[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2902":3,"related-2902":46},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":8,"paper":36,"created_at":45},2902,"TSAFI-DT:可持续性感知花生产量预测数字孪生框架,登MDPI AI 7(9)364","https:\u002F\u002Fwww.mdpi.com\u002F2673-2688\u002F7\u002F9\u002F364","本研究提出TSAFI-DT可回顾验证的、数据驱动的Digital Twin原型,集成时空数据重建、可持续状态表征、分层产量预测、反事实分析与情景模拟。基于1997-2023年印度地区级花生数据,采用贝叶斯优化的XGBoost模型进行一步前瞻产量预测,RMSE=0.171 t\u002Fha,显著优于基线;结合固定效应与合成控制分析,eRAI扩展再生农业指数整合作物多样性、生产力稳定性、土地利用效率和产量趋势,预测产量在可持续性扰动下可提升12.4%。",null,"MDPI AI","2026-09-14T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,19,13,6,1,"方法新颖、数据规模扎实的农业数字孪生研究，对智慧农业与产量预测领域有参考价值，但属细分学术进展，非产业级事件。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","数字孪生","可持续农业","花生产量预测",[32,33],"TSAFI-DT 花生 数字孪生","印度 花生 产量预测","TSAFI-DT花生数字孪生-2902",0,{"doi":8,"openalex_id":8,"authors":37,"venue":8,"cited_by_count":35,"oa_url":8,"card":38,"direction":42,"ingested_from":44},[],{"tldr":39,"method":40,"finding":41,"direction":42,"opportunity":43},"提出可持续性感知数字孪生框架TSAFI-DT，用于印度花生产量预测与情景模拟。","基于1997-2023年印度地区级数据，用贝叶斯优化XGBoost和合成控制分析","XGBoost预测RMSE为0.171 t\u002Fha，可持续性扰动下产量可提升12.4%。","农业人工智能与决策模型","可探索将数字孪生与实时物联网数据结合，实现动态可持续性评估与决策支持。","agent","2026-09-19T00:06:08.754978Z",{"total":20,"page":21,"page_size":20,"items":47},[48,95,131,173,212,242],{"id":49,"title":50,"url":51,"summary":52,"summary_zh":53,"content":8,"source_name":54,"source_url":51,"published_at":55,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":56,"score_detail":57,"sources":62,"tags":64,"search_phrases":67,"slug":70,"view_count":35,"doi":71,"paper":72,"created_at":94},2659,"Application of Remote Sensing and Machine Learning in Sustainable Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18189472","Global agriculture is undergoing a period of profound transformation, driven by the need to increase food production in a context characterized by climate change, the degradation of natural resources and increasing pressure on agricultural ecosystems [...]","全球农业正经历一场深刻变革，其驱动力是在气候变化、自然资源退化以及农业生态系统压力日益增大的背景下提高粮食产量的需求。[...]","Sustainability","2026-09-16T00:00:00Z",77,{"impact":16,"substance":58,"depth":59,"authority":19,"freshness":60,"relevant":21,"comment":61},20,17,9,"发表于核心期刊的遥感与机器学习综述，方法视角新颖、时效性强，对智慧农业技术路线有参考价值，值得进入每日精选。",[63],{"name":54,"url":51},[26,27,65,29,66],"机器学习","遥感",[68,69],"农业人工智能 可持续农业 智慧农业 机器学习","农业人工智能 可持续农业","农业人工智能可持续农业智慧农业机器学习-2659","10.3390\u002Fsu18189472",{"doi":71,"openalex_id":73,"authors":74,"venue":54,"cited_by_count":35,"oa_url":51,"card":87,"direction":91,"ingested_from":93},"W7213301703",[75,78,81,84],{"name":76,"orcid":77},"Mihai Valentin Herbei","https:\u002F\u002Forcid.org\u002F0000-0002-3884-3658",{"name":79,"orcid":80},"Ana-Cornelia Badea","https:\u002F\u002Forcid.org\u002F0000-0003-4521-5403",{"name":82,"orcid":83},"Aleksandar Ristić","https:\u002F\u002Forcid.org\u002F0000-0003-0979-3345",{"name":85,"orcid":86},"Paul Sestraș","https:\u002F\u002Forcid.org\u002F0000-0002-8554-0924",{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"综述遥感与机器学习在可持续农业中的应用现状与前景。","综述遥感数据与机器学习方法在农业中的应用。","遥感结合机器学习可提升农业监测与可持续管理能力。","农业遥感与作物表型","可探索多源遥感与可解释机器学习融合，用于小农户精准决策与碳核算。","openalex","2026-09-16T23:30:28.640661Z",{"id":96,"title":97,"url":98,"summary":99,"summary_zh":100,"content":8,"source_name":101,"source_url":98,"published_at":102,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":103,"score_detail":104,"sources":108,"tags":110,"search_phrases":113,"slug":116,"view_count":35,"doi":117,"paper":118,"created_at":130},2641,"Smart