[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3782":3,"related-3782":105},{"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":9,"secondary_topics":32,"classification":33,"search_phrases":80,"slug":83,"view_count":22,"doi":84,"paper":85,"created_at":104},3782,"AI-driven assessment of forest dynamics and ecosystem services in response to land use change: current progress and future perspectives","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fffgc.2026.1933500","Forests cover approximately 31% of the global land surface and provide critical ecosystem services including carbon sequestration, hydrological regulation, and biodiversity support, yet ongoing deforestation and fragmentation are progressively eroding these services. The convergence of artificial intelligence (AI), multi-source remote sensing, and cloud computing offers unprecedented opportunities to monitor forest dynamics and quantify ecosystem service responses to land use change, but the literature remains fragmented, with no synthesis tracing the causal chain from land use drivers through forest responses to ecosystem service outcomes. This review addresses that gap through an original four-tier framework encompassing monitoring, assessment, prediction, and optimization, clarifying how AI and geospatial technologies perform at each stage and how outputs of one tier feed into the next. We searched Web of Science, Scopus, and Google Scholar for studies published between 2016 and 2026; screening against explicit eligibility criteria and a formal quality appraisal confirmed 27 core studies directly reporting a quantified AI\u002FML performance metric, supplemented by 70 contextual references. At the monitoring tier, deep learning architectures such as U-Net achieve F1-scores of 0.75–0.77 for forest change detection, with an advantage over conventional classifiers that is conditional rather than absolute, varying with label quality, spectral availability, and geographic transfer. At the assessment tier, AI has advanced carbon stock estimation through multi-source fusion, species distribution modeling in which convolutional neural networks substantially outperform Maximum Entropy models, an advantage that persists and even widens under stricter spatial cross-validation even as both methods' absolute accuracy declines, and hydrological service quantification coupled with process-based models. At the prediction tier, machine learning-enhanced land use simulation has reached accuracies exceeding 0.93, while reinforcement learning shows potential to jointly optimize ecosystem service and economic objectives, though only at proof-of-concept stage. Persistent challenges include limited explainability, near-absent uncertainty quantification, weak cross-domain transferability, and largely sequential AI-process model integration, with direct consequences for policy applications: AI-derived carbon stock maps and ecosystem service assessments intended to inform emissions trading, conservation planning, or land use policy currently lack the quantified uncertainty and cross-region validation such applications require. We propose a phased research agenda spanning near-term methodological consolidation, medium-term foundation model adaptation, and long-term integrated forest digital twins with equitable global coverage, offering a roadmap for translating AI and geospatial technologies into evidence-based forest ecosystem stewardship.","森林覆盖全球约31%的陆地表面，提供碳固存、水文调节和生物多样性维持等关键生态系统服务，但持续的森林砍伐和破碎化正在逐步侵蚀这些服务。人工智能（AI）、多源遥感与云计算的融合为监测森林动态和量化生态系统服务对土地利用变化的响应提供了前所未有的机遇，但相关文献仍较为分散，尚无综合研究追溯从土地利用驱动因素到森林响应再到生态系统服务结果的因果链。本综述通过一个原创的四层框架——监测、评估、预测和优化——来填补这一空白，阐明AI和地理空间技术在各阶段的表现以及某一层的输出如何输入到下一层。我们检索了Web of Science、Scopus和Google Scholar中2016年至2026年间发表的研究；依据明确的纳入标准进行筛选并经正式质量评估，最终确认27项直接报告量化AI\u002FML性能指标的核心研究，并辅以70篇背景参考文献。