[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-543":3,"related-543":65},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":34,"secondary_topics":40,"classification":41,"search_phrases":59,"slug":62,"view_count":63,"doi":8,"paper":8,"created_at":64},543,"AgriAgent: A Heterogeneous Multi-Agent Orchestration Framework for Autonomous, Knowledge-Grounded Precision Agriculture","https:\u002F\u002Fwww.epj-conferences.org\u002Farticles\u002Fepjconf\u002Fabs\u002F2026\u002F39\u002Fepjconf_aist4ge26_06004\u002Fepjconf_aist4ge26_06004.html","面向精准农业受限于多模遥测难以实时自治的认知瓶颈,提出 AgriAgent 分层多智能体(MAS)框架,将大语言模型从被动分析工具重塑为目标导向的协调器,采用包含感知、认知、执行三层的三层编排(TLO)拓扑。构建基于 APSIM 生物物理模型并经 GAN 扩展的高保真 10,000 状态合成库,引入黑天鹅环境胁迫。基准测试显示推理成功率 98.2%,决策时延降低 97%,将传统人在回路模式转为智能体驱动,为生物-数字自治农场提供蓝图。",null,"Open Access\n\n| Issue |  | EPJ Web Conf. **Volume**383, 2026 The 1 st International Conference on Artificial Intelligence and Smart Technologies for Green Energy (AIST4GE’26) |\n| --- |\n|  |\n|  |\n| Article Number |  | 06004 |\n| Number of page(s) |  | 9 |\n| Section |  | Artificial Intelligence for Precision Agriculture and Hydraulic Systems |\n| DOI |  | [https:\u002F\u002Fdoi.org\u002F10.1051\u002Fepjconf\u002F202638306004](https:\u002F\u002Fdoi.org\u002F10.1051\u002Fepjconf\u002F202638306004) |\n| Published online |  | 12 August 2026 |\n\nEPJ Web of Conferences **383**, 06004 (2026)\n\n[https:\u002F\u002Fdoi.org\u002F10.1051\u002Fepjconf\u002F202638306004](https:\u002F\u002Fdoi.org\u002F10.1051\u002Fepjconf\u002F202638306004)\n\n*, Mohammed Berrahal**, Mimoune Yandouzi, Mounir Grari, Aissa Kerkour Elmiad and Mohammed Badaoui\n\nIIA Research Lab, ESTO, Mohamed First University, Oujda, Morocco\n\n* e-mail: [n.boussala@ump.ac.ma](mailto:n.boussala@ump.ac.ma)\n\n** e-mail: [m.berrahal@ump.ac.ma](mailto:m.berrahal@ump.ac.ma)\n\nPublished online: 12 August 2026\n\nAbstract\n\nThe global agricultural sector is currently navigating an unprecedented convergence of challenges, characterized by escalating caloric demands, systemic climate instability, and the urgent necessity for resource-neutral production. While the advent of Precision Agriculture (PA) introduced data-driven monitoring, the industry remains hindered by a \"cognitive bottleneck\" the inability to transform fragmented, multi-modal telemetry into real-time, autonomous interventions with-out intensive human oversight. This paper introduces AgriAgent, a pioneering, hierarchical Multi-Agent System (MAS) framework that redefines the role of Large Language Models (LLMs) from passive analytical tools to active, goal-oriented orchestrators. Unlike traditional monolithic architectures, AgriAgent em-ploys a Tri-Layer Orchestration (TLO) topology, comprising specialized agents for Perception, Cognition, and Actuation. To validate the framework's reasoning plasticity, we developed a high-fidelity synthetic library of _N_ = 10,000 biological states, grounded in AP-SIM biophysical modeling and expanded via Generative Adversarial Networks (GANs) to include \"Black Swan\" environmental stressors. Our results demonstrate that by shifting from a reactive \"human-in-the-loop\" model to a proactive \"agent-driven\" paradigm, the system achieves a state of Ag-ronomic Equilibrium. Quantitative benchmarking shows a 98.2% reasoning suc-cess rate, a 97% reduction in decision latency, and a significant optimization of computational overhead. This research provides the definitive blueprint for the transition toward \"Bio-Digital\" autonomy, where the farm functions as a self-correcting, intelligent ecosystem capable of sustaining both global yield and eco-logical integrity in an increasingly volatile climate.