Agriculture and Sustainable Development in Agro-Ecosystems: Innovative Computational Modeling for Digital Twins","https:\u002F\u002Fdoi.org\u002F10.37394\u002F232015.2026.22.80","By boosting yields, improving efficiency, and reducing costs (while managing resources), digital technologies have driven innovation in agro-ecosystems in recent years. As a means to overcome ever-limited resources, Smart Agriculture – sometimes also referred to as Agriculture 4.0 – has increasingly leveraged digital solutions to achieve efficiency and sustainability. Digital twins in rural systems perform as computational (in silico) replicas of food production lines, which can drive innovation by enabling real-time optimization as well as predictive decision-making. Considering that both non-human and human actors are directly involved, this paper discusses (from a strategic alignment viewpoint) a prospective symbiotic ethos that farmers and ranchers may pursue when it comes to integrated, participatory and efficient transfer and usage of digital technology in rural activities. Bearing particularly in mind smallholders, challenges and opportunities for Smart Agriculture include: (i) implementation of hybrid in-house simulators of agro-ecosystems by suitably combining mechanistic modeling with data-driven simulation; (ii) use of dimensionless mathematical modeling to expedite scale-up, optimization, and translation of innovative digital technologies; and (iii) validating as well as transferring novel digital solutions in consideration of strategic issues identified by rural end-users.","近年来，数字技术通过提高产量、提升效率、降低成本（同时管理资源），推动了农业生态系统的创新。作为克服日益有限的资源的一种手段，智慧农业（有时也被称为农业4.0）越来越多地利用数字解决方案来实现效率和可持续性。农村系统中的数字孪生作为食品生产线的计算（计算机模拟）副本，通过实现实时优化和预测性决策来推动创新。考虑到非人类和人类参与者都直接参与其中，本文（从战略协同的视角）探讨了农民和牧场主在将数字技术整合、参与式和高效地转移及应用于农村活动时可能追求的一种前瞻性共生理念。特别考虑到小农户，智慧农业面临的挑战和机遇包括：（i）通过将机理建模与数据驱动模拟适当结合，实施农业生态系统的混合内部模拟器；（ii）使用无量纲数学建模来加速创新数字技术的规模化、优化和转化；（iii）在考虑农村终端用户所识别的战略问题的基础上，验证和转移新型数字解决方案。","WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT","2026-09-15T00:00:00Z",74,{"impact":105,"substance":58,"depth":59,"authority":19,"freshness":106,"relevant":21,"comment":107},16,8,"核心期刊论文，提出农业生态数字孪生的混合建模与无量纲化方法，对小农户数字化转型有参考价值，但偏理论、时效略滞后。",[109],{"name":101,"url":98},[26,27,28,111,112],"小农户","农业建模",[114,115],"农业人工智能 农业建模 数字孪生 智慧农业","农业人工智能 农业建模","农业人工智能农业建模数字孪生智慧农业-2641","10.37394\u002F232015.2026.22.80",{"doi":117,"openalex_id":119,"authors":120,"venue":101,"cited_by_count":35,"oa_url":123,"card":124,"direction":128,"ingested_from":93},"W7213267978",[121],{"name":122,"orcid":8},"Jose Rabi","https:\u002F\u002Fwseas.com\u002Fjournals\u002Fead\u002F2026\u002Fb625115-032(2026).pdf",{"tldr":125,"method":126,"finding":127,"direction":128,"opportunity":129},"探讨数字孪生与计算建模在智慧农业可持续发展中的应用，聚焦小农户的挑战与机遇。","混合机理与数据驱动模拟、无量纲数学建模、数字孪生计算副本。","提出农民与牧场主共生的数字技术转移理念，强调小农户的参与式整合。","智慧农业 \u002F 