[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5232":3,"related-5232":115},{"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":38,"search_phrases":94,"slug":97,"view_count":98,"doi":99,"paper":100,"created_at":114},5232,"Current applications of artificial intelligence for advancing agriculture, sustainability and climate mitigation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02540-1","Artificial intelligence (AI) is reshaping agriculture by enabling data-driven decision-making across crop production systems and supporting the transition toward more sustainable, resilient, and climate-smart farming. This review critically synthesizes advances in AI applications spanning precision irrigation, nutrient and soil management, yield prediction, pest and disease monitoring, computer vision, autonomous agricultural robotics, and large language model (LLM)-based decision support systems. Beyond summarizing technological developments, the review evaluates their practical deployment, emphasizing that successful agricultural AI depends not only on predictive accuracy but also on model generalizability, explainability, economic feasibility, interoperability, and integration with existing agronomic knowledge. Emerging technologies including Vision Transformers, foundation models, Digital Twins, multimodal AI, Retrieval-Augmented Generation, and federated and continual learning—are discussed as important directions for overcoming limitations related to data scarcity, environmental variability, and scalable field implementation. The review highlights that responsible AI adoption requires transparent governance frameworks addressing data ownership, privacy, algorithmic fairness, cybersecurity, and equitable access, particularly for smallholder farming systems. Although numerous AI models report outstanding performance, widespread agricultural adoption remains constrained by cross-region validation, fragmented datasets, infrastructure requirements, and insufficient real-world deployment. Future progress will depend on multidisciplinary collaboration integrating advances in artificial intelligence, sensing technologies, robotics, crop science, and agricultural engineering to develop robust, trustworthy, and farmer-centered decision-support systems. By critically evaluating technological innovations and implementation challenges, this review provides a framework for understanding the current state of AI in agriculture while identifying research priorities that will shape the next generation of sustainable and climate-resilient agricultural systems.","人工智能（AI）正在重塑农业，通过在作物生产系统中实现数据驱动的决策，并支持向更可持续、更具韧性和气候智能型农业转型。本综述批判性地综合了AI应用的进展，涵盖精准灌溉、养分与土壤管理、产量预测、病虫害监测、计算机视觉、自主农业机器人以及基于大语言模型（LLM）的决策支持系统。除总结技术发展外，本综述还评估了其实际部署情况，强调农业AI的成功不仅取决于预测准确性，还取决于模型泛化能力、可解释性、经济可行性、互操作性以及与现有农学知识的整合。文中讨论了包括视觉Transformer、基础模型、数字孪生、多模态AI、检索增强生成以及联邦学习和持续学习在内的新兴技术，将其视为克服数据稀缺、环境变异性和田间可扩展实施等局限的重要方向。本综述强调，负责任的AI采用需要透明的治理框架，以解决数据所有权、隐私、算法公平性、网络安全和公平获取等问题，尤其是对小农耕作系统而言。尽管众多AI模型报告了卓越的性能，但农业领域的广泛采用仍受限于跨区域验证、碎片化数据集、基础设施要求以及现实世界部署不足。未来的进展将取决于多学科协作，整合人工智能、传感技术、机器人技术、作物科学和农业工程领域的进展，以开发稳健、可信且以农民为中心的决策支持系统。通过批判性评估技术创新和实施挑战，本综述提供了一个理解AI在农业中现状的框架，同时确定了将塑造下一代可持续和气候韧性农业系统的研究优先事项。",null,"Discover Artificial Intelligence","2026-10-17T00:00:00Z","论文",10,false,83,{"impact":17,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,18,12,9,1,"该综述系统梳理了AI在农业中的应用与落地瓶颈，覆盖精准灌溉、机器人、大模型决策支持等方向，信息密度高，但属综述类论文，产业级突破性有限，可入精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","大语言模型","数字孪生","气候智慧农业",{"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":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,55,63,70,76,83,89],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":54},19,true,[48],"artificial intelligence",[50,48,51,52,53],"计算机视觉","computer vision","large language model","decision