在监测层，U-Net等深度学习架构在森林变化检测中达到0.75–0.77的F1分数，其相对于传统分类器的优势是有条件的而非绝对的，随标签质量、光谱可用性和地理迁移性而变化。在评估层，AI通过多源融合推进了碳储量估算、物种分布建模——其中卷积神经网络显著优于最大熵模型，且这一优势在更严格的空间交叉验证下持续存在甚至扩大，尽管两种方法的绝对精度均有所下降——以及耦合过程模型的水文服务量化。在预测层，机器学习增强的土地利用模拟精度已超过0.93，而强化学习展现出联合优化生态系统服务和经济目标的潜力，但仍处于概念验证阶段。持续存在的挑战包括可解释性有限、不确定性量化几乎缺失、跨域迁移能力薄弱以及AI与过程模型的整合多为顺序式，这些问题对政策应用具有直接影响：旨在为排放目标提供信息的AI衍生碳储量图和生态系统服务评估……",null,"Frontiers in Forests and Global Change","2026-09-28T00:00:00Z","论文",10,false,82,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,19,14,9,1,"系统综述AI与遥感在森林动态及生态系统服务评估中的四层框架，方法学增量明确，但属国际期刊综述、非国内三农政策事件，适合主题聚合而非头条精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业人工智能","数字孪生","林业遥感","土地利用变化","生态系统服务",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":79},[39,49,59,67,74],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":48},"H6",11,true,[],[45,31,46,47],"生物多样性","biodiversity","ecosystem service",[30,31],{"code":50,"score":13,"supported":42,"title":51,"abstract":52,"tags":58},"I2",[],[53,54,55,56,57],"深度学习","机器学习","machine learning","deep learning","artificial intelligence",[27],{"code":60,"score":61,"supported":42,"title":62,"abstract":63,"tags":66},"I3",6,[],[64,65],"遥感","remote sensing",[],{"code":68,"score":69,"supported":42,"title":70,"abstract":71,"tags":73},"H4",3,[],[72],"carbon sequestration",[],{"code":75,"score":22,"supported":14,"title":76,"abstract":77,"tags":78},"I1",[],[],[28],"农业相关性需要复核",[81,82],"森林动态 AI 遥感","生态系统服务 土地利用变化","森林动态AI遥感-3782","10.3389\u002Fffgc.2026.1933500",{"doi":84,"openalex_id":86,"authors":87,"venue":10,"cited_by_count":94,"oa_url":95,"card":96,"direction":102,"ingested_from":103},"W7214677634",[88,90,92],{"name":89,"orcid":9},"Lili Zhao",{"name":91,"orcid":9},"Xuncheng Fan",{"name":93,"orcid":9},"Xiao Shihong",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fforests-and-global-change\u002Farticles\u002F10.3389\u002Fffgc.2026.1933500\u002Fpdf",{"tldr":97,"method":98,"finding":99,"direction":100,"opportunity":101},"综述AI结合遥感监测森林动态与生态系统服务，提出监测-评估-预测-优化四层框架。","检索2016-2026年文献，筛选27项核心研究，按四层框架归纳AI\u002FML方法。","深度学习提升森林变化检测与碳储量估算，但可解释性、不确定性量化和跨域迁移仍不足。","农业遥感与作物表型","可探索AI与过程模型耦合的不确定性量化及跨区域迁移，支撑森林碳汇政策评估。","智慧农业 \u002F 农业物联网","openalex","2026-09-29T23:30:15.293149Z",{"total":61,"page":22,"page_size":61,"items":106},[107,194,248,293,339,415],{"id":108,"title":109,"url":110,"summary":111,"summary_zh":112,"content":9,"source_name":113,"source_url":110,"published_at":114,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":115,"score_detail":116,"sources":119,"tags":121,"primary_topic":125,"secondary_topics":130,"classification":131,"search_phrases":175,"slug":178,"view_count":94,"doi":179,"paper":180,"created_at":193},5388,"A digital twin framework for proactive disease management in paddy cultivation using spatio-temporal graph neural networks","https:\u002F\u002Fdoi.org\u002F10.1080\u002F20421338.2026.2707951","Rice production is highly affected by disease outbreaks, particularly in Asian agricultural systems, resulting in major yield losses and excessive pesticide usage. This study proposes a digital twin framework for paddy cultivation that integrates predictive disease modelling with real-time crop phenology simulation to support proactive disease management. The framework utilizes historical phenological records, IoT-based microclimatic sensor data, irrigation information, and field management records collected from 150 hectares of paddy fields in Maharashtra, India. A Spatio-Temporal Graph Neural Network (ST-GNN), integrating Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks, is employed to model spatial disease propagation and temporal crop dynamics for predicting blast, brown planthopper, and bacterial leaf blight outbreaks up to 14 days in advance. Experimental results demonstrate 87.3% prediction accuracy, 23.7% reduction in pesticide usage, and 31.2% reduction in crop losses, while outperforming traditional Random Forest and SVM models in F1-score performance. The proposed framework enables real-time decision support for sustainable precision agriculture and optimized resource management.","水稻生产极易受到病害暴发的影响，在亚洲农业体系中尤为突出，造成重大产量损失和农药过度使用。