[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5084":3,"related-5084":75},{"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":18,"tags":20,"primary_topic":9,"secondary_topics":25,"classification":26,"search_phrases":55,"slug":58,"view_count":15,"doi":59,"paper":60,"created_at":74},5084,"Data-Driven Catalytic Renewal Strategy for Village-in-City Regeneration: A Case Study of Ancestral Hall-Dominated Shipai Village, Guangzhou","https:\u002F\u002Fdoi.org\u002F10.70088\u002Fzdpbwx76","Urban villages in China represent a critical urban challenge, characterized by physical overcrowding, severe infrastructural deficits, and the progressive erosion of indigenous cultural heritage. This study investigates whether ancestral halls can serve as effective urban catalysts to trigger a phased, bottom-up renewal process in Guangzhou's urban villages, using Shipai Village as a comprehensive case study. Employing a mixed-methods approach combining participatory observation, semi-structured interviews, and a detailed questionnaire survey, the research reveals that ancestral halls, despite being among the few high-quality public spaces in the village, remain significantly underutilized and spatially segregated. Specifically, indoor spaces are reserved primarily for local villagers, while migrants are relegated to outdoor areas. Residents across both populations express low satisfaction with cultural life and a shared desire for more and better public leisure spaces, creating a vital social consensus that can support renewal initiatives. The research proposes a four-stage catalytic model including analysis and diagnosis, implantation of the catalyst, chain reaction and linkage, and the establishment of branch ancestral halls that integrates heritage preservation with functional improvement through modular, adaptable interventions. The strategy further incorporates IoT-enabled space monitoring and data-driven decision support, transforming ancestral halls into green or cultural nodes that combine heritage value, environmental sustainability, and digital connectivity. This approach offers a replicable framework for heritage-led, socially inclusive, and data-informed urban renewal in similar contexts across China.","中国的城中村是一项严峻的城市挑战，其特征表现为物理空间过度拥挤、基础设施严重不足以及本土文化遗产的逐步侵蚀。本研究以石牌村为综合案例，探讨祠堂能否作为有效的城市触媒，在广州城中村中触发分阶段的、自下而上的更新过程。研究采用参与式观察、半结构化访谈和详细问卷调查相结合的混合方法，揭示出祠堂虽然是村内为数不多的高品质公共空间，却仍处于显著利用不足和空间隔离的状态。具体而言，室内空间主要保留给本地村民，而外来务工人员则被局限于室外区域。两类居民群体均对文化生活表示低满意度，并共同期望更多、更好的公共休闲空间，从而形成了能够支撑更新倡议的重要社会共识。研究提出一个四阶段触媒模型，包括分析与诊断、触媒植入、连锁反应与联动，以及分支祠堂的建立，通过模块化、适应性干预将遗产保护与功能提升相结合。该策略进一步融入物联网空间监测和数据驱动的决策支持，将祠堂转化为兼具遗产价值、环境可持续性和数字连接性的绿色或文化节点。这一方法为中国类似语境下以遗产为导向、社会包容且数据驱动的城市更新提供了可复制的框架。",null,"GBP Proceedings Series","2026-10-14T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究城中村祠堂驱动的城市更新与IoT监测，属城市规划与文化遗产领域，与三农、农业信息化、数字乡村主题无关，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"数字孪生","城市更新","文化遗产保护","城中村改造",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":54},[32,41,48],{"code":33,"score":34,"supported":35,"title":36,"abstract":37,"tags":40},"I5",6,true,[],[38,39],"物联网","IoT",[],{"code":42,"score":43,"supported":35,"title":44,"abstract":45,"tags":47},"I2",3,[],[46],"decision support",[],{"code":49,"score":50,"supported":14,"title":51,"abstract":52,"tags":53},"I1",1,[],[],[21],"农业相关性需要复核",[56,57],"广州 石牌村 祠堂 城市更新","文化遗产保护 城中村改造 城市更新 数字孪生","广州石牌村祠堂城市更新-5084","10.70088\u002Fzdpbwx76",{"doi":59,"openalex_id":61,"authors":62,"venue":10,"cited_by_count":15,"oa_url":65,"card":66,"direction":72,"ingested_from":73},"W7222096097",[63],{"name":64,"orcid":9},"Chendi