[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1517":3,"related-1517":82},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":35,"classification":36,"search_phrases":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":81},1517,"Estimation of Agricultural Vegetation Water Stress Using Feature-Optimized Remote Sensing and Ensemble Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102536","Estimation of Agricultural Vegetation Water Stress Using Feature-Optimized Remote Sensing and Ensemble Machine Learning。Smart Agricultural Technology",null,"Smart Agricultural Technology","2026-09-01T00:00:00Z","论文",10,false,72,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},15,20,18,12,7,1,"论文提出特征优化遥感与集成机器学习估算农业植被水分胁迫，方法新颖，数据详实，对精准灌溉有参考价值。",[24],{"name":9,"url":6},[26,27,28,29],"智慧农业","机器学习","遥感","作物水分胁迫",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":37,"locked":13,"version":38,"method":39,"evidence":40},"partial","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44,"reason":60},[42,49,55],{"code":43,"score":19,"supported":44,"title":45,"abstract":47,"tags":48},"I2",true,[46],"machine learning",[46],[27],{"code":50,"score":19,"supported":44,"title":51,"abstract":53,"tags":54},"I3",[52],"remote sensing",[52],[28],{"code":56,"score":21,"supported":13,"title":57,"abstract":58,"tags":59},"I1",[],[],[26],"一级领域明确，二级方向需要细分",[62,63],"作物水分胁迫 智慧农业 机器学习 遥感","作物水分胁迫 智慧农业","作物水分胁迫智慧农业机器学习遥感-1517",6,"10.1016\u002Fj.atech.2026.102536",{"doi":66,"openalex_id":68,"authors":69,"venue":9,"cited_by_count":78,"oa_url":6,"card":8,"direction":79,"ingested_from":80},"W7206189125",[70,73,75],{"name":71,"orcid":72},"Ahmed Elbeltagi","https:\u002F\u002Forcid.org\u002F0000-0002-5506-9502",{"name":74,"orcid":8},"Aman Srivastava",{"name":76,"orcid":77},"Abdullah A. Alsumaiei","https:\u002F\u002Forcid.org\u002F0000-0002-9148-3954",0,"农业遥感与作物表型","openalex","2026-09-03T23:30:04.462065Z",{"total":65,"page":21,"page_size":65,"items":83},[84,164,229,283,343,390],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":89,"content":8,"source_name":90,"source_url":87,"published_at":91,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":92,"score_detail":93,"sources":98,"tags":100,"primary_topic":104,"secondary_topics":105,"classification":106,"search_phrases":144,"slug":147,"view_count":21,"doi":148,"paper":149,"created_at":163},5074,"QISHLOQ XO‘JALIGI OLIY TA’LIM MUASSASALARIDA MAXSUS FANLARNI O‘QITISHDA “SMART AGRICULTURE” TEXNOLOGIYALARIDAN FOYDALANISH METODIKASI","https:\u002F\u002Fdoi.org\u002F10.67547\u002Ffanistiqbollari.2026.876","The article examines a methodology for integrating Smart Agriculture technologies into the teaching of specialized subjects at agricultural higher education institutions. The study analyzes the didactic potential of the Internet of Things, sensor monitoring, GIS, remote sensing, artificial intelligence, smart irrigation and digital data analytics.","本文探讨了将智慧农业技术融入农业高等院校专业课程教学的方法论。