\n\n_© The Authors, published by EDP Sciences, 2026_\n\n[![Image 1: Licence Creative Commons](https:\u002F\u002Fi.creativecommons.org\u002Fl\u002Fby\u002F4.0\u002F88x31.png)](https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F)This is an Open Access article distributed under the terms of the [Creative Commons Attribution License 4.0](https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.\n\nCurrent usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.\n\nData correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.\n\nInitial download of the metrics may take a while.\n\n*   [Reader's services](https:\u002F\u002Fwww.epj-conferences.org\u002Fcomponent\u002Fservices\u002F)\n*   [Email-alert](https:\u002F\u002Fwww.epj-conferences.org\u002Fcomponent\u002Fservices\u002F?label=toc_email)","EPJ Web of Conferences (AIST4GE'26)","2026-08-11T22:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,20,12,8,1,"提出多智能体框架提升精准农业自主性，数据详实，但属学术前沿，产业影响尚待验证。",[25],{"name":10,"url":6},[27,28,29,30,31,32,33],"农业人工智能","精准农业","多智能体系统","LLM","自主决策","智能农场","生物-数字",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":42,"locked":14,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":48},[47,53],{"code":35,"score":20,"supported":48,"title":49,"abstract":51,"tags":52},true,[50],"precision agriculture",[28],[28],{"code":54,"score":55,"supported":14,"title":56,"abstract":57,"tags":58},"I2",2,[],[],[30,27],[60,61],"农业人工智能 多智能体系统 生物-数字 智能农场","农业人工智能 多智能体系统","农业人工智能多智能体系统生物-数字智能农场-543",4,"2026-08-15T00:02:55.829229Z",{"total":66,"page":22,"page_size":66,"items":67},6,[68,124,171,226,273,322],{"id":69,"title":70,"url":71,"summary":72,"summary_zh":8,"content":8,"source_name":73,"source_url":71,"published_at":74,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":75,"score_detail":76,"sources":81,"tags":83,"primary_topic":87,"secondary_topics":88,"classification":89,"search_phrases":101,"slug":104,"view_count":105,"doi":106,"paper":107,"created_at":123},5539,"Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10473-z","Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems。Precision Agriculture","Precision Agriculture","2026-10-22T00:00:00Z",79,{"impact":77,"substance":78,"depth":17,"authority":79,"freshness":13,"relevant":22,"comment":80},16,21,14,"核心期刊新论文，提出鲁棒且可解释的RGB作物行检测方法，对视觉导航农机具参考价值明确，但属细分技术进展，未达重大突破层级。",[82],{"name":73,"url":71},[84,27,28,85,86],"智慧农业","机器视觉","作物行检测",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":90},{"candidates":91,"agricultural_evidence":48},[92,97],{"code":35,"score":93,"supported":48,"title":94,"abstract":95,"tags":96},5,[],[50],[84,28],{"code":54,"score":55,"supported":14,"title":98,"abstract":99,"tags":100},[],[],[27,85],[102,103],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 机器视觉","RGB作物行检测-5539",0,"10.1007\u002Fs11119-026-10473-z",{"doi":106,"openalex_id":108,"authors":109,"venue":73,"cited_by_count":105,"oa_url":8,"card":8,"direction":8,"ingested_from":122},"W7223117828",[110,113,116,119],{"name":111,"orcid":112},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":114,"orcid":115},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":117,"orcid":118},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":120,"orcid":121},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","openalex","2026-10-22T11:20:09.678587Z",{"id":125,"title":126,"url":127,"summary":128,"summary_zh":129,"content":8,"source_name":130,"source_url":127,"published_at":131,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":75,"score_detail":132,"sources":135,"tags":137,"primary_topic":140,"secondary_topics":141,"classification":142,"search_phrases":154,"slug":157,"view_count":105,"doi":158,"paper":159,"created_at":170},5316,"PLANT-GPK