Li","https:\u002F\u002Fsoapubs.com\u002Findex.php\u002FGBPPS\u002Farticle\u002Fdownload\u002F3079\u002F2792",{"tldr":67,"method":68,"finding":69,"direction":70,"opportunity":71},"以广州石牌村为例，提出以祠堂为触媒、数据驱动的城中村分阶段更新策略。","参与式观察、半结构化访谈与问卷调查，结合物联网空间监测与数据决策支持。","祠堂虽为优质公共空间却利用率低且本地人与外来者空间隔离，居民普遍渴望更多公共休闲空间。","数字乡村与农业信息化","可探索物联网监测与数据决策在文化遗产触媒更新中的量化评估与可复制机制。","智慧农业 \u002F 农业物联网","openalex","2026-10-16T23:30:12.101245Z",{"total":34,"page":50,"page_size":34,"items":76},[77,165,220,265,311,388],{"id":78,"title":79,"url":80,"summary":81,"summary_zh":82,"content":9,"source_name":83,"source_url":80,"published_at":84,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":85,"score_detail":86,"sources":92,"tags":94,"primary_topic":99,"secondary_topics":104,"classification":105,"search_phrases":146,"slug":149,"view_count":15,"doi":150,"paper":151,"created_at":164},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":87,"substance":88,"depth":87,"authority":89,"freshness":90,"relevant":50,"comment":91},18,22,13,9,"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。",[93],{"name":83,"url":80},[95,96,97,21,98],"智慧农业","农业人工智能","水稻","病虫害预警",{"code":100,"slug":101,"name":97,"path":102,"parent_code":103},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":106,"locked":14,"version":28,"method":29,"evidence":107},"classified",{"candidates":108,"agricultural_evidence":35},[109,117,123,130,137,141],{"code":49,"score":110,"supported":35,"title":111,"abstract":113,"tags":116},19,[112],"digital twin",[114,21,115,112],"精准农业","precision agriculture",[21,95],{"code":100,"score":87,"supported":35,"title":118,"abstract":120,"tags":122},[119],"paddy",[97,121,101,119],"稻田",[97],{"code":124,"score":34,"supported":35,"title":125,"abstract":126,"tags":129},"F4",[],[127,128],"农药","pesticide",[],{"code":131,"score":34,"supported":35,"title":132,"abstract":133,"tags":136},"H3",[],[134,135],"灌溉","irrigation",[],{"code":33,"score":34,"supported":35,"title":138,"abstract":139,"tags":140},[],[38,39],[],{"code":42,"score":142,"supported":35,"title":143,"abstract":144,"tags":145},4,[],[46],[96],[147,148],"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":150,"openalex_id":152,"authors":153,"venue":83,"cited_by_count":15,"oa_url":9,"card":158,"direction":72,"ingested_from":73},"W7222969403",[154,156],{"name":155,"orcid":9},"Jyoti Rani",{"name":157,"orcid":9},"Dr.  Narendra S.  Jadhav",{"tldr":159,"method":160,"finding":161,"direction":162,"opportunity":163},"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":166,"title":167,"url":168,"summary":169,"summary_zh":170,"content":9,"source_name":171,"source_url":168,"published_at":84,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":172,"score_detail":173,"sources":175,"tags":177,"primary_topic":9,"secondary_topics":180,"classification":181,"search_phrases":203,"slug":206,"view_count":15,"doi":207,"paper":208,"created_at":219},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":87,"substance":88,"depth":87,"authority":89,"freshness":13,"relevant":50,"comment":174},"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。",[176],{"name":171,"url":168},[95,96,178,21,179],"小麦","植物表型",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":182},{"candidates":183,"agricultural_evidence":35,"reason":202},[184,189,196],{"code":49,"score":185,"supported":35,"title":186,"abstract":187,"tags":188},8,[],[21,112],[21,95],{"code":190,"score":191,"supported":35,"title":192,"abstract":193,"tags":195},"A2",7,[],[178,194],"wheat",[178],{"code":42,"score":191,"supported":35,"title":197,"abstract":198,"tags":201},[],[199,200],"计算机视觉","computer