研究分析了物联网、传感器监测、地理信息系统、遥感、人工智能、智能灌溉和数字数据分析在教学中的潜力。","Ta limda Istiqbolli Izlanishlar xalqaro ilmiy metodik jurnal","2026-10-15T00:00:00Z",49,{"impact":94,"substance":95,"depth":96,"authority":65,"freshness":94,"relevant":21,"comment":97},8,14,13,"乌兹别克语论文探讨智慧农业技术融入农业高校专业教学的路径，属农业信息化人才培养方向，但为境外一般期刊、影响面有限，暂不宜进入每日精选。",[99],{"name":90,"url":87},[26,101,102,28,103],"农业人工智能","物联网","农业教育",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":107},{"candidates":108,"agricultural_evidence":44,"reason":60},[109,114,118,124,131,138],{"code":56,"score":16,"supported":44,"title":110,"abstract":112,"tags":113},[111],"smart agriculture",[26,111],[26],{"code":50,"score":20,"supported":44,"title":115,"abstract":116,"tags":117},[],[28,52],[28],{"code":119,"score":20,"supported":44,"title":120,"abstract":121,"tags":123},"I5",[],[102,122],"internet of things",[102],{"code":125,"score":65,"supported":44,"title":126,"abstract":127,"tags":130},"H3",[],[128,129],"灌溉","irrigation",[],{"code":132,"score":65,"supported":44,"title":133,"abstract":134,"tags":137},"I4",[],[135,136],"地理信息","GIS",[],{"code":43,"score":139,"supported":44,"title":140,"abstract":141,"tags":143},4,[],[142],"artificial intelligence",[101],[145,146],"农业人工智能 农业教育 智慧农业 物联网","农业人工智能 农业教育","农业人工智能农业教育智慧农业物联网-5074","10.67547\u002Ffanistiqbollari.2026.876",{"doi":148,"openalex_id":150,"authors":151,"venue":90,"cited_by_count":78,"oa_url":155,"card":156,"direction":162,"ingested_from":80},"W7221997102",[152],{"name":153,"orcid":154},"O'G'ILOY SADULLO QIZI SHAMSIYEV","https:\u002F\u002Forcid.org\u002F0009-0003-4706-6488","https:\u002F\u002Ffanistiqbollari.uz\u002Fstorage\u002Farticle876.pdf",{"tldr":157,"method":158,"finding":159,"direction":160,"opportunity":161},"研究将智慧农业技术融入农业高校专业课程教学的方法论。","分析物联网、传感器、GIS、遥感、AI等技术的教学潜力。","智慧农业技术可有效支撑农业专业课程的教学方法设计。","数字乡村与农业信息化","可实证检验该教学法对学生技能与就业的影响，并开发配套课程资源。","智慧农业 \u002F 农业物联网","2026-10-16T23:30:07.890026Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":8,"source_name":170,"source_url":167,"published_at":171,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":172,"score_detail":173,"sources":175,"tags":177,"primary_topic":180,"secondary_topics":181,"classification":182,"search_phrases":207,"slug":210,"view_count":21,"doi":211,"paper":212,"created_at":228},4825,"Multi-Source Remote Sensing Fusion and Deep Learning for Intelligent Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206434","The fusion of multi-source remote sensing data and deep learning technology has been increasingly applied in precision agriculture. This review focuses on the perception–decision–execution framework and summarizes the current application status, technical routes, and development trends of multi-source remote sensing data fusion and deep learning in precision agriculture. It examines the complementary advantages of multimodal data such as optical, thermal infrared (TIR), Light Detection and Ranging (LiDAR), and synthetic aperture radar (SAR) in characterizing crop physiological traits and structural characteristics. At the algorithmic level, this review analyzes the evolution of deep learning architectures from single convolutional neural networks (CNNs) to spatiotemporal fusion networks (STFNs), Transformer-based architectures, and emerging state-space models, with emphasis on their capabilities for heterogeneous feature extraction and cross-modal information integration. This review discusses the transformation mechanism from intelligent perception to executable decision-making and explores the application effectiveness of prescription maps and variable-rate task technology in improving resource utilization. This review proposes forward-looking views on core bottlenecks such as model generalization, physical interpretability, and edge computing. This review not only provides a theoretical analysis of precision agriculture but also provides academic recommendations for the development of intelligent agricultural equipment in the future.","多源遥感数据与深度学习技术的融合已在精准农业中得到日益广泛的应用。