v2.7: Spatial Heterogeneity and Predictive Digital Twin for Plant Phenotyping","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23460273","Traditional image-based plant phenotyping assumes spatial homogeneity, estimating a single set of physiological parameters for the entire plant. This paper introduces a novel extension to the PLANT-GPK framework: Spatial Physiological Heatmaps and Predictive Digital Twins. By dividing the plant image into a 3x3 grid and solving the inverse ODE problem for each spatial patch, we reveal the hidden spatial heterogeneity of resource allocation. Furthermore, the recovered parameters are extrapolated to predict the exact day of harvest using a continuous smooth-physics model, providing a powerful prescriptive tool for precision agriculture.","传统的基于图像的植物表型分析假设空间同质性，即对整个植株估计单一组生理参数。本文提出了对PLANT-GPK框架的一种新颖扩展：空间生理热图与预测性数字孪生。通过将植物图像划分为3×3网格并针对每个空间区块求解逆常微分方程（ODE）问题，我们揭示了资源分配中隐藏的空间异质性。此外，利用连续光滑物理模型对所恢复的参数进行外推，以预测确切的收获日期，为精准农业提供了强有力的规范性工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-19T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":20,"freshness":133,"relevant":22,"comment":134},9,"该论文提出植物表型空间异质性建模与预测性数字孪生方法，方法新颖、结论有实用价值，但属细分领域预印本成果，未达重大突破层级。",[136],{"name":130,"url":127},[84,27,138,28,139],"数字孪生","植物表型",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":143},{"candidates":144,"agricultural_evidence":48},[145,150],{"code":35,"score":19,"supported":48,"title":146,"abstract":148,"tags":149},[147],"digital twin",[28,138,50],[138,84,28],{"code":54,"score":22,"supported":14,"title":151,"abstract":152,"tags":153},[],[],[27],[155,156],"PLANT-GPK 植物表型","空间生理热图 数字孪生","PLANT-GPK植物表型-5316","10.5281\u002Fzenodo.23460273",{"doi":158,"openalex_id":160,"authors":161,"venue":130,"cited_by_count":105,"oa_url":127,"card":164,"direction":168,"ingested_from":122},"W7222885652",[162],{"name":163,"orcid":8},"MOHAMMED TAHAR SAIM",{"tldr":165,"method":166,"finding":167,"direction":168,"opportunity":169},"将植物图像分3x3网格反演ODE参数，构建空间生理热图与预测数字孪生。","基于PLANT-GPK框架，分块求解逆ODE问题，并用连续平滑物理模型外推。","揭示了植物资源分配的空间异质性，可预测精确收获日。","农业遥感与作物表型","可探索更细粒度网格与多模态表型融合，提升数字孪生预测精度与田间适用性。","2026-10-19T23:30:19.414490Z",{"id":172,"title":173,"url":174,"summary":175,"summary_zh":8,"content":8,"source_name":176,"source_url":174,"published_at":177,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":178,"score_detail":179,"sources":182,"tags":184,"primary_topic":186,"secondary_topics":187,"classification":188,"search_phrases":205,"slug":208,"view_count":55,"doi":209,"paper":210,"created_at":225},5082,"Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jip.2026.108765","Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives。Journal of Invertebrate Pathology","Journal of Invertebrate Pathology","2026-10-14T00:00:00Z",67,{"impact":20,"substance":77,"depth":180,"authority":79,"freshness":21,"relevant":22,"comment":181},17,"核心期刊综述，聚焦昆虫病原线虫在精准农业中的应用方法与挑战，专业性强但属细分领域，产业影响有限。",[183],{"name":176,"url":174},[84,27,28,185],"生物防治",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":189},{"candidates":190,"agricultural_evidence":48},[191,196,201],{"code":35,"score":192,"supported":48,"title":193,"abstract":194,"tags":195},13,[50],[50],[84,28],{"code":197,"score":22,"supported":14,"title":198,"abstract":199,"tags":200},"F4",[],[],[185],{"code":54,"score":22,"supported":14,"title":202,"abstract":203,"tags":204},[],[],[27],[206,207],"昆虫病原线虫 