support",[27,28],{"code":56,"score":57,"supported":46,"title":58,"abstract":59,"tags":62},"H2",6,[],[60,61],"土壤","soil",[],{"code":64,"score":57,"supported":46,"title":65,"abstract":66,"tags":69},"H3",[],[67,68],"灌溉","irrigation",[],{"code":71,"score":72,"supported":46,"title":73,"abstract":74,"tags":75},"I1",5,[],[29],[29,26],{"code":77,"score":78,"supported":46,"title":79,"abstract":80,"tags":82},"F6",3,[],[81],"农业机器人",[],{"code":84,"score":78,"supported":46,"title":85,"abstract":86,"tags":88},"G2",[],[87],"smallholder",[],{"code":90,"score":21,"supported":14,"title":91,"abstract":92,"tags":93},"H7",[],[],[30],[95,96],"气候智慧农业 AI","农业人工智能 气候智慧农业 大语言模型 数字孪生","气候智慧农业AI-5232",0,"10.1007\u002Fs44163-026-02540-1",{"doi":99,"openalex_id":101,"authors":102,"venue":10,"cited_by_count":98,"oa_url":6,"card":106,"direction":112,"ingested_from":113},"W7222823256",[103],{"name":104,"orcid":105},"Mostafa Farajpour","https:\u002F\u002Forcid.org\u002F0000-0003-4223-502X",{"tldr":107,"method":108,"finding":109,"direction":110,"opportunity":111},"综述农业人工智能在精准灌溉、产量预测、病虫害监测等应用及落地挑战。","文献综述，覆盖视觉Transformer、基础模型、数字孪生、多模态AI与联邦学","农业AI落地受限于跨区域验证、数据碎片化与基础设施，需兼顾可解释性与公平治理。","农业人工智能与决策模型","可研究小农户场景下跨区域可泛化、可解释且低成本的农业大模型与联邦学习部署。","智慧农业 \u002F 农业物联网","openalex","2026-10-18T23:30:07.666127Z",{"total":57,"page":21,"page_size":57,"items":116},[117,188,243,333,408,471],{"id":118,"title":119,"url":120,"summary":121,"summary_zh":122,"content":9,"source_name":123,"source_url":120,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":130,"tags":132,"primary_topic":135,"secondary_topics":140,"classification":142,"search_phrases":162,"slug":165,"view_count":98,"doi":166,"paper":167,"created_at":187},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":127,"substance":128,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":129},16,21,"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。",[131],{"name":123,"url":120},[26,27,133,28,134],"农业物联网","传感器标准化",{"code":136,"slug":137,"name":138,"path":139,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[141],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":143},{"candidates":144,"agricultural_evidence":46},[145,152,156],{"code":136,"score":18,"supported":46,"title":146,"abstract":148,"tags":151},[147],"IoT",[149,150,147],"物联网","internet of things",[133],{"code":32,"score":127,"supported":46,"title":153,"abstract":154,"tags":155},[53],[52,53],[27,28],{"code":71,"score":157,"supported":46,"title":158,"abstract":159,"tags":161},7,[],[26,160],"smart agriculture",[26],[163,164],"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":166,"openalex_id":168,"authors":169,"venue":123,"cited_by_count":98,"oa_url":120,"card":182,"direction":112,"ingested_from":113},"W7222920424",[170,172,174,176,178,180],{"name":171,"orcid":9},"Leqi Lai",{"name":173,"orcid":9},"Langtao Duan",{"name":175,"orcid":9},"Yuhao Chen",{"name":177,"orcid":9},"Yuhang Zheng",{"name":179,"orcid":9},"Jinyu Yang",{"name":181,"orcid":9},"Mingyao Wen",{"tldr":183,"method":184,"finding":185,"direction":112,"opportunity":186},"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",{"id":189,"title":190,"url":191,"summary":192,"summary_zh":193,"content":9,"source_name":194,"source_url":191,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":196,"sources":199,"tags":201,"primary_topic":204,"secondary_topics":205,"classification":206,"search_phrases":226,"slug":229,"view_count":98,"doi":230,"paper":231,"created_at":242},5315,"PLANT-GPK v4.0: Multi-Stage Plant Phenotyping with Time-Discovery and Harvest Prediction","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23461121","This paper presents a comprehensive extension to the PLANT-GPK framework, demonstrating its robust capability to analyze plants across entirely different developmental stages from single images. By formulating an inverse problem that simultaneously optimizes physiological parameters and the hidden time dimension (t), the model successfully discovered the exact age of two distinct wheat samples: a vegetative (green) stage and a senescent (golden) stage. The framework bridges computer vision, spatial heterogeneity analysis, and ODE-based physics to provide a complete digital twin capable of forecasting the exact day of harvest regardless of the plant's current state.","本文对PLANT-GPK框架进行了全面扩展，展示了其仅凭单张图像即可分析处于完全不同发育阶段植物的稳健能力。