[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3521":3,"related-3521":110},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":79,"slug":82,"view_count":22,"doi":83,"paper":84,"created_at":109},3521,"A practical model and online tool to predict cover crop nitrogen content and carbon-to-nitrogen ratio via remote sensing","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10434-6","Abstract Purpose Estimations of cover crop (CC) nitrogen (N) content and carbon-to-nitrogen (C:N) ratio are crucial for guiding N fertilizer recommendations for subsequent cash crops, yet prediction tools that can be easily adopted by practitioners are lacking. This study aims to (1) develop models to predict CC N content and C:N ratio using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) from Uncrewed Aerial Vehicles (UAV) and satellite imagery, combined with growing degree days (GDD, base 5 °C) and CC species; and (2) integrate the models developed into an online application. Methods Over 260 CC biomass samples were collected from 10 Pennsylvania counties in the Chesapeake Bay watershed representing diverse species (triticale, cereal rye, wheat, brassicas, legumes, and mixtures). Four models were evaluated: (1) UAV NDVI + GDD + species, (2) UAV NDRE + GDD + species, (3) satellite NDVI + GDD + species, and (4) satellite NDRE + GDD + species. Results Random forest models (R² > 0.74 for N content and R² > 0.86 for C: N ratio) generally explained more variability than generalized additive models (R² > 0.70 for N content and R² > 0.85 for C: N ratio) based on 10-fold cross validation. GDD and vegetation indices were the strongest predictors for N content, while GDD and species group influenced C: N ratio the most. In the independent site-years validation, UAV-NDRE explained the most variability in CC N content (R² = 0.82), and both UAV-based models (R² = 0.52) explained more variability than the satellite models for C: N ratio prediction. Conclusion Random forest models accurately predicted CC N content and C: N ratio and were integrated into an online application to support precision agriculture practices.","摘要 目的 估算覆盖作物（cover crop，CC）氮（N）含量及碳氮比（C:N）对于指导后续经济作物的氮肥推荐至关重要，但目前缺乏便于从业者采用的预测工具。本研究旨在（1）利用无人机（Uncrewed Aerial Vehicles，UAV）和卫星影像获取的归一化差异植被指数（Normalized Difference Vegetation Index，NDVI）和归一化差异红边指数（Normalized Difference Red Edge Index，NDRE），结合生长度日（growing degree days，GDD，基准温度5 °C）和覆盖作物物种，开发预测覆盖作物氮含量和碳氮比的模型；（2）将所开发模型整合至在线应用程序中。方法 在切萨皮克湾流域宾夕法尼亚州10个县采集了260余份覆盖作物生物量样品，涵盖多种物种（小黑麦、黑麦、小麦、芸薹属、豆科及混播）。评估了四种模型：（1）无人机NDVI + GDD + 物种，（2）无人机NDRE + GDD + 物种，（3）卫星NDVI + GDD + 物种，（4）卫星NDRE + GDD + 物种。结果 基于10折交叉验证，随机森林模型（氮含量R² > 0.74，碳氮比R² > 0.86）总体上比广义加性模型（氮含量R² > 0.70，碳氮比R² > 0.85）解释了更多的变异。GDD和植被指数是氮含量的最强预测因子，而GDD和物种组对碳氮比影响最大。在独立站点-年份验证中，无人机NDRE对覆盖作物氮含量变异的解释能力最强（R² = 0.82），且两个基于无人机的模型（R² = 0.52）在碳氮比预测方面均比卫星模型解释了更多的变异。结论 随机森林模型准确预测了覆盖作物氮含量和碳氮比，并已整合至在线应用程序中以支持精准农业实践。",null,"Precision Agriculture","2026-09-25T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,14,9,1,"基于无人机与卫星遥感的覆盖作物氮素与碳氮比预测模型并落地在线工具，方法扎实、数据规模可观，对精准施肥有直接应用价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","精准施肥","覆盖作物","遥感监测","氮素管理",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":46},[45,51,59,67,74],{"code":33,"score":21,"supported":46,"title":47,"abstract":49,"tags":50},true,[48],"remote