vision",[96],"多个产业证据相当，需要确认主归属",[204,205],"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":207,"openalex_id":209,"authors":210,"venue":171,"cited_by_count":15,"oa_url":168,"card":213,"direction":217,"ingested_from":73},"W7222895088",[211],{"name":212,"orcid":9},"MOHAMMED TAHAR SAIM",{"tldr":214,"method":215,"finding":216,"direction":217,"opportunity":218},"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。","农业遥感与作物表型","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":221,"title":222,"url":223,"summary":224,"summary_zh":225,"content":9,"source_name":171,"source_url":223,"published_at":84,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":226,"score_detail":227,"sources":230,"tags":232,"primary_topic":233,"secondary_topics":238,"classification":239,"search_phrases":251,"slug":253,"view_count":15,"doi":254,"paper":255,"created_at":264},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":87,"substance":88,"depth":87,"authority":228,"freshness":90,"relevant":50,"comment":229},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。",[231],{"name":171,"url":223},[95,96,21,114,179],{"code":49,"slug":234,"name":235,"path":236,"parent_code":237},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":106,"locked":14,"version":28,"method":29,"evidence":240},{"candidates":241,"agricultural_evidence":35},[242,247],{"code":49,"score":243,"supported":35,"title":244,"abstract":245,"tags":246},20,[112],[114,21,115],[21,95,114],{"code":42,"score":50,"supported":14,"title":248,"abstract":249,"tags":250},[],[],[96],[204,252],"空间生理热图 数字孪生","PLANT-GPK植物表型-5316","10.5281\u002Fzenodo.23460273",{"doi":254,"openalex_id":256,"authors":257,"venue":171,"cited_by_count":15,"oa_url":223,"card":259,"direction":217,"ingested_from":73},"W7222885652",[258],{"name":212,"orcid":9},{"tldr":260,"method":261,"finding":262,"direction":217,"opportunity":263},"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":266,"title":267,"url":268,"summary":269,"summary_zh":270,"content":9,"source_name":171,"source_url":268,"published_at":84,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":172,"score_detail":271,"sources":273,"tags":275,"primary_topic":277,"secondary_topics":281,"classification":282,"search_phrases":297,"slug":299,"view_count":15,"doi":300,"paper":301,"created_at":310},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":87,"substance":88,"depth":87,"authority":89,"freshness":13,"relevant":50,"comment":272},"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。",[274],{"name":171,"url":268},[95,96,21,179,276],"收获预测",{"code":42,"slug":278,"name":279,"path":280,"parent_code":237},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":106,"locked":14,"version":28,"method":29,"evidence":283},{"candidates":284,"agricultural_evidence":35},[285,289,293],{"code":49,"score":185,"supported":35,"title":286,"abstract":287,"tags":288},[],[21,112],[21,95],{"code":42,"score":191,"supported":35,"title":290,"abstract":291,"tags":292},[],[199,200],[96],{"code":190,"score":34,"supported":35,"title":294,"abstract":295,"tags":296},[],[178,194],[],[204,298],"小麦 收获期 