农业物联网","可研究小农户场景下混合模拟器的轻量化与低成本部署，以及数字孪生技术的参与式验证方法。","2026-09-16T23:30:10.078180Z",{"id":132,"title":133,"url":134,"summary":135,"summary_zh":136,"content":8,"source_name":137,"source_url":134,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":138,"score_detail":139,"sources":142,"tags":144,"search_phrases":147,"slug":150,"view_count":35,"doi":151,"paper":152,"created_at":172},2537,"A Counterfactual-Enabled Agricultural Decision Support Framework for Sustainability-Aware Groundnut Yield Prediction Using Bayesian-Optimized XGBoost","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fai7090364","Sustainable agricultural planning requires predictive frameworks that can capture spatiotemporal variability, sustainability dynamics, and the potential outcomes of alternative management scenarios. The research proposes TSAFI-DT, a retrospectively validated, data-driven Digital Twin prototype integrating spatiotemporal data reconstruction, sustainability-state representation, hierarchical yield forecasting, counterfactual analysis, and scenario simulation. The framework operates on historical district-level APY observations and therefore represents a retrospective approximation of Digital Twin operation rather than a continuously synchronized cyber-physical agricultural Digital Twin. The Extended Regenerative Agriculture Index (eRAI) combines crop diversity, productivity–stability, land-use efficiency, and yield-trend information to characterize district-level sustainability states. A Bayesian-optimized XGBoost model is employed for one-step-ahead yield forecasting under temporal validation, while fixed-effects and synthetic-control analyses provide complementary associational and intervention-associated evidence. Evaluation using district-level groundnut data from India during 1997–2023 demonstrates that the proposed predictor achieves an RMSE of 0.171 t\u002Fha and R2=0.92, outperforming the evaluated baselines with statistically significant differences (p\u003C0.05). The fully adjusted fixed-effects model identifies a positive association between higher sustainability states and yield, while retrospective Digital Twin replay demonstrates close temporal agreement between predicted and observed outcomes. Model-based scenario simulations indicate predicted yield increases of up to 12.4% under the evaluated sustainability-state perturbations; these estimates represent counterfactual sensitivity rather than guaranteed causal effects. TSAFI-DT provides a reproducible framework for sustainability-aware agricultural forecasting, comparative scenario exploration, and data-driven decision support.","可持续农业规划需要能够捕捉时空变异性、可持续性动态以及替代管理情景潜在结果的预测框架。本研究提出TSAFI-DT，一个经回溯验证的数据驱动数字孪生（Digital Twin）原型，集成了时空数据重建、可持续性状态表征、分层产量预测、反事实分析和情景模拟。该框架基于历史地区级APY观测数据运行，因此代表的是数字孪生运行的回溯近似，而非持续同步的网络-物理农业数字孪生。扩展再生农业指数（Extended Regenerative Agriculture Index, eRAI）综合了作物多样性、生产力-稳定性、土地利用效率和产量趋势信息，以刻画地区级可持续性状态。采用贝叶斯优化的XGBoost模型在时间验证下进行一步超前产量预测，同时固定效应和合成控制分析提供互补的关联性证据和干预关联性证据。利用印度1997—2023年地区级花生数据进行评估，结果表明所提出的预测器实现了0.171 t\u002Fha的RMSE和R²=0.92，优于所评估的基线模型且差异具有统计学显著性（p\u003C0.05）。完全调整的固定效应模型识别出较高可持续性状态与产量之间的正相关关系，而回溯性数字孪生重放表明预测结果与观测结果在时间上高度一致。基于模型的情景模拟显示，在所评估的可持续性状态扰动下，预测产量增幅最高可达12.4%；这些估计代表的是反事实敏感性而非保证的因果效应。