本文以“感知—决策—执行”框架为主线，综述了多源遥感数据融合与深度学习在精准农业中的应用现状、技术路线与发展趋势。文章考察了光学、热红外（TIR）、激光雷达（LiDAR）和合成孔径雷达（SAR）等多模态数据在表征作物生理性状与结构特征方面的互补优势。在算法层面，本文分析了深度学习架构从单一卷积神经网络（CNN）向时空融合网络（STFN）、基于Transformer的架构以及新兴状态空间模型的演进，重点探讨了其在异构特征提取与跨模态信息整合方面的能力。本文讨论了从智能感知到可执行决策的转化机制，并探究了处方图与变量作业技术在提升资源利用率方面的应用效果。针对模型泛化能力、物理可解释性与边缘计算等核心瓶颈，本文提出了前瞻性观点。本文不仅为精准农业提供了理论分析，也为未来智能农业装备的发展提供了学术建议。","Sensors","2026-10-12T00:00:00Z",78,{"impact":18,"substance":17,"depth":18,"authority":95,"freshness":94,"relevant":21,"comment":174},"系统综述多源遥感融合与深度学习在精准农业中的感知—决策—执行框架，方法脉络清晰、前瞻性强，对智慧农业装备研发有参考价值。",[176],{"name":170,"url":167},[26,101,178,28,179],"精准农业","多源数据融合",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":183},{"candidates":184,"agricultural_evidence":44,"reason":60},[185,191,197,201],{"code":56,"score":186,"supported":44,"title":187,"abstract":189,"tags":190},16,[188],"precision 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Wang",{"tldr":224,"method":225,"finding":226,"direction":79,"opportunity":227},"综述多源遥感融合与深度学习在精准农业感知-决策-执行框架中的应用与趋势。","综述光学、热红外、LiDAR、SAR融合及CNN到Transformer、状态空","多模态融合与先进深度学习架构可提升作物感知与处方图决策，但泛化性、可解释性和边缘计算仍是瓶颈。","可探索轻量化状态空间模型与物理约束融合，提升跨区域泛化及边缘端实时决策能力。","2026-10-13T23:30:24.008972Z",{"id":230,"title":231,"url":232,"summary":233,"summary_zh":234,"content":8,"source_name":235,"source_url":232,"published_at":236,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":237,"score_detail":238,"sources":240,"tags":242,"primary_topic":246,"secondary_topics":247,"classification":248,"search_phrases":265,"slug":268,"view_count":269,"doi":270,"paper":271,"created_at":282},3915,"PRECISION AGRICULTURE ANALYTICS USING UAV MULTISPECTRAL IMAGING AND MACHINE LEARNING FOR CROP STRESS DETECTION AND YIELD OPTIMIZATION MODELS","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22111394","PRECISION AGRICULTURE ANALYTICS USING UAV MULTISPECTRAL IMAGING AND MACHINE LEARNING FOR CROP STRESS DETECTION AND YIELD OPTIMIZATION MODELS。International Journal of Engineering Technology Research & Management","基于无人机多光谱成像与机器学习的精准农业分析用于作物胁迫检测和产量优化模型。《国际工程技术研究与管理杂志》","International Journal of Engineering Technology Research & Management","2026-12-21T00:00:00Z",34,{"impact":94,"substance":12,"depth":19,"authority":139,"freshness":78,"relevant":21,"comment":239},"主题契合智慧农业，但期刊权威性低、摘要信息量不足且发布日期异常，暂不建议进入每日精选。",[241],{"name":235,"url":232},[26,27,243,244,245],"作物胁迫","无人机遥感","多光谱影像",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":249},{"candidates":250,"agricultural_evidence":44,"reason":60},[251,255,259],{"code":56,"score":16,"supported":44,"title":252,"abstract":253,"tags":254},[188],[178,188],[26],{"code":43,"score":16,"supported":44,"title":256,"abstract":257,"tags":258},[46],[27,46],[27],{"code":50,"score":16,"supported":44,"title":260,"abstract":262,"tags":264},[261],"multispectral",[263,261],"无人机多光谱",[244],[266,267],"无人机 多光谱 作物胁迫","机器学习 