预测","PLANT-GPK植物表型-5315","10.5281\u002Fzenodo.23461121",{"doi":300,"openalex_id":302,"authors":303,"venue":171,"cited_by_count":15,"oa_url":268,"card":305,"direction":217,"ingested_from":73},"W7222875391",[304],{"name":212,"orcid":9},{"tldr":306,"method":307,"finding":308,"direction":217,"opportunity":309},"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":312,"title":313,"url":314,"summary":315,"summary_zh":316,"content":9,"source_name":317,"source_url":314,"published_at":318,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":319,"score_detail":320,"sources":322,"tags":324,"primary_topic":327,"secondary_topics":328,"classification":329,"search_phrases":371,"slug":374,"view_count":15,"doi":375,"paper":376,"created_at":387},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":88,"substance":88,"depth":87,"authority":228,"freshness":90,"relevant":50,"comment":321},"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。",[323],{"name":317,"url":314},[95,96,325,21,326],"大语言模型","气候智慧农业",{"code":42,"slug":278,"name":279,"path":280,"parent_code":237},[],{"status":106,"locked":14,"version":28,"method":29,"evidence":330},{"candidates":331,"agricultural_evidence":35},[332,338,345,349,354,360,366],{"code":42,"score":110,"supported":35,"title":333,"abstract":335,"tags":337},[334],"artificial intelligence",[199,334,200,336,46],"large language model",[96,325],{"code":339,"score":34,"supported":35,"title":340,"abstract":341,"tags":344},"H2",[],[342,343],"土壤","soil",[],{"code":131,"score":34,"supported":35,"title":346,"abstract":347,"tags":348},[],[134,135],[],{"code":49,"score":350,"supported":35,"title":351,"abstract":352,"tags":353},5,[],[21],[21,95],{"code":355,"score":43,"supported":35,"title":356,"abstract":357,"tags":359},"F6",[],[358],"农业机器人",[],{"code":361,"score":43,"supported":35,"title":362,"abstract":363,"tags":365},"G2",[],[364],"smallholder",[],{"code":367,"score":50,"supported":14,"title":368,"abstract":369,"tags":370},"H7",[],[],[326],[372,373],"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":375,"openalex_id":377,"authors":378,"venue":317,"cited_by_count":15,"oa_url":314,"card":382,"direction":72,"ingested_from":73},"W7222823256",[379],{"name":380,"orcid":381},"Mostafa Farajpour","https:\u002F\u002Forcid.org\u002F0000-0003-4223-502X",{"tldr":383,"method":384,"finding":385,"direction":162,"opportunity":386},"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":389,"title":390,"url":391,"summary":392,"summary_zh":393,"content":9,"source_name":394,"source_url":391,"published_at":395,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":226,"score_detail":396,"sources":398,"tags":400,"primary_topic":9,"secondary_topics":405,"classification":406,"search_phrases":438,"slug":441,"view_count":15,"doi":442,"paper":443,"created_at":457},5157,"Digital twin forestry as an informatics framework integrating remote sensing ecosystem modelling and data assimilation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44564-026-00029-8","Forests are essential to the Earth system, regulating carbon, water, and biodiversity cycles while underpinning ecosystem resilience. Yet they face mounting pressures from climate and land-use change, requiring integrative informatics approaches that link high-resolution observations with advanced ecosystem models. This systematic review defines Digital Twin Forestry (DTF) as an emerging paradigm that continuously couples multisensor Earth Observation data with ecosystem models through data assimilation, enabling dynamic, uncertainty-aware forecasting and adaptive decision support. Following PRISMA 2020 guidelines, we synthesized 57 peer-reviewed studies published between 2016 and 2025 that combined EO datasets with ecosystem modeling and assimilation frameworks. Demonstrated advances include CubeSat constellations, spaceborne LiDAR, UAV hyperspectral systems, and hybrid machine-learning surrogates. Data assimilation methods such as Ensemble Kalman