TSAFI-DT为可持续性感知的农业预测、比较情景探索和数据驱动决策支持提供了一个可复现的框架。","AI",76,{"impact":105,"substance":17,"depth":16,"authority":140,"freshness":106,"relevant":21,"comment":141},12,"提出融合数字孪生、反事实分析与贝叶斯优化XGBoost的花生产量预测框架，方法新颖、数据跨度长且验证充分，对可持续农业决策支持有参考价值。",[143],{"name":137,"url":134},[26,27,145,28,146],"产量预测","花生种植",[148,149],"农业人工智能 产量预测 数字孪生 智慧农业","农业人工智能 产量预测","农业人工智能产量预测数字孪生智慧农业-2537","10.3390\u002Fai7090364",{"doi":151,"openalex_id":153,"authors":154,"venue":137,"cited_by_count":35,"oa_url":134,"card":166,"direction":171,"ingested_from":93},"W7212812029",[155,158,161,163],{"name":156,"orcid":157},"Rekha R Nair","https:\u002F\u002Forcid.org\u002F0000-0002-7207-2877",{"name":159,"orcid":160},"Tina Babu","https:\u002F\u002Forcid.org\u002F0000-0001-7846-3679",{"name":162,"orcid":8},"Sumendra Yogarayan",{"name":164,"orcid":165},"Abdul Razak","https:\u002F\u002Forcid.org\u002F0000-0002-6108-3183",{"tldr":167,"method":168,"finding":169,"direction":42,"opportunity":170},"提出TSAFI-DT数字孪生框架，用贝叶斯优化XGBoost预测印度花生产量并做反事实情景分析。","基于1997-2023年印度县级花生产量数据，构建eRAI可持续性指数，采用贝叶","模型RMSE为0.171 t\u002Fha、R²=0.92，高可持续状态与产量正相关，情景模拟产量最高提升1","可延伸至实时物联网数据驱动的数字孪生，并验证反事实情景的因果效应与跨作物泛化能力。","数字乡村与农业信息化","2026-09-15T23:30:26.594281Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":8,"source_name":179,"source_url":176,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":180,"score_detail":181,"sources":185,"tags":187,"search_phrases":191,"slug":194,"view_count":35,"doi":195,"paper":196,"created_at":211},2503,"Agentic AI for Livestock Housing Management: Applications, Benchmarking, and Readiness Assessment","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112361","Agentic artificial intelligence is emerging as an extension of Precision Livestock Farming by linking perception, reasoning, planning, and bounded action within human-supervised livestock-housing workflows. This review synthesizes 90 publications on agentic AI, multi-agent systems, retrieval-augmented generation, large language models, foundation models, robotics, digital twins, simulation, computer vision, cyber-physical control, and related enabling technologies for livestock-housing management. We propose a Perception–Reasoning–Action–Safety (PRAS) loop and an Agentic Livestock Housing Readiness Scale to classify systems from passive monitoring and advisory decision support to supervised, safety-constrained closed-loop operation. A staged benchmarking perspective is also used to integrate algorithmic performance, biological relevance, safety, auditability, economic feasibility, and human–AI interaction. Current evidence is strongest for perception, advisory reasoning, natural-language data access, welfare-risk interpretation, and simulation-supported decision support, whereas robust barn-wide autonomous control remains largely unvalidated. Technology categories were coded non-exclusively; therefore, publication frequencies indicate representation within the selected corpus rather than effectiveness, evidence strength, or deployment readiness. Across species, dairy cattle provide the most developed evidence base, poultry studies mainly address environmental comfort and nutrition support, swine systems emphasize simulation-based precision feeding, and small-ruminant evidence remains concentrated in advisory tools and contextual embodied monitoring. Overall, agentic AI in livestock housing is currently more mature as an