本研究提出一个水稻栽培数字孪生（digital twin）框架，将预测性病害建模与实时作物物候模拟相结合，以支持主动式病害管理。该框架利用从印度马哈拉施特拉邦150公顷稻田采集的历史物候记录、基于物联网的微气候传感器数据、灌溉信息及田间管理记录。采用融合图卷积网络（Graph Convolutional Networks, GCN）与长短期记忆网络（Long Short-Term Memory, LSTM）的时空图神经网络（Spatio-Temporal Graph Neural Network, ST-GNN），对病害空间传播与作物时序动态进行建模，以提前14天预测稻瘟病、褐飞虱和白叶枯病的暴发。实验结果表明，预测准确率达87.3%，农药使用量减少23.7%，作物损失减少31.2%，且在F1分数上优于传统随机森林（Random Forest）和支持向量机（SVM）模型。所提框架可为可持续精准农业和优化资源管理提供实时决策支持。","African Journal of Science Technology Innovation and Development","2026-10-19T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":117,"freshness":21,"relevant":22,"comment":118},13,"方法新颖、数据规模明确且量化效果显著，属智慧农业细分领域实质进展，值得进入每日精选。",[120],{"name":113,"url":110},[122,27,123,28,124],"智慧农业","水稻","病虫害预警",{"code":126,"slug":127,"name":123,"path":128,"parent_code":129},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":132,"locked":14,"version":35,"method":36,"evidence":133},"classified",{"candidates":134,"agricultural_evidence":42},[135,142,148,155,162,169],{"code":75,"score":19,"supported":42,"title":136,"abstract":138,"tags":141},[137],"digital twin",[139,28,140,137],"精准农业","precision agriculture",[28,122],{"code":126,"score":17,"supported":42,"title":143,"abstract":145,"tags":147},[144],"paddy",[123,146,127,144],"稻田",[123],{"code":149,"score":61,"supported":42,"title":150,"abstract":151,"tags":154},"F4",[],[152,153],"农药","pesticide",[],{"code":156,"score":61,"supported":42,"title":157,"abstract":158,"tags":161},"H3",[],[159,160],"灌溉","irrigation",[],{"code":163,"score":61,"supported":42,"title":164,"abstract":165,"tags":168},"I5",[],[166,167],"物联网","IoT",[],{"code":50,"score":170,"supported":42,"title":171,"abstract":172,"tags":174},4,[],[173],"decision support",[27],[176,177],"水稻 病害预测 数字孪生","ST-GNN 水稻 病虫害","水稻病害预测数字孪生-5388","10.1080\u002F20421338.2026.2707951",{"doi":179,"openalex_id":181,"authors":182,"venue":113,"cited_by_count":94,"oa_url":9,"card":187,"direction":102,"ingested_from":103},"W7222969403",[183,185],{"name":184,"orcid":9},"Jyoti Rani",{"name":186,"orcid":9},"Dr.  Narendra S.  Jadhav",{"tldr":188,"method":189,"finding":190,"direction":191,"opportunity":192},"构建水稻数字孪生框架，用时空图神经网络提前14天预测病害。","融合物联网微气候、物候与管理数据，用GCN+LSTM的ST-GNN建模。","预测准确率87.3%，农药减23.7%、损失减31.2%，优于RF与SVM。","农业人工智能与决策模型","可探索多尺度时空图建模与数字孪生闭环决策，并迁移至其他作物与区域。","2026-10-20T23:30:12.636299Z",{"id":195,"title":196,"url":197,"summary":198,"summary_zh":199,"content":9,"source_name":200,"source_url":197,"published_at":114,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":201,"score_detail":202,"sources":204,"tags":206,"primary_topic":9,"secondary_topics":209,"classification":210,"search_phrases":232,"slug":235,"view_count":94,"doi":236,"paper":237,"created_at":247},5317,"PLANT-GPK v4.0: A Unified Time-Discovery Framework for Multi-Stage Plant Phenotyping","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23460911","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 future harvest windows 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，该模型成功发现了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）的物理学，提供了一个完整的数字孪生体，无论植物当前处于何种状态，均能预测未来的收获窗口。","Zenodo (CERN European Organization for Nuclear