通过构建一个同时优化生理参数与隐藏时间维度（t）的反问题，该模型成功推断出两个不同小麦样本的精确年龄：一个处于营养期（绿色阶段），另一个处于衰老期（金色阶段）。该框架融合了计算机视觉、空间异质性分析以及基于常微分方程（ODE）的物理建模，提供了一个完整的数字孪生，无论植物当前处于何种状态，均能预测确切的收获日期。","Zenodo (CERN European Organization for Nuclear Research)",81,{"impact":18,"substance":17,"depth":18,"authority":197,"freshness":13,"relevant":21,"comment":198},13,"该研究提出从单张图像反演植物生育阶段与收获日的数字孪生框架，方法新颖且具产业应用潜力，值得进入每日精选。",[200],{"name":194,"url":191},[26,27,29,202,203],"植物表型","收获预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":207},{"candidates":208,"agricultural_evidence":46},[209,215,219],{"code":71,"score":210,"supported":46,"title":211,"abstract":212,"tags":214},8,[],[29,213],"digital twin",[29,26],{"code":32,"score":157,"supported":46,"title":216,"abstract":217,"tags":218},[],[50,51],[27],{"code":220,"score":57,"supported":46,"title":221,"abstract":222,"tags":225},"A2",[],[223,224],"小麦","wheat",[],[227,228],"PLANT-GPK 植物表型","小麦 收获期 预测","PLANT-GPK植物表型-5315","10.5281\u002Fzenodo.23461121",{"doi":230,"openalex_id":232,"authors":233,"venue":194,"cited_by_count":98,"oa_url":191,"card":236,"direction":240,"ingested_from":113},"W7222875391",[234],{"name":235,"orcid":9},"MOHAMMED TAHAR SAIM",{"tldr":237,"method":238,"finding":239,"direction":240,"opportunity":241},"扩展PLANT-GPK框架，从单张图像反演植物生理参数与隐藏时间维度，实现跨生育期表型分析与收获期预","基于ODE物理模型与逆问题优化，联合估计生理参数和隐藏时间t，用两个不同生育期小","模型能准确发现两个不同生育期小麦样本的精确年龄，并预测确切收获日期。","农业遥感与作物表型","可将单图时间反演扩展到多作物多生育期，结合时序遥感构建大尺度数字孪生与收获期预报。","2026-10-19T23:30:19.267920Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":9,"source_name":249,"source_url":246,"published_at":250,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":251,"sources":254,"tags":256,"primary_topic":259,"secondary_topics":260,"classification":267,"search_phrases":309,"slug":312,"view_count":313,"doi":314,"paper":315,"created_at":332},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","2026-10-10T00:00:00Z",{"impact":17,"substance":252,"depth":18,"authority":197,"freshness":210,"relevant":21,"comment":253},20,"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。",[255],{"name":249,"url":246},[26,27,257,29,258],"农业大模型","气候韧性农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[261,266],{"code":90,"slug":262,"name":263,"path":264,"parent_code":265},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":136,"slug":137,"name":138,"path":139,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":268},{"candidates":269,"agricultural_evidence":46},[270,278,285,292,296,303],{"code":32,"score":45,"supported":46,"title":271,"abstract":272,"tags":277},[48],[273,274,275,276,48],"深度学习","机器学习","machine learning","deep learning",[27,257],{"code":71,"score":279,"supported":46,"title":280,"abstract":281,"tags":284},11,[],[282,29,283],"精准农业","precision agriculture",[29,26],{"code":90,"score":13,"supported":46,"title":286,"abstract":287,"tags":291},[],[288,289,290],"气候变化","气候适应","climate change",[258],{"code":136,"score":20,"supported":46,"title":293,"abstract":294,"tags":295},[],[149,150,147],[],{"code":297,"score":57,"supported":46,"title":298,"abstract":299,"tags":302},"I3",[],[300,301],"遥感","remote sensing",[],{"code":304,"score":78,"supported":46,"title":305,"abstract":306,"tags":308},"A7",[],[307],"间作",[],[310,311],"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":314,"openalex_id":316,"authors":317,"venue":249,"cited_by_count":98,"oa_url":246,"card":327,"direction":112,"ingested_from":113},"W7221321372",[318,321,324],{"name":319,"orcid":320},"Krishan