orchestration, explanation, and decision-support layer than as an autonomous control technology. By distinguishing direct housing applications, semi-agentic prototypes, and enabling technologies, this review clarifies the gap between current evidence and deployable autonomy. Progress towards higher readiness will require cross-farm validation, biological plausibility, source-grounding audits, safety assurance, interoperability, economic assessment, transparent benchmarking, and explicit human oversight.","智能体人工智能（Agentic AI）正在成为精准畜牧养殖（Precision Livestock Farming）的延伸，通过在人工监督的畜舍工作流程中将感知、推理、规划与有限行动相连接。本综述综合了90篇文献，涵盖智能体人工智能、多智能体系统、检索增强生成、大语言模型、基础模型、机器人技术、数字孪生、仿真、计算机视觉、信息物理控制及相关使能技术在畜舍管理中的应用。我们提出了感知—推理—行动—安全（Perception–Reasoning–Action–Safety, PRAS）循环和智能体畜舍就绪度量表（Agentic Livestock Housing Readiness Scale），将系统从被动监测与建议性决策支持分类至受监督、安全约束的闭环运行。同时采用分阶段基准测试视角，整合算法性能、生物学相关性、安全性、可审计性、经济可行性及人机交互。当前证据最充分的领域为感知、建议性推理、自然语言数据访问、福利风险解读及仿真支持的决策支持，而稳健的畜舍级自主控制仍基本未经验证。技术类别采用非排他性编码，因此文献频次仅表示在所选语料中的代表性，而非有效性、证据强度或部署就绪度。跨物种来看，奶牛提供了最成熟的证据基础，家禽研究主要涉及环境舒适度与营养支持，猪系统侧重于基于仿真的精准饲喂，而小反刍动物的证据仍集中于建议性工具与情境化具身监测。总体而言，畜舍中的智能体人工智能目前作为编排、解释与决策支持层比作为自主控制技术更为成熟。通过区分直接畜舍应用、半智能体原型与使能技术，本综述厘清了当前证据与可部署自主性之间的差距。迈向更高就绪度需要跨农场验证、生物学合理性、来源溯源审计、安全保障、互操作性、经济评估、透明基准测试及明确的人工监督。","Computers and Electronics in Agriculture",86,{"impact":17,"substance":182,"depth":16,"authority":183,"freshness":60,"relevant":21,"comment":184},23,14,"该综述系统梳理了智能体AI在畜禽舍管理中的应用，提出PRAS框架与准备度评估量表，方法新颖、结论审慎，对智慧畜牧研究与落地具有较高参考价值。",[186],{"name":179,"url":176},[26,27,188,28,189,190],"多智能体","智能畜牧","精准养殖",[192,193],"农业人工智能 多智能体 数字孪生 智慧农业","农业人工智能 多智能体","农业人工智能多智能体数字孪生智慧农业-2503","10.1016\u002Fj.compag.2026.112361",{"doi":195,"openalex_id":197,"authors":198,"venue":179,"cited_by_count":35,"oa_url":205,"card":206,"direction":128,"ingested_from":93},"W7213126233",[199,201,203],{"name":200,"orcid":8},"Alexey Ruchay",{"name":202,"orcid":8},"Hao Guo",{"name":204,"orcid":8},"Andrea Pezzuolo","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0168169926009592\u002Fpdf",{"tldr":207,"method":208,"finding":209,"direction":42,"opportunity":210},"综述90篇文献，提出PRAS框架与就绪度量表，评估智能体AI在畜舍管理中的应用与成熟度。","文献综述，提出感知-推理-行动-安全循环与就绪度量表，分阶段基准评估。","智能体AI目前更适合作为决策支持与解释层，而非自主控制，牛的证据最充分。","可研究跨农场验证、安全约束闭环控制与可审计基准，填补自主畜舍管理空白。","2026-09-15T23:30:01.732357Z",{"id":213,"title":214,"url":215,"summary":216,"summary_zh":217,"content":8,"source_name":218,"source_url":215,"published_at":219,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":220,"score_detail":221,"sources":224,"tags":226,"search_phrases":228,"slug":231,"view_count":35,"doi":232,"paper":233,"created_at":241},1624,"AGRITWIN AI: A DIGITAL TWIN AND MULTIMODAL EXPLAINABLE AI FRAMEWORK FOR PRECISION AGRICULTURE USING SATELLITE IMAGERY, IOT SENSORS, WEATHER INTELLIGENCE, LARGE LANGUAGE MODELS, AND RETRIEVAL-AUGMENTED GENERATION","https:\u002F\u002Fdoi.org\u002F10.56726\u002Firjmets102032","Current digital precision agricultural systems suffer from a critical, structural operational data-cognition gap: modern cyber-physical sensing systems (e.g.IoT devices, remote sensing pipelines) operate as opaque numeric computation engines yielding multi-dimensional, high-frequency numeric matrices while agriculture specific Large Language Models (LLMs) remain locked into a non-numeric