Research)",81,{"impact":17,"substance":18,"depth":17,"authority":117,"freshness":13,"relevant":22,"comment":203},"该研究提出统一时间发现框架，可从单幅图像反演植物生育阶段并预测收获窗口，方法新颖且具数字孪生应用潜力，值得进入每日精选。",[205],{"name":200,"url":197},[122,27,207,28,208],"小麦","植物表型",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":211},{"candidates":212,"agricultural_evidence":42,"reason":231},[213,218,225],{"code":75,"score":214,"supported":42,"title":215,"abstract":216,"tags":217},8,[],[28,137],[28,122],{"code":219,"score":220,"supported":42,"title":221,"abstract":222,"tags":224},"A2",7,[],[207,223],"wheat",[207],{"code":50,"score":220,"supported":42,"title":226,"abstract":227,"tags":230},[],[228,229],"计算机视觉","computer vision",[27],"多个产业证据相当，需要确认主归属",[233,234],"PLANT-GPK 植物表型","小麦 数字孪生 生育期","PLANT-GPK植物表型-5317","10.5281\u002Fzenodo.23460911",{"doi":236,"openalex_id":238,"authors":239,"venue":200,"cited_by_count":94,"oa_url":197,"card":242,"direction":100,"ingested_from":103},"W7222895088",[240],{"name":241,"orcid":9},"MOHAMMED TAHAR SAIM",{"tldr":243,"method":244,"finding":245,"direction":100,"opportunity":246},"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，实现跨发育阶段表型分析。","基于ODE物理模型的反问题优化，联合估计生理参数与时间t，用两个小麦样本验证。","模型成功发现营养期与衰老期小麦的精确年龄，可预测收获窗口。","单图跨阶段时间反演可延伸至多作物多生育期数字孪生与收获期预测的通用化研究。","2026-10-19T23:30:19.498798Z",{"id":249,"title":250,"url":251,"summary":252,"summary_zh":253,"content":9,"source_name":200,"source_url":251,"published_at":114,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":254,"score_detail":255,"sources":258,"tags":260,"primary_topic":261,"secondary_topics":266,"classification":267,"search_phrases":279,"slug":281,"view_count":94,"doi":282,"paper":283,"created_at":292},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。",79,{"impact":17,"substance":18,"depth":17,"authority":256,"freshness":21,"relevant":22,"comment":257},12,"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。",[259],{"name":200,"url":251},[122,27,28,139,208],{"code":75,"slug":262,"name":263,"path":264,"parent_code":265},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":132,"locked":14,"version":35,"method":36,"evidence":268},{"candidates":269,"agricultural_evidence":42},[270,275],{"code":75,"score":271,"supported":42,"title":272,"abstract":273,"tags":274},20,[137],[139,28,140],[28,122,139],{"code":50,"score":22,"supported":14,"title":276,"abstract":277,"tags":278},[],[],[27],[233,280],"空间生理热图 数字孪生","PLANT-GPK植物表型-5316","10.5281\u002Fzenodo.23460273",{"doi":282,"openalex_id":284,"authors":285,"venue":200,"cited_by_count":94,"oa_url":251,"card":287,"direction":100,"ingested_from":103},"W7222885652",[286],{"name":241,"orcid":9},{"tldr":288,"method":289,"finding":290,"direction":100,"opportunity":291},"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。","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":294,"title":295,"url":296,"summary":297,"summary_zh":298,"content":9,"source_name":200,"source_url":296,"published_at":114,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":201,"score_detail":299,"sources":301,"tags":303,"primary_topic":305,"secondary_topics":309,"classification":310,"search_phrases":325,"slug":327,"view_count":94,"doi":328,"paper":329,"created_at":338},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。",{"impact":17,"substance":18,"depth":17,"authority":117,"freshness":13,"relevant":22,"comment":300},"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。",[302],{"name":200,"url":296},[122,27,28,208,304],"收获预测",{"code":50,"slug":306,"name":307,"path":308,"parent_code":265},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":132,"locked":14,"version":35,"method":36,"evidence":311},{"candidates":312,"agricultural_evidence":42},[313,317,321],{"code":75,"score":214,"supported":42,"title":314,"abstract":315,"tags":316},[],[28,137],[28,122],{"code":50,"score":220,"supported":42,"title":318,"abstract":319,"tags":320},[],[228,229],[27],{"code":219,"score":61,"supported":42,"title":322,"abstract":323,"tags":324},[],[207,223],[],[233,326],"小麦 收获期 