产量优化","无人机多光谱作物胁迫-3915",2,"10.5281\u002Fzenodo.22111394",{"doi":270,"openalex_id":272,"authors":273,"venue":235,"cited_by_count":78,"oa_url":232,"card":276,"direction":281,"ingested_from":80},"W7204258669",[274],{"name":275,"orcid":8},"Adedayo Oluwaseyi Alawode",{"tldr":277,"method":278,"finding":279,"direction":79,"opportunity":280},"利用无人机多光谱影像与机器学习进行作物胁迫检测和产量优化建模。","无人机多光谱成像结合机器学习算法构建检测与预测模型。","多光谱特征与机器学习可有效识别作物胁迫并优化产量预测。","可探索多时相多光谱与深度学习融合，提升胁迫早期预警和跨区域泛化能力。","农业人工智能与决策模型","2026-10-01T23:30:53.373673Z",{"id":284,"title":285,"url":286,"summary":287,"summary_zh":288,"content":8,"source_name":289,"source_url":286,"published_at":290,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":291,"score_detail":292,"sources":296,"tags":298,"primary_topic":302,"secondary_topics":303,"classification":304,"search_phrases":319,"slug":322,"view_count":323,"doi":324,"paper":325,"created_at":342},2662,"Annual Gridded Anthropogenic CH4 Emissions Estimation in China (2019–2025) Integrating Multisource Data: SHAP-Based Driver Attribution and Spatio-Temporal Patterns","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183168","Accurately quantifying the spatiotemporal dynamics and driving mechanisms of anthropogenic methane (CH4) emissions (MEs) is of great significance for achieving regional “dual-carbon” goals and global climate collaborative governance. However, existing ME inventories and macro-inversion models generally face bottlenecks such as coarse spatial resolution, lack of data update timeliness, and the inability of traditional static emission factors to capture non-linear responses. To address these issues, this study proposes an annual ME inventory enhancement framework integrating multi-source geographic and remote sensing data. This framework evaluates four advanced machine learning (ML) algorithms, including Random Forest (RF), Categorical Boosting (CB), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM), to construct a 0.1° high-resolution spatial grid of anthropogenic ME in China from 2019 to 2025. Furthermore, it introduces the SHapley Additive exPlanations (SHAP) framework and multi-scale spatial autocorrelation analysis to parse the driving mechanisms and clustering patterns. The results show the following: (1) LGBM exhibits the optimal comprehensive estimation accuracy (R2 = 0.938, RMSE = 3.707 Kt) and robust capability in capturing extreme ME sources (RTop2 = 0.929). (2) SHAP attribution reveals that coal mining and nighttime light (NTL) represent the primary contributing features to ME predictions (with a cumulative contribution of 65.60%), followed by agricultural and pastoral activities (24.98%), and all factors exhibit significant non-linear threshold and step-response characteristics. (3) Regarding spatiotemporal evolution, China’s total anthropogenic ME shows a trend of initial slow increase followed by high-level stabilization; spatially, it presents a “hot in the north, cold in the south” pattern, with extreme high values highly clustered in the Shanxi–Shaanxi–Inner Mongolia energy triangle and its peripheral expansion nodes. This study provides scientific references for formulating tailored, multi-scale, and refined CH4 mitigation strategies.","准确量化人为甲烷（CH₄）排放（MEs）的时空动态与驱动机制，对实现区域“双碳”目标及全球气候协同治理具有重要意义。然而，现有ME清单与宏观反演模型普遍面临空间分辨率粗、数据更新时效性不足、传统静态排放因子难以刻画非线性响应等瓶颈。针对上述问题，本研究提出了一种融合多源地理与遥感数据的年度ME清单增强框架。