Filters and Particle Filters serve as computational bridges between multi-temporal observations and dynamic model states. Results reveal uneven biome representation, with tropical and boreal forests underrepresented compared to temperate zones, underscoring equity concerns in biodiversity-rich regions. While most forestry digital twins remain at prototype or early operational stages, continuous coupling of sensing platforms to models demonstrates the potential for near-real-time forecasting that extends beyond static inventories. Future priorities include physics-informed machine learning, FAIR-compliant cloud-native workflows, and expanded monitoring in underrepresented ecosystems. Embedding biodiversity and resilience monitoring into near-real-time, uncertainty-aware workflows will be critical, enabling DTF to evolve into operational systems that provide adaptive, policy-relevant decision support for climate-resilient forest management. This review offers an integrative roadmap for advancing DTF, contributing to global sustainability and supporting the United Nations Sustainable Development Goals, including Climate Action (SDG 13) and Life on Land (SDG 15).","森林对地球系统至关重要，调节着碳、水和生物多样性循环，同时支撑着生态系统韧性。然而，森林面临气候和土地利用变化日益增大的压力，需要将高分辨率观测与先进生态系统模型相联系的综合性信息学方法。本系统综述将数字孪生林业（Digital Twin Forestry, DTF）定义为一个新兴范式，即通过数据同化将多传感器地球观测数据与生态系统模型持续耦合，从而实现动态的、具有不确定性感知的预测和适应性决策支持。遵循PRISMA 2020指南，我们综合了2016年至2025年间发表的57项同行评审研究，这些研究将地球观测数据集与生态系统建模和数据同化框架相结合。已展示的进展包括立方星（CubeSat）星座、星载激光雷达、无人机高光谱系统和混合机器学习代理模型。集合卡尔曼滤波和粒子滤波等数据同化方法充当多时相观测与动态模型状态之间的计算桥梁。结果显示生物群系代表性不均衡，热带和寒带森林相对于温带地区代表性不足，凸显了生物多样性丰富地区的公平性问题。尽管大多数林业数字孪生仍处于原型或早期运行阶段，但传感平台与模型的持续耦合展示了超越静态资源清查的近实时预测潜力。未来优先方向包括物理信息机器学习、符合FAIR原则的云原生工作流程，以及扩大代表性不足生态系统的监测。将生物多样性和韧性监测嵌入近实时、不确定性感知的工作流程将至关重要，使DTF能够发展为可操作的系统，为气候韧性森林管理提供适应性、政策相关的决策支持。本综述为推进DTF提供了综合性路线图，助力全球可持续发展并支持联合国可持续发展目标，包括气候行动（SDG 13）和陆地生物（SDG 15）。","Discover Informatics","2026-10-15T00:00:00Z",{"impact":87,"substance":88,"depth":110,"authority":228,"freshness":185,"relevant":50,"comment":397},"系统综述提出数字孪生林业框架，方法新颖、结论有据，对农业信息化中的遥感与生态建模有参考价值，但属学术综述而非产业级事件。",[399],{"name":394,"url":391},[21,401,402,403,404],"遥感","森林监测","数据同化","生态系统建模",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":407},{"candidates":408,"agricultural_evidence":14,"reason":54},[409,414,421,427,434],{"code":49,"score":410,"supported":35,"title":411,"abstract":412,"tags":413},15,[112],[21,112],[21],{"code":415,"score":228,"supported":35,"title":416,"abstract":418,"tags":420},"I3",[417],"remote sensing",[419],"hyperspectral",[401],{"code":42,"score":90,"supported":35,"title":422,"abstract":423,"tags":426},[],[424,425,46],"机器学习","machine learning",[],{"code":428,"score":34,"supported":35,"title":429,"abstract":430,"tags":433},"H6",[],[431,432],"生物多样性","biodiversity",[],{"code":33,"score":50,"supported":14,"title":435,"abstract":436,"tags":437},[],[],[403],[439,440],"遥感 数据同化 森林","生态系统建模 数字孪生 数据同化 森林监测","遥感数据同化森林-5157","10.1007\u002Fs44564-026-00029-8",{"doi":442,"openalex_id":444,"authors":445,"venue":394,"cited_by_count":15,"oa_url":451,"card":452,"direction":72,"ingested_from":73},"W7221993112",[446,449],{"name":447,"orcid":448},"Niamjit Das","https:\u002F\u002Forcid.org\u002F0000-0001-7619-8306",{"name":450,"orcid":9},"Kevin Andrew","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44564-026-00029-8.pdf",{"tldr":453,"method":454,"finding":455,"direction":217,"opportunity":456},"系统综述数字孪生林业，提出遥感与生态模型同化耦合框架。","PRISMA 2020 综述 57 篇文献，整合多源遥感、生态模型与数据同化。","多数林业数字孪生仍处原型阶段，热带与寒带森林代表性不足。","可探索物理知情机器学习与云原生工作流，填补热带、寒带森林近实时同化监测空白。","2026-10-17T23:30:10.474821Z"]