Kumar","https:\u002F\u002Forcid.org\u002F0009-0005-7569-4590",{"name":322,"orcid":323},"Annu Khatri","https:\u002F\u002Forcid.org\u002F0009-0000-3103-9520",{"name":325,"orcid":326},"Indu Shekhar Thakur","https:\u002F\u002Forcid.org\u002F0000-0003-2382-1218",{"tldr":328,"method":329,"finding":330,"direction":110,"opportunity":331},"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":334,"title":335,"url":336,"summary":337,"summary_zh":338,"content":9,"source_name":339,"source_url":336,"published_at":340,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":341,"sources":344,"tags":346,"primary_topic":348,"secondary_topics":349,"classification":352,"search_phrases":389,"slug":392,"view_count":313,"doi":393,"paper":394,"created_at":407},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":18,"substance":17,"depth":18,"authority":342,"freshness":20,"relevant":21,"comment":343},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。",[345],{"name":339,"url":336},[26,27,149,29,347],"作物产量预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[350,351],{"code":136,"slug":137,"name":138,"path":139,"parent_code":36},{"code":90,"slug":262,"name":263,"path":264,"parent_code":265},{"status":39,"locked":14,"version":40,"method":41,"evidence":353},{"candidates":354,"agricultural_evidence":46},[355,359,363,367,371,375,379,385],{"code":71,"score":45,"supported":46,"title":356,"abstract":357,"tags":358},[213],[26,29,160,213],[29,26],{"code":32,"score":19,"supported":46,"title":360,"abstract":361,"tags":362},[53],[48],[27],{"code":136,"score":13,"supported":46,"title":364,"abstract":365,"tags":366},[],[149,150,147],[149],{"code":90,"score":20,"supported":46,"title":368,"abstract":369,"tags":370},[],[288,289,290],[],{"code":56,"score":57,"supported":46,"title":372,"abstract":373,"tags":374},[],[60,61],[],{"code":64,"score":57,"supported":46,"title":376,"abstract":377,"tags":378},[],[67,68],[],{"code":380,"score":78,"supported":46,"title":381,"abstract":382,"tags":384},"H8",[],[383],"可持续农业",[],{"code":297,"score":21,"supported":14,"title":386,"abstract":387,"tags":388},[],[],[347],[390,391],"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":393,"openalex_id":395,"authors":396,"venue":339,"cited_by_count":98,"oa_url":336,"card":402,"direction":112,"ingested_from":113},"W7219849006",[397,400],{"name":398,"orcid":399},"Rohit Kumar","https:\u002F\u002Forcid.org\u002F0009-0000-8484-6588",{"name":401,"orcid":9},"Munish Bhatia",{"tldr":403,"method":404,"finding":405,"direction":112,"opportunity":406},"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":409,"title":410,"url":411,"summary":412,"summary_zh":413,"content":9,"source_name":123,"source_url":411,"published_at":414,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":415,"score_detail":416,"sources":418,"tags":420,"primary_topic":421,"secondary_topics":422,"classification":423,"search_phrases":451,"slug":454,"view_count":313,"doi":455,"paper":456,"created_at":470},4063,"An Intelligent LLM–Fuzzy Decision Support and Digital Twin Framework for Adaptive Agricultural Robotics Systems","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8100423","This study develops an integrated LLM–fuzzy decision-support and digital twin framework for adaptive agricultural robotics, combining semantic reasoning, fuzzy inference, multi-objective optimization, and feedback-driven state updating. Numerical sensor data, operational information, and textual field knowledge are transformed into validated decision rules and model-ready parameters, enabling coordinated decisions across economic, operational, crop-stress, and sustainability-related objectives. The framework is evaluated as a simulation-based proof of concept calibrated with real observations from a