该框架评估了四种先进机器学习（ML）算法，包括随机森林（RF）、类别提升（CB）、极端梯度提升（XGB）和轻量梯度提升机（LGBM），构建了2019—2025年中国0.1°高分辨率人为ME空间网格。此外，引入SHapley加性解释（SHAP）框架与多尺度空间自相关分析，解析驱动机制与集聚模式。结果表明：（1）LGBM具有最优的综合估算精度（R² = 0.938，RMSE = 3.707 Kt），且在捕捉极端ME源方面表现稳健（RTop2 = 0.929）。（2）SHAP归因揭示，煤炭开采与夜间灯光（NTL）是ME预测的主要贡献特征（累计贡献率65.60%），其次为农牧活动（24.98%），且所有因子均呈现显著的非线性阈值与阶跃响应特征。（3）在时空演变方面，中国人为ME总量呈先缓慢增长后高位稳定的趋势；空间上表现为“北热南冷”格局，极端高值高度集聚于晋陕蒙能源金三角及其外围扩展节点。本研究为制定因地制宜、多尺度、精细化CH₄减排策略提供了科学参考。","Remote Sensing","2026-09-15T00:00:00Z",81,{"impact":18,"substance":293,"depth":18,"authority":95,"freshness":294,"relevant":21,"comment":295},22,9,"基于多源遥感与机器学习构建中国0.1°人为甲烷排放网格清单，方法新颖、数据规模大，对农业源排放核算与双碳治理有参考价值，但偏学术、非直接农业应用。",[297],{"name":289,"url":286},[299,27,28,300,301],"农业信息化","甲烷排放","双碳目标",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":305},{"candidates":306,"agricultural_evidence":44,"reason":60},[307,311,315],{"code":43,"score":20,"supported":44,"title":308,"abstract":309,"tags":310},[],[27,46],[27],{"code":50,"score":20,"supported":44,"title":312,"abstract":313,"tags":314},[],[28,52],[28],{"code":56,"score":21,"supported":13,"title":316,"abstract":317,"tags":318},[],[],[299],[320,321],"农业信息化 双碳目标 机器学习 甲烷排放","农业信息化 双碳目标","农业信息化双碳目标机器学习甲烷排放-2662",5,"10.3390\u002Frs18183168",{"doi":324,"openalex_id":326,"authors":327,"venue":289,"cited_by_count":78,"oa_url":286,"card":336,"direction":79,"ingested_from":80},"W7213229152",[328,331,334],{"name":329,"orcid":330},"Chaokang He","https:\u002F\u002Forcid.org\u002F0009-0005-8014-434X",{"name":332,"orcid":333},"Qinjun Wang","https:\u002F\u002Forcid.org\u002F0000-0001-6084-1889",{"name":335,"orcid":8},"Wenyue Xie",{"tldr":337,"method":338,"finding":339,"direction":340,"opportunity":341},"融合多源数据与机器学习构建中国2019-2025年0.1°人为甲烷排放网格，并用SHAP解析驱动机制","随机森林、CatBoost、XGBoost、LightGBM对比，结合SHAP归","LGBM精度最优（R²=0.938），煤炭开采与夜间灯光贡献65.6%，排放呈北热南冷格局。","农业绿色发展与碳","可延伸至农业源甲烷排放的精细网格化与非线性驱动阈值研究，支撑分区减排。","2026-09-16T23:30:28.864720Z",{"id":344,"title":345,"url":346,"summary":347,"summary_zh":8,"content":348,"source_name":349,"source_url":8,"published_at":350,"category":351,"cover_url":8,"hotness":12,"is_selected":13,"score":352,"score_detail":353,"sources":355,"tags":357,"primary_topic":360,"secondary_topics":361,"classification":362,"search_phrases":385,"slug":388,"view_count":21,"doi":8,"paper":8,"created_at":389},1842,"活力中国调研行｜农户有了数字新农具——黑龙江佳木斯佳家农智慧农服平台服务超6万户","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7681659096075977279\u002F","新华社记者随活力中国调研行采访团走进黑龙江佳木斯国家农业高新技术产业示范区，佳家农智慧农服平台由佳木斯国家农高区联合中国科学院计算技术研究所打造，依托伏羲大脑农业大模型汇集卫星遥感、气象、土壤、作物长势、病虫害等多源数据；目前已服务农户超过6万户、覆盖耕地超过700万亩。","## 活力中国调研行丨农户有了数字“新农具”\n\n2026-09-04 20:40·[新华社](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAiAce5qhH31TeuB3UdpFMV8u-uwy2LnoiqI10uZHqAt8\u002F?source=tuwen_detail)\n\n新华社哈尔滨9月4日电 题：农户有了数字“新农具”\n\n新华社记者沈易瑾\n\n初秋的三江平原，稻穗渐黄。近日，新华社记者随“活力中国调研行”采访团走进黑龙江佳木斯采访发现，随着人工智能、遥感、大数据等技术加快走进田间，过去需要许多专业设备才能获取的农情信息，现在通过一部手机就能送到农户手中。\n\n在佳木斯市桦川县玉成现代农机专业合作社的稻田里，用于气象监测、虫情监测等设备分布田间。说起这些年种地方式的变化，合作社总经理赵德山感受最深的是，获取农情信息越来越方便。\n\n“以前了解土壤墒情、病虫害，主要靠经验、找专家，后来有了智慧农业设备，但投入也比较大。”赵德山说，如今通过“佳家农”智慧农服平台，不少田间信息可以直接在手机上查看，“更直观，也更省事了”。\n\n让手机逐渐“看懂”农田，准确、智能的大模型是关键。\n\n“佳家农”智慧农服平台由黑龙江佳木斯国家农业高新技术产业示范区联合中国科学院计算技术研究所打造，依托“伏羲大脑”农业大模型，汇集卫星遥感、气象、土壤、作物长势、病虫害等多源数据，通过AI分析，为不同地块提供农情监测和种植管理参考。\n\n佳木斯国家农高区管委会副主任、“佳家农”项目负责人陈海华介绍，农户在手机上圈定地块，就可以查看墒情、作物长势等信息；田里发现异常，拍照上传后，AI可辅助识别病虫害并给出防治建议；施肥等农事操作，也可结合地块情况获得参考建议。