预测","PLANT-GPK植物表型-5315","10.5281\u002Fzenodo.23461121",{"doi":328,"openalex_id":330,"authors":331,"venue":200,"cited_by_count":94,"oa_url":296,"card":333,"direction":100,"ingested_from":103},"W7222875391",[332],{"name":241,"orcid":9},{"tldr":334,"method":335,"finding":336,"direction":100,"opportunity":337},"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。","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":340,"title":341,"url":342,"summary":343,"summary_zh":344,"content":9,"source_name":345,"source_url":342,"published_at":346,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":347,"score_detail":348,"sources":350,"tags":352,"primary_topic":355,"secondary_topics":356,"classification":357,"search_phrases":398,"slug":401,"view_count":94,"doi":402,"paper":403,"created_at":414},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。","Discover Artificial Intelligence","2026-10-17T00:00:00Z",83,{"impact":18,"substance":18,"depth":17,"authority":256,"freshness":21,"relevant":22,"comment":349},"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。",[351],{"name":345,"url":342},[122,27,353,28,354],"大语言模型","气候智慧农业",{"code":50,"slug":306,"name":307,"path":308,"parent_code":265},[],{"status":132,"locked":14,"version":35,"method":36,"evidence":358},{"candidates":359,"agricultural_evidence":42},[360,365,372,376,381,387,393],{"code":50,"score":19,"supported":42,"title":361,"abstract":362,"tags":364},[57],[228,57,229,363,173],"large language model",[27,353],{"code":366,"score":61,"supported":42,"title":367,"abstract":368,"tags":371},"H2",[],[369,370],"土壤","soil",[],{"code":156,"score":61,"supported":42,"title":373,"abstract":374,"tags":375},[],[159,160],[],{"code":75,"score":377,"supported":42,"title":378,"abstract":379,"tags":380},5,[],[28],[28,122],{"code":382,"score":69,"supported":42,"title":383,"abstract":384,"tags":386},"F6",[],[385],"农业机器人",[],{"code":388,"score":69,"supported":42,"title":389,"abstract":390,"tags":392},"G2",[],[391],"smallholder",[],{"code":394,"score":22,"supported":14,"title":395,"abstract":396,"tags":397},"H7",[],[],[354],[399,400],"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","10.1007\u002Fs44163-026-02540-1",{"doi":402,"openalex_id":404,"authors":405,"venue":345,"cited_by_count":94,"oa_url":342,"card":409,"direction":102,"ingested_from":103},"W7222823256",[406],{"name":407,"orcid":408},"Mostafa Farajpour","https:\u002F\u002Forcid.org\u002F0000-0003-4223-502X",{"tldr":410,"method":411,"finding":412,"direction":191,"opportunity":413},"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。","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。","2026-10-18T23:30:07.666127Z",{"id":416,"title":417,"url":418,"summary":419,"summary_zh":420,"content":9,"source_name":421,"source_url":418,"published_at":422,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":423,"score_detail":424,"sources":427,"tags":429,"primary_topic":433,"secondary_topics":437,"classification":443,"search_phrases":468,"slug":471,"view_count":94,"doi":472,"paper":473,"created_at":494},5096,"Land Cover Change, Ecosystem Service Implications, and Economy–Land Use Decoupling:","https:\u002F\u002Fdoi.org\u002F10.14246\u002Firspsd.14.4_186","Land cover change plays a critical role in shaping watershed ecological conditions and supporting regional economic activities. This study analyzes land cover dynamics in the Tiworo Watershed, Southeast Sulawesi, Indonesia, for 2014, 2019, and 2024, and examines their relationship with sectoral economic growth. Remote sensing and GIS were used to map and quantify land cover changes, while sectoral Gross Regional Domestic Product (GRDP) data for agriculture, forestry, and fisheries represented economic performance. Economy–land use interactions were assessed using the Tapio decoupling elasticity index. Economy–land use