semi-smart tomato farm, while controlled digital twin scenarios are used to assess its behavior under environmental and operational disturbances. Compared with numerical optimization alone, the complete LLM–fuzzy framework reduces operational cost by 3.35% and crop stress by 7.98%, while improving operational performance by 4.45%. Ablation, repeated-run, scenario, sensitivity, and dynamic-adaptation analyses further demonstrate consistent computational performance under the evaluated conditions. Sustainability is assessed through a model-based measure integrating water use, energy consumption, and soil-compaction-related impacts. These findings demonstrate the potential of combining semantic reasoning, uncertainty processing, optimization, and digital-twin feedback for adaptive agricultural decision support. However, the results represent a calibrated simulation-based proof of concept rather than validation through a full-scale autonomous field deployment; broader multi-season and field-scale validation is therefore required before generalizing the reported operational and sustainability benefits.","本研究开发了一个面向自适应农业机器人（adaptive agricultural robotics）的集成式LLM-模糊决策支持与数字孪生框架，融合了语义推理、模糊推理、多目标优化和反馈驱动的状态更新。数值传感器数据、运营信息和文本田间知识被转化为经过验证的决策规则和模型就绪参数，从而支持在经济、运营、作物胁迫和可持续性相关目标之间进行协调决策。该框架以基于仿真的概念验证形式进行评估，并利用半智能番茄农场的真实观测数据进行校准，同时使用受控数字孪生场景评估其在环境和运营扰动下的行为表现。与仅使用数值优化相比，完整的LLM-模糊框架将运营成本降低了3.35%，作物胁迫降低了7.98%，同时将运营性能提升了4.45%。消融实验、重复运行、场景、敏感性和动态自适应分析进一步表明，在所评估条件下该框架具有一致的计算性能。可持续性通过一个基于模型的度量进行评估，该度量综合了用水量、能源消耗和土壤压实相关影响。这些发现表明，将语义推理、不确定性处理、优化和数字孪生反馈相结合用于自适应农业决策支持具有潜力。然而，这些结果代表的是一种经过校准的基于仿真的概念验证，而非通过全尺度自主田间部署所进行的验证；因此，在将所报告的运营和可持续性效益加以推广之前，仍需开展更广泛的多季节和田间尺度验证。","2026-10-02T00:00:00Z",77,{"impact":127,"substance":17,"depth":18,"authority":197,"freshness":210,"relevant":21,"comment":417},"方法新颖、结论有量化支撑，但仅为仿真验证的概念验证，尚未田间落地，属细分领域前沿进展。",[419],{"name":123,"url":411},[26,27,81,29,282],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":424},{"candidates":425,"agricultural_evidence":46},[426,431,435,442,446],{"code":71,"score":427,"supported":46,"title":428,"abstract":429,"tags":430},17,[213],[29,213],[29,26,282],{"code":32,"score":19,"supported":46,"title":432,"abstract":433,"tags":434},[53],[53],[27],{"code":436,"score":57,"supported":46,"title":437,"abstract":438,"tags":441},"B1",[],[439,440],"番茄","tomato",[],{"code":56,"score":57,"supported":46,"title":443,"abstract":444,"tags":445},[],[60,61],[],{"code":77,"score":447,"supported":46,"title":448,"abstract":449,"tags":450},4,[],[81],[81],[452,453],"LLM 模糊决策 农业机器人","数字孪生 番茄农场","LLM模糊决策农业机器人-4063","10.3390\u002Fagriengineering8100423",{"doi":455,"openalex_id":457,"authors":458,"venue":123,"cited_by_count":98,"oa_url":411,"card":465,"direction":112,"ingested_from":113},"W7216447671",[459,462],{"name":460,"orcid":461},"Hamed Nozari","https:\u002F\u002Forcid.org\u002F0000-0002-6500-6708",{"name":463,"orcid":464},"Zornitsa B. Yordanova","https:\u002F\u002Forcid.org\u002F0000-0002-6056-8445",{"tldr":466,"method":467,"finding":468,"direction":110,"opportunity":469},"提出LLM-模糊决策与数字孪生框架，用于自适应农业机器人协同决策。","LLM语义推理、模糊推理、多目标优化与数字孪生仿真，基于半智能番茄农场数据。","相比纯数值优化，运营成本降3.35%、作物胁迫降7.98%、运营性能升4.45%。","可开展多季节田间规模化验证，并融合实时物联网数据提升数字孪生自适应能力。","2026-10-03T23:30:10.334001Z",{"id":472,"title":473,"url":474,"summary":475,"summary_zh":476,"content":9,"source_name":477,"source_url":474,"published_at":414,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":478,"score_detail":479,"sources":481,"tags":483,"primary_topic":486,"secondary_topics":487,"classification":488,"search_phrases":516,"slug":519,"view_count":78,"doi":520,"paper":521,"created_at":535},4059,"Smart Agriculture Technology: A Review of Robotic Platforms, Sensing Modalities, Artificial Intelligence, Datasets, Evaluation Metrics, and Future Research Directions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16192137","Smart