精准农业","Journal of Invertebrate Pathology 线虫","昆虫病原线虫精准农业-5082","10.1016\u002Fj.jip.2026.108765",{"doi":209,"openalex_id":211,"authors":212,"venue":176,"cited_by_count":105,"oa_url":8,"card":8,"direction":224,"ingested_from":122},"W7221746877",[213,216,219,221],{"name":214,"orcid":215},"Alperen Kaan Bütüner","https:\u002F\u002Forcid.org\u002F0000-0002-2121-3529",{"name":217,"orcid":218},"Hilal Erdoğan","https:\u002F\u002Forcid.org\u002F0000-0002-0387-2600",{"name":220,"orcid":8},"İsmail Alper Susurluk",{"name":222,"orcid":223},"Edwin E. Lewis","https:\u002F\u002Forcid.org\u002F0000-0002-8825-1562","智慧农业 \u002F 农业物联网","2026-10-16T23:30:09.556034Z",{"id":227,"title":228,"url":229,"summary":230,"summary_zh":8,"content":8,"source_name":231,"source_url":8,"published_at":232,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":233,"score_detail":234,"sources":236,"tags":238,"primary_topic":241,"secondary_topics":242,"classification":243,"search_phrases":259,"slug":262,"view_count":22,"doi":8,"paper":263,"created_at":272},4647,"[预印本]植物病害数据集的数据中心综述:分类、关键分析、环境变异性与精准农业启示(A Data-Centric Review of Plant Disease Datasets: Taxonomy, Critical Analysis, Environmental Variability, and Implications for Precision Agriculture)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.07087","arXiv综述(2610.07087,2026年10月5日提交)从数据采集、可访问性、植物多样性、病害构成、类别结构与不平衡严重度建立植物病害数据集分类法,对比实验室与田间数据集差异,分析类内、跨作物、跨数据集多层次不平衡及环境变异性对模型性能的影响,提出融合温湿度、叶面湿润度等多模态环境参数的方向。[预印本未经同行评议]。","arXiv 2026-10-05 [预印本]","2026-10-05T00:00:00Z",71,{"impact":79,"substance":78,"depth":17,"authority":133,"freshness":133,"relevant":22,"comment":235},"arXiv预印本系统梳理植物病害数据集分类与不平衡问题，对农业AI数据建设有参考价值，但未经同行评议且偏学术，适合主题聚合而非头条精选。",[237],{"name":231,"url":229},[84,27,28,239,240],"植物病害","农业数据集",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":244},{"candidates":245,"agricultural_evidence":48},[246,250,254],{"code":35,"score":17,"supported":48,"title":247,"abstract":248,"tags":249},[28,50],[],[84,28],{"code":54,"score":22,"supported":14,"title":251,"abstract":252,"tags":253},[],[],[27],{"code":255,"score":22,"supported":14,"title":256,"abstract":257,"tags":258},"I6",[],[],[240],[260,261],"植物病害 环境变异性 精准农业","农业人工智能 农业数据集 智慧农业 植物病害","植物病害环境变异性精准农业-4647",{"doi":8,"openalex_id":8,"authors":264,"venue":8,"cited_by_count":105,"oa_url":8,"card":265,"direction":269,"ingested_from":271},[],{"tldr":266,"method":267,"finding":268,"direction":269,"opportunity":270},"综述植物病害数据集分类与不平衡问题，提出融合环境参数的方向。","综述数据集分类法，对比实验室与田间数据，分析多层次不平衡。","环境变异性和多层次类别不平衡显著影响病害识别模型性能。","农业人工智能与决策模型","构建融合温湿度、叶面湿润度等环境参数的多模态病害数据集与鲁棒模型。","agent","2026-10-11T00:09:47.731337Z",{"id":274,"title":275,"url":276,"summary":277,"summary_zh":8,"content":8,"source_name":278,"source_url":276,"published_at":279,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":280,"score_detail":281,"sources":284,"tags":286,"primary_topic":289,"secondary_topics":290,"classification":291,"search_phrases":307,"slug":310,"view_count":105,"doi":311,"paper":312,"created_at":321},4214,"A data-centric multi-modal ai framework for weed detection and classification in smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42452-026-09579-w","A data-centric multi-modal ai framework for weed detection and classification in smart agriculture。