interactions were assessed using the Tapio decoupling elasticity index as a retrospective empirical measure based on observed changes in land cover and sectoral GRDP over the study periods. The results show increasing sectoral GRDP accompanied by relatively limited changes in dominant land cover classes. Absolute decoupling was observed during 2014–2019, relative decoupling during 2019–2024, and overall absolute decoupling for 2014–2024, indicating that economic growth was not proportionally associated with large-scale land conversion. Spatial analysis reveals localized landscape transformations, including mangrove degradation, settlement expansion, and plantation growth, which signal emerging pressures on environmentally sensitive areas. Although ecosystem services are not directly valued, land cover change is interpreted as an indicator of ecological pressure with implications for provisioning ecosystem services. These findings highlight the importance of integrating land-use monitoring and economic indicators to support adaptive watershed management, spatial planning, and sustainable development.","土地覆盖变化在塑造流域生态状况和支撑区域经济活动方面发挥着关键作用。本研究分析了印度尼西亚东南苏拉威西省蒂沃罗流域（Tiworo Watershed）2014年、2019年和2024年的土地覆盖动态，并考察其与部门经济增长的关系。研究采用遥感和地理信息系统（GIS）对土地覆盖变化进行制图和量化，同时以农业、林业和渔业的部门地区生产总值（GRDP）数据表征经济表现。经济与土地利用的相互作用采用Tapio脱钩弹性指数进行评估，该指数作为基于研究期内观测到的土地覆盖变化和部门GRDP变化的回顾性经验测度。结果表明，部门GRDP持续增长，而主导土地覆盖类型的变化相对有限。2014—2019年呈现绝对脱钩，2019—2024年呈现相对脱钩，2014—2024年整体呈现绝对脱钩，表明经济增长与大规模土地转换之间不存在比例关联。空间分析揭示了局部景观转型，包括红树林退化、居民点扩张和种植园增长，这些信号表明环境敏感区域正面临新兴压力。尽管生态系统服务未被直接估值，但土地覆盖变化被解释为生态压力的指标，并对供给型生态系统服务具有潜在影响。这些发现凸显了整合土地利用监测与经济指标的重要性，以支持适应性流域管理、空间规划和可持续发展。","International Review for Spatial Planning and Sustainable Development","2026-10-14T00:00:00Z",63,{"impact":214,"substance":17,"depth":425,"authority":117,"freshness":214,"relevant":22,"comment":426},16,"基于遥感与GIS的流域土地利用变化与经济增长脱钩研究，方法规范但属区域案例，对国内三农信息化参考价值有限。",[428],{"name":421,"url":418},[430,30,431,31,432],"遥感监测","流域管理","空间规划",{"code":40,"slug":46,"name":434,"path":435,"parent_code":436},"生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity","H",[438],{"code":439,"slug":440,"name":441,"path":442,"parent_code":265},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":132,"locked":14,"version":35,"method":36,"evidence":444},{"candidates":445,"agricultural_evidence":42},[446,450,458,462],{"code":40,"score":117,"supported":42,"title":447,"abstract":448,"tags":449},[47],[31],[30,31],{"code":439,"score":13,"supported":42,"title":451,"abstract":452,"tags":457},[],[453,454,455,456],"地理信息","空间分析","GIS","spatial analysis",[432],{"code":60,"score":220,"supported":42,"title":459,"abstract":460,"tags":461},[],[64,65],[430],{"code":463,"score":69,"supported":42,"title":464,"abstract":465,"tags":467},"D",[],[466],"渔业",[],[469,470],"Tiworo流域 土地利用 遥感","Tapio脱钩 农业经济 土地覆盖","Tiworo流域土地利用遥感-5096","10.14246\u002Firspsd.14.4_186",{"doi":472,"openalex_id":474,"authors":475,"venue":421,"cited_by_count":94,"oa_url":488,"card":489,"direction":100,"ingested_from":103},"W7221789653",[476,479,482,485],{"name":477,"orcid":478},"Safril Kasim","https:\u002F\u002Forcid.org\u002F0009-0009-4679-4233",{"name":480,"orcid":481},"Hasddin Hasddin","https:\u002F\u002Forcid.org\u002F0000-0002-0114-5274",{"name":483,"orcid":484},"La Fariki","https:\u002F\u002Forcid.org\u002F0009-0003-7870-9028",{"name":486,"orcid":487},"Abhishek Agarwal","https:\u002F\u002Forcid.org\u002F0000-0002-0900-6383","https:\u002F\u002Fwww.jstage.jst.go.jp\u002Farticle\u002Firspsd\u002F14\u002F4\u002F14_9\u002F_pdf",{"tldr":490,"method":491,"finding":492,"direction":100,"opportunity":493},"分析印尼Tiworo流域2014-2024年土地覆盖变化与部门经济增长的脱钩关系。","遥感与GIS制图，结合GRDP数据和Tapio脱钩弹性指数。","经济持续增长但土地覆盖变化有限，整体呈绝对脱钩，局部红树林退化等生态压力显现。","可将脱钩指数与生态系统服务价值核算结合，量化流域生态压力并支撑空间规划。","2026-10-16T23:30:28.687070Z"]