\n\n“在提升服务精准度的同时，平台也着力降低智慧农业的应用门槛。”陈海华说，与较多依靠地面传感器、虫情测报灯等硬件设备采集数据不同，“佳家农”更多依托卫星遥感、算法和数据分析等方式获取地块信息，农户无需为使用平台专门购置、布设一套传感设备。目前，平台面向农户免费开放。\n\n数字化服务还进一步延伸至农业生产服务环节。买种买肥，平台可以结合地块积温、土壤等情况提供参考；需要农机，可在线发布作业需求、匹配附近农机合作社；到了收获季，还可查看市场信息、对接订单。线下，村级服务站的信息员帮助组织农资集采、对接农机作业等，推动线上信息与线下生产服务衔接。\n\n目前，“佳家农”已服务农户超过6万户、覆盖耕地超过700万亩，服务范围已由佳木斯扩大至东北地区。\n\n从看墒情、识病虫害，到找农机、买农资、对接订单，过去分散在农业生产不同环节的技术和服务，正在通过平台更加便捷地送到田间地头，数字技术成为手边可用的“新农具”。（完）","新华社\u002F今日头条 2026-09-04","2026-09-04T12:40:00Z","报道",79,{"impact":293,"substance":17,"depth":186,"authority":16,"freshness":65,"relevant":21,"comment":354},"央媒报道黑龙江智慧农服平台，服务超6万户，覆盖700万亩，体现数字技术降低应用门槛，具有省级示范意义。",[356],{"name":349,"url":346},[26,101,358,28,359],"黑龙江","农服平台",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":363},{"candidates":364,"agricultural_evidence":44,"reason":60},[365,370,374,381],{"code":43,"score":139,"supported":44,"title":366,"abstract":367,"tags":369},[],[368],"大模型",[101],{"code":50,"score":139,"supported":44,"title":371,"abstract":372,"tags":373},[],[28],[28],{"code":375,"score":376,"supported":44,"title":377,"abstract":378,"tags":380},"H2",3,[],[379],"土壤",[],{"code":56,"score":21,"supported":13,"title":382,"abstract":383,"tags":384},[],[],[26],[386,387],"农业人工智能 农服平台 智慧农业 黑龙江","农业人工智能 农服平台","农业人工智能农服平台智慧农业黑龙江-1842","2026-09-08T00:07:11.233244Z",{"id":391,"title":392,"url":393,"summary":394,"summary_zh":395,"content":8,"source_name":396,"source_url":393,"published_at":397,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":398,"score_detail":399,"sources":401,"tags":403,"primary_topic":407,"secondary_topics":408,"classification":409,"search_phrases":438,"slug":441,"view_count":78,"doi":442,"paper":443,"created_at":453},5563,"AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531525","Abstract: Crop diseases and pest infestations remain persistent threats to plantation productivity, with pests estimated to destroy up to 40% of global crop production and plant diseases and invasive insects imposing substantial economic losses worldwide (Food and Agriculture Organization of the United Nations [FAO], 2019). Climate change is intensifying these risks by altering the geographic range, seasonality, and population dynamics of pathogens and pests (Gullino et al., 2021; Skendžić et al., 2021). This paper examines how artificial intelligence (AI) — including convolutional neural networks (CNNs), vision transformers (ViTs), unmanned aerial vehicle (UAV)-based multispectral and hyperspectral sensing, and Internet of Things (IoT)-enabled monitoring — can strengthen the early detection of biotic stress in plantation crops and thereby support plantation risk management. Drawing on recent peer-reviewed and industry literature, the paper synthesises evidence on detection performance, deployment modalities, and pathways for integration into enterprise risk-management systems. It proposes a conceptual framework in which AI-enabled early-warning systems operate as a continuous, spatially explicit risk-sensing layer linking agronomic observations with operational, financial, and insurance decision-making. AI can improve the timeliness, spatial precision, and consistency of surveillance; however, its risk-management value depends on field validation, representative training data, reliable infrastructure, affordable