Discover Applied Sciences","Discover Applied Sciences","2026-10-04T00:00:00Z",64,{"impact":20,"substance":77,"depth":282,"authority":20,"freshness":133,"relevant":22,"comment":283},15,"提出面向智慧农业的多模态AI杂草检测分类框架，方法有创新但属细分技术进展，影响力有限。",[285],{"name":278,"url":276},[84,27,287,28,288],"杂草识别","多模态",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":292},{"candidates":293,"agricultural_evidence":48},[294,299,303],{"code":35,"score":192,"supported":48,"title":295,"abstract":297,"tags":298},[296],"smart agriculture",[296],[84,28],{"code":54,"score":55,"supported":14,"title":300,"abstract":301,"tags":302},[],[],[27,288],{"code":197,"score":22,"supported":14,"title":304,"abstract":305,"tags":306},[],[],[287],[308,309],"多模态 杂草检测 智慧农业","AI 杂草分类 精准农业","多模态杂草检测智慧农业-4214","10.1007\u002Fs42452-026-09579-w",{"doi":311,"openalex_id":313,"authors":314,"venue":278,"cited_by_count":105,"oa_url":276,"card":8,"direction":224,"ingested_from":122},"W7219919857",[315,318],{"name":316,"orcid":317},"Sayali Shinde","https:\u002F\u002Forcid.org\u002F0009-0002-8724-2880",{"name":319,"orcid":320},"Vahida Attar","https:\u002F\u002Forcid.org\u002F0000-0002-2285-7393","2026-10-05T23:30:09.187536Z",{"id":323,"title":324,"url":325,"summary":326,"summary_zh":327,"content":8,"source_name":328,"source_url":325,"published_at":329,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":330,"score_detail":331,"sources":333,"tags":335,"primary_topic":337,"secondary_topics":338,"classification":339,"search_phrases":355,"slug":358,"view_count":55,"doi":359,"paper":360,"created_at":389},2801,"A comprehensive survey on machine vision applications in precision agriculture: current trends and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41060-026-01278-4","A comprehensive survey on machine vision applications in precision agriculture: current trends and future perspectives。International Journal of Data Science and Analytics","精准农业中机器视觉应用的综合综述：当前趋势与未来展望。《国际数据科学与分析杂志》","International Journal of Data Science and Analytics","2026-09-17T00:00:00Z",77,{"impact":17,"substance":19,"depth":180,"authority":192,"freshness":133,"relevant":22,"comment":332},"核心期刊发表的机器视觉精准农业综述，方法梳理与趋势判断具参考价值，但属综述类论文，产业影响有限。",[334],{"name":328,"url":325},[84,27,28,336,85],"遥感监测",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":340},{"candidates":341,"agricultural_evidence":48},[342,346,350],{"code":35,"score":77,"supported":48,"title":343,"abstract":344,"tags":345},[50],[28,50],[84,28],{"code":54,"score":55,"supported":14,"title":347,"abstract":348,"tags":349},[],[],[27,85],{"code":351,"score":22,"supported":14,"title":352,"abstract":353,"tags":354},"I3",[],[],[336],[356,357],"农业人工智能 智慧农业 机器视觉 精准农业","农业人工智能 智慧农业","农业人工智能智慧农业机器视觉精准农业-2801","10.1007\u002Fs41060-026-01278-4",{"doi":359,"openalex_id":361,"authors":362,"venue":328,"cited_by_count":105,"oa_url":8,"card":384,"direction":269,"ingested_from":122},"W7213471057",[363,365,367,369,372,374,376,379,382],{"name":364,"orcid":8},"Shirun Gu",{"name":366,"orcid":8},"Xinyuan Fan",{"name":368,"orcid":8},"Lihui Zhu",{"name":370,"orcid":371},"Caixia Song","https:\u002F\u002Forcid.org\u002F0000-0003-3897-7629",{"name":373,"orcid":8},"Lei Mu",{"name":375,"orcid":8},"Zichen Zhang",{"name":377,"orcid":378},"Rui Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-8634-3519",{"name":380,"orcid":381},"Tong Xu","https:\u002F\u002Forcid.org\u002F0000-0001-5564-192X",{"name":383,"orcid":8},"Zhiyuan Zhang",{"tldr":385,"method":386,"finding":387,"direction":269,"opportunity":388},"综述机器视觉在精准农业中的应用现状与未来趋势。","文献综述，梳理机器视觉在精准农业中的技术路线。","机器视觉已广泛用于作物监测、病虫害识别等，但落地仍受数据与算力限制。","可聚焦轻量化模型与边缘部署，解决田间实时性与数据稀缺问题。","2026-09-17T23:30:54.103781Z"]