world that is conceptually divorced from physical realities on the ground.To bridge this data-cognition gap, this paper proposes AgriTwin AI: a holistic, end-to-end framework coupling a spatial-temporal Digital Twin engine to an autonomous, Multimodal Explainable AI (XAI) advisory pipeline.AgriTwin AI ingests heterogeneous input from 1D linear IoT sensor streams, text-based meteorological intelligence, and 2D multi-spectral satellite raster images; an asymmetric, cross-modal late-fusion architecture synchronizes these data types into a coherent, real-time virtual representation of the farm field.When the physics-driven equations of the Digital Twin engine identify an environmental threshold breach or anomaly, AgriTwin AI automatically triggers a bespoke Retrieval-Augmented Generation (RAG) cycle that is governed by an agronomic Domain Knowledge Processing Layer (DKPL).Linking fine-tuned vision-language parameters of Low-Rank Adaptation (LoRA) to text documents from regional agricultural handbooks stored in a local vector database, AgriTwin AI translates complex numerical anomalies into highly specific, traceable, and human-verifiable natural language diagnostic information.Based on preliminary simulation, AgriTwin AI reduces biological hallucinations below 0.4% and achieves a 95.2% safety constraint validation accuracy, establishing a reliable system for data-driven, explainable, and secure agricultural decision making.","当前的数字精准农业系统面临着一个关键的结构性运营数据认知鸿沟：现代网络物理感知系统（如物联网设备、遥感处理管道）作为不透明的数值计算引擎运行，产生多维、高频的数值矩阵，而农业专用的大语言模型（LLMs）仍局限于非数值世界，在概念上与地面物理现实脱节。为弥合这一数据认知鸿沟，本文提出AgriTwin AI：一个将时空数字孪生引擎与自主多模态可解释人工智能（XAI）咨询管道相结合的整体端到端框架。AgriTwin AI接收来自一维线性物联网传感器流、基于文本的气象情报以及二维多光谱卫星栅格图像的异构输入；一种非对称的跨模态后期融合架构将这些数据类型同步为连贯、实时的农田虚拟表示。当数字孪生引擎的物理驱动方程识别出环境阈值突破或异常时，AgriTwin AI自动触发由农学领域知识处理层（DKPL）管理的定制检索增强生成（RAG）循环。通过将低秩适应（LoRA）的微调视觉-语言参数与存储在本地向量数据库中的区域农业手册文本文档相连接，AgriTwin AI将复杂的数值异常转化为高度具体、可追溯且可由人工验证的自然语言诊断信息。基于初步模拟，AgriTwin AI将生物幻觉率降至0.4%以下，并达到95.2%的安全约束验证准确率，为数据驱动、可解释且安全的农业决策建立了可靠系统。","International Research Journal of Modernization in Engineering Technology and Science","2026-09-03T00:00:00Z",73,{"impact":16,"substance":17,"depth":16,"authority":106,"freshness":222,"relevant":21,"comment":223},7,"提出整合数字孪生与多模态可解释AI的农业框架，方法新颖，但尚处模拟阶段，信源为普通期刊。",[225],{"name":218,"url":215},[26,27,227,28,66],"大语言模型",[229,230],"农业人工智能 大语言模型 数字孪生 智慧农业","农业人工智能 大语言模型","农业人工智能大语言模型数字孪生智慧农业-1624","10.56726\u002Firjmets102032",{"doi":232,"openalex_id":234,"authors":235,"venue":218,"cited_by_count":35,"oa_url":215,"card":236,"direction":128,"ingested_from":93},"W7207853514",[],{"tldr":237,"method":238,"finding":239,"direction":42,"opportunity":240},"提出 AgriTwin AI 框架，融合数字孪生与多模态可解释 AI，弥合农业数据与认知鸿沟。","结合数字孪生、跨模态融合、LoRA微调视觉语言模型及RAG，利用卫星、IoT和气","将数值异常转为可验证的自然语言诊断，幻觉率低于0.4%，安全验证准确率95.2%。","可探索多模态融合在作物病害早期诊断中的应用，或开发轻量化边缘部署方案以适配田间实时决策。","2026-09-04T23:30:11.566549Z",{"id":243,"title":244,"url":245,"summary":246,"summary_zh":247,"content":8,"source_name":248,"source_url":245,"published_at":219,"category":11,"cover_url":8,"hotness":249,"is_selected":13,"score":250,"score_detail":251,"sources":254,"tags":258,"search_phrases":260,"slug":262,"view_count":35,"doi":263,"paper":264,"created_at":282},1618,"Smart Agronomy: Emerging Technologies and Sustainable