agriculture has moved beyond isolated precision-farming tools toward integrated cyber–physical systems that combine robotics, multimodal sensing, connectivity, artificial intelligence, and data-driven decision support. This review synthesizes recent developments in the field using a systematic critical review approach. It proposes a systems-level framework for analyzing smart agriculture across robotic platforms, sensing and digital infrastructure, AI methods, public datasets and benchmarks, evaluation metrics, application domains, and deployment maturity. The review covers ground robots, UAVs, harvesting manipulators, multi-robot systems, RGB and spectral sensing, LiDAR, IoT networks, edge\u002Fcloud platforms, AI-based perception and decision-making, and multimodal vision–language models. The analysis shows that the literature has made clear progress in prototypes, datasets, and task-specific performance, but several gaps still limit practical deployment. These include weak cross-season and cross-region validation, fragmented benchmarking practices, limited deployment-oriented metrics, insufficient techno-economic reporting, and unresolved challenges related to interoperability and human–robot interaction. Public datasets and emerging multimodal benchmarks show that the field is moving toward greater standardization; however, strong benchmark performance does not yet consistently translate into reliable field operation. The review concludes by outlining future research directions in benchmark realism, field-robust AI, edge deployment, digital twins, privacy-preserving learning, human-centered autonomy, and long-term agronomic and economic validation.","智慧农业已超越孤立的精准农业工具，迈向集机器人、多模态感知、连接性、人工智能和数据驱动决策支持于一体的信息物理系统。本综述采用系统性批判性综述方法，梳理了该领域的最新进展，提出了一个系统级框架，从机器人平台、感知与数字基础设施、人工智能方法、公共数据集与基准、评估指标、应用领域和部署成熟度等维度分析智慧农业。综述涵盖地面机器人、无人机、采摘机械臂、多机器人系统、RGB与光谱感知、激光雷达、物联网网络、边缘\u002F云平台、基于人工智能的感知与决策以及多模态视觉-语言模型。分析表明，文献在原型、数据集和特定任务性能方面已取得明显进展，但若干差距仍制约着实际部署，包括跨季节与跨区域验证薄弱、基准测试实践碎片化、面向部署的指标有限、技术经济报告不足，以及互操作性和人机交互方面的挑战尚未解决。公共数据集和新兴多模态基准表明该领域正朝着更高标准化方向迈进；然而，优异的基准性能尚未能一致地转化为可靠的田间作业。综述最后展望了未来研究方向，包括基准现实性、田间鲁棒人工智能、边缘部署、数字孪生、隐私保护学习、以人为中心的自主性以及长期农艺与经济验证。","Agriculture",80,{"impact":18,"substance":17,"depth":45,"authority":197,"freshness":210,"relevant":21,"comment":480},"系统梳理智慧农业机器人、感知、AI与基准评测的综述，指出跨季节验证与部署指标缺口，对农业信息化研究有较高参考价值。",[482],{"name":477,"url":474},[26,27,81,29,484,485],"多模态感知","农业数据集",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":489},{"candidates":490,"agricultural_evidence":46},[491,495,499,503,507,511],{"code":71,"score":45,"supported":46,"title":492,"abstract":493,"tags":494},[160],[26,282,29,160],[29,26],{"code":32,"score":127,"supported":46,"title":496,"abstract":497,"tags":498},[48],[48,53],[27,484],{"code":136,"score":57,"supported":46,"title":500,"abstract":501,"tags":502},[],[149,147],[],{"code":304,"score":78,"supported":46,"title":504,"abstract":505,"tags":506},[],[307],[],{"code":77,"score":21,"supported":14,"title":508,"abstract":509,"tags":510},[],[],[81],{"code":512,"score":21,"supported":14,"title":513,"abstract":514,"tags":515},"I6",[],[],[485],[517,518],"农业机器人 多模态感知 评测基准","智慧农业 数字孪生 边缘部署","农业机器人多模态感知评测基准-4059","10.3390\u002Fagriculture16192137",{"doi":520,"openalex_id":522,"authors":523,"venue":477,"cited_by_count":98,"oa_url":474,"card":530,"direction":112,"ingested_from":113},"W7216116945",[524,527],{"name":525,"orcid":526},"Ghaith Al-refai","https:\u002F\u002Forcid.org\u002F0000-0002-8505-9719",{"name":528,"orcid":529},"Mahmoud Al-Sarayreh","https:\u002F\u002Forcid.org\u002F0000-0001-8711-0453",{"tldr":531,"method":532,"finding":533,"direction":112,"opportunity":534},"系统综述智慧农业机器人、传感、AI、数据集与评估指标，指出落地瓶颈并展望方向。","系统批判性综述，提出系统级框架，覆盖机器人、多模态传感、AI与基准。","原型与数据集进展明显，但跨季节验证、基准碎片化与部署指标不足阻碍落地。","可研究跨季节跨区域鲁棒AI、部署导向评估指标及数字孪生与隐私保护学习。","2026-10-03T23:30:10.036723Z"]