"depth":19,"authority":164,"freshness":83,"relevant":22,"comment":547},21,"硬件统一接口与边缘处理结合大模型决策支持的农业物联网系统论文，方法新颖、有实地部署数据，但属细分技术进展，公共影响有限。",[549],{"name":542,"url":539},[27,28,551,552,553],"农业物联网","大语言模型","传感器标准化",{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},[556],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":558},{"candidates":559,"agricultural_evidence":58},[560,564,569],{"code":46,"score":19,"supported":58,"title":561,"abstract":562,"tags":563},[88],[86,87,88],[551],{"code":33,"score":481,"supported":58,"title":565,"abstract":566,"tags":568},[166],[567,166],"large language model",[28,552],{"code":68,"score":288,"supported":58,"title":570,"abstract":571,"tags":572},[],[27,161],[27],[574,575],"Agri-Synergy 物联网 传感器","DeepSeek 决策支持","Agri-Synergy物联网传感器-5376","10.3390\u002Fagriengineering8100446",{"doi":577,"openalex_id":579,"authors":580,"venue":542,"cited_by_count":123,"oa_url":539,"card":593,"direction":130,"ingested_from":131},"W7222920424",[581,583,585,587,589,591],{"name":582,"orcid":9},"Leqi Lai",{"name":584,"orcid":9},"Langtao Duan",{"name":586,"orcid":9},"Yuhao Chen",{"name":588,"orcid":9},"Yuhang Zheng",{"name":590,"orcid":9},"Jinyu Yang",{"name":592,"orcid":9},"Mingyao Wen",{"tldr":594,"method":595,"finding":596,"direction":130,"opportunity":597},"提出软硬件协同的混合物联网系统，统一传感器接口并用大模型提供交互式决策支持。","通用RS485\u002FModbus传感器PCB、节点边缘处理、DeepSeek大模型A","传感器精度达标，多节点延迟238ms、丢包低于0.5%，成本降低3-5倍。","可研究弱信号下通信可靠性提升与边缘-云端LLM协同的长期田间鲁棒性。","2026-10-20T23:30:08.319698Z"]