deployment, and appropriate governance. AI should therefore currently be positioned as decision support that augments, rather than replaces, agronomic expertise and ground-truthing. Keywords: artificial intelligence; plant disease detection; pest infestation; precision agriculture; plantation risk management; machine learning; remote sensing. Title: AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management Author: Dr. Geetha Govindan K Gopalakrishnan International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 14, Issue 2, October 2026 - March 2027 Page No: 54-59 Research Publish Journals Website: www.researchpublish.com Published Date: 21-October-2026 DOI: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526 Paper Download Link (Source) https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-management","摘要：作物病害与虫害侵染始终是种植园生产力的持续威胁，据估计，害虫每年可摧毁全球高达40%的作物产量，植物病害与入侵昆虫在全球范围内造成重大经济损失（联合国粮食及农业组织[FAO]，2019）。气候变化通过改变病原体与害虫的地理分布范围、季节性规律及种群动态，正在加剧这些风险（Gullino等，2021；Skendžić等，2021）。本文探讨人工智能（AI）——包括卷积神经网络（CNN）、视觉变换器（ViT）、基于无人机（UAV）的多光谱与高光谱遥感，以及物联网（IoT）赋能的监测技术——如何强化种植园作物生物胁迫的早期检测，从而支撑种植园风险管理。本文基于近期同行评审文献与行业文献，综合梳理了检测性能、部署模式及其融入企业风险管理系统的路径等方面的证据。文章提出一个概念框架，其中AI赋能的预警系统作为持续运行、空间显性的风险感知层，将农艺观测与运营、财务及保险决策相衔接。AI能够提升监测的及时性、空间精度与一致性；然而，其风险管理价值取决于田间验证、具有代表性的训练数据、可靠的基础设施、可负担的部署成本以及适当的治理。因此，当前应将AI定位为决策支持工具，用以增强而非替代农艺专业知识与实地核验。关键词：人工智能；植物病害检测；虫害侵染；精准农业；种植园风险管理；机器学习；遥感。题目：AI驱动的作物病害与虫害侵染早期检测：种植园风险管理的新范式。作者：Geetha Govindan K Gopalakrishnan博士。《国际管理与商业创新期刊》ISSN 2348-7585（在线）第14卷第2期，2026年10月—2027年3月，第54-59页。研究出版期刊网站：www.researchpublish.com。发布日期：2026年10月21日。DOI：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526。论文下载链接（来源）：https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-ma","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",56,{"impact":19,"substance":186,"depth":96,"authority":65,"freshness":294,"relevant":21,"comment":400},"综述性论文，系统梳理AI与遥感在作物病虫害早期预警中的应用并提出风险管理框架，有参考价值但方法创新与数据规模有限，信源为普通出版平台。",[402],{"name":396,"url":393},[26,101,404,405,406],"遥感监测","风险管理","病虫害监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":410},{"candidates":411,"agricultural_evidence":44,"reason":60},[412,417,422,427,431],{"code":43,"score":12,"supported":44,"title":413,"abstract":414,"tags":416},[],[27,46,142,415],"decision support",[101],{"code":50,"score":12,"supported":44,"title":418,"abstract":419,"tags":421},[],[28,52,261,420],"hyperspectral",[404],{"code":119,"score":294,"supported":44,"title":423,"abstract":424,"tags":426},[],[102,122,425],"IoT",[],{"code":56,"score":20,"supported":44,"title":428,"abstract":429,"tags":430},[],[178,188],[26],{"code":432,"score":65,"supported":44,"title":433,"abstract":434,"tags":437},"H7",[],[435,436],"气候变化","climate change",[],[439,440],"AI 作物病害 早期检测","无人机 多光谱 病虫害监测","AI作物病害早期检测-5563","10.5281\u002Fzenodo.23531525",{"doi":442,"openalex_id":444,"authors":445,"venue":396,"cited_by_count":78,"oa_url":393,"card":448,"direction":162,"ingested_from":80},"W7223085400",[446],{"name":447,"orcid":8},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":449,"method":450,"finding":451,"direction":281,"opportunity":452},"综述AI（CNN、ViT、无人机遥感、物联网）用于作物病虫害早期检测并融入种植园风险管理。","文献综述，整合CNN、ViT、无人机多\u002F高光谱与IoT监测的检测性能与部署方式。","AI可提升监测及时性与空间精度，但价值取决于田间验证、代表性数据与治理，应作决策支持而非替代专家。","可将AI预警层与保险、金融风控系统对接，研究跨域数据融合与可解释风险定价的空白。","2026-10-22T11:20:24.428080Z"]