Practices","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22275229","Agriculture is changing rapidly due to the application of new technologies to agri-food systems. The article “Smart Agronomy: Emerging Technologies and Sustainable Practices,” edited by Anil Kumar and colleagues, provides background on some of these technologies and sustainable agronomic practices. This particular review is oriented toward Chapter 13, “The Future of Agronomy: Emerging Trends and Challenges.” This chapter covers the path toward new agronomic practices such as precision agriculture, digital agriculture, artificial intelligence (AI), machine learning (ML), robotics, and biotechnology, as well as climate-smart agriculture, vertical farming, and soil conservation. The chapter also discusses some environmental, technological, economic, social, and policy challenges that may hinder the adoption and use of the technologies described. The review of the chapter and the successful use of related scholarly literature concerning Agriculture 4.0, precision agriculture, and machine learning, as well as sustainable agronomy and future food security, provide the primary basis for this report. From this review, the chapter is a much-needed, comprehensive review of modern agronomy and the new sustainable technologies, as well as the new social and environmental responsibilities that accompany these technologies. The new agronomic practices described in the chapter rely on new social, economic, and environmental responsibilities, and the chapter should be strengthened with empirical evidence and assessments. The reviewed material also provides the foundational concepts of smart agronomy that integrate newly sustainable technologies.","农业正因新技术在农业食品系统中的应用而迅速变革。由Anil Kumar及其同事编辑的文章《智慧农学：新兴技术与可持续实践》为其中一些技术和可持续农艺实践提供了背景介绍。本综述主要针对第13章“农学的未来：新兴趋势与挑战”展开。该章涵盖了通往精准农业、数字农业、人工智能（AI）、机器学习（ML）、机器人技术及生物技术等新型农艺实践，以及气候智慧型农业、垂直农业和土壤保护的路径。该章还讨论了可能阻碍所述技术采纳与应用的环境、技术、经济、社会及政策方面的若干挑战。对本章的评述以及对有关农业4.0、精准农业、机器学习、可持续农学及未来粮食安全等相关学术文献的成功运用，构成了本报告的主要基础。通过本次评述可见，该章是对现代农学、新兴可持续技术以及伴随这些技术而来的新的社会与环境责任的一次亟需的全面综述。该章所述的新型农艺实践依赖于新的社会、经济和环境责任，且该章应以实证证据和评估加以强化。所评述的材料还提供了整合新兴可持续技术的智慧农学的基础概念。","Zenodo (CERN European Organization for Nuclear Research)",25,57,{"impact":140,"substance":252,"depth":183,"authority":12,"freshness":20,"relevant":21,"comment":253},15,"综述性论文，系统梳理智慧农业新兴技术与挑战，但缺乏实证数据，影响有限。",[255,256],{"name":248,"url":245},{"name":248,"url":257},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22275230",[26,27,259,29],"精准农业",[261,69],"农业人工智能 可持续农业 智慧农业 精准农业","农业人工智能可持续农业智慧农业精准农业-1618","10.5281\u002Fzenodo.22275229",{"doi":263,"openalex_id":265,"authors":266,"venue":248,"cited_by_count":35,"oa_url":245,"card":277,"direction":128,"ingested_from":93},"W7207551232",[267,269,271,273,275],{"name":268,"orcid":8},"Neriza J. Kiram",{"name":270,"orcid":8},"Abey K. Sobrepeña",{"name":272,"orcid":8},"Nedzfa A. Ahajani",{"name":274,"orcid":8},"Rasmalyn S. Samla",{"name":276,"orcid":8},"Alwajir O. Tuttuh",{"tldr":278,"method":279,"finding":280,"direction":128,"opportunity":281},"综述智能农学新兴技术与可持续实践，重点评述第13章未来农学趋势与挑战。","文献综述与章节评述，结合Agriculture 4.0、精准农业、机器学习等学术","智能农学依赖新技术，但需加强实证评估，并应对环境、技术、经济、社会和政策挑战。","智能农学技术的社会与环境责任评估不足，可研究技术采纳的障碍及可持续性实证分析。","2026-09-04T23:30:09.902078Z"]