sensing",[],[30],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":58},"I1",7,[],[56,57],"精准农业","precision agriculture",[27],{"code":60,"score":61,"supported":46,"title":62,"abstract":63,"tags":66},"A2",6,[],[64,65],"小麦","wheat",[],{"code":68,"score":69,"supported":46,"title":70,"abstract":71,"tags":73},"F3",4,[],[72],"fertilizer",[28],{"code":75,"score":22,"supported":14,"title":76,"abstract":77,"tags":78},"A7",[],[],[29],[80,81],"覆盖作物 氮含量 遥感 预测","UAV NDRE 碳氮比 在线工具","覆盖作物氮含量遥感预测-3521","10.1007\u002Fs11119-026-10434-6",{"doi":83,"openalex_id":85,"authors":86,"venue":10,"cited_by_count":101,"oa_url":6,"card":102,"direction":106,"ingested_from":108},"W7214325666",[87,89,91,93,95,97,99],{"name":88,"orcid":9},"N. Kaur",{"name":90,"orcid":9},"C. White",{"name":92,"orcid":9},"A. Lefever",{"name":94,"orcid":9},"A. Thieme",{"name":96,"orcid":9},"J. Jennewein",{"name":98,"orcid":9},"WD. Hively",{"name":100,"orcid":9},"D. Carrijo",0,{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"用无人机和卫星遥感结合积温与物种，构建覆盖作物氮含量及碳氮比预测模型并开发在线工具。","260个覆盖作物样本，随机森林与广义加性模型，UAV\u002F卫星NDVI、NDRE、G","随机森林预测精度高（N含量R²>0.74，C:N比R²>0.86），UAV-NDRE对N含量解释力最","农业遥感与作物表型","可探索多源遥感融合与实时在线工具在精准氮肥推荐中的田间验证和推广。","openalex","2026-09-26T23:30:02.797286Z",{"total":61,"page":22,"page_size":61,"items":111},[112,190,264,347,424,467],{"id":113,"title":114,"url":115,"summary":116,"summary_zh":117,"content":9,"source_name":118,"source_url":115,"published_at":119,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":120,"score_detail":121,"sources":125,"tags":127,"primary_topic":131,"secondary_topics":134,"classification":136,"search_phrases":161,"slug":164,"view_count":101,"doi":165,"paper":166,"created_at":189},5367,"Integration of multi-source UAV remote sensing features and derived features for winter wheat aboveground biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102656","Accurate estimation of winter wheat ( Triticum aestivum L.) aboveground biomass (AGB) is important for crop growth monitoring and field management. However, unmanned aerial vehicle (UAV)-based AGB estimation remains challenging due to variations in canopy conditions across growth stages, and different remote sensing features can capture complementary characteristics related to AGB estimation. This study integrated multi-source UAV-derived features, including vegetation indices (VIs), texture features (TFs), light detection and ranging (LiDAR)-derived features, and derived features (DFs) generated from the original feature sets, with four machine learning algorithms for winter wheat AGB estimation. Results showed that TFs provided better prediction performance than other single-source feature sets. Compared with single-source feature sets, the integration of VIs, TFs, LiDAR-derived features, and DFs further improved AGB estimation, and the random forest (RF) model achieved the best performance among the evaluated models (R² = 0.923, RMSE = 1.731 Mg ha⁻¹). Incremental R² analysis based on a stage-only baseline model showed that the RF model explained additional variation in AGB beyond growth-stage information, with an incremental R² of 0.479. Stage-specific evaluation indicated that model performance varied among growth stages, with the highest accuracy observed at the booting stage (R² = 0.632, RMSE = 1.425 Mg ha⁻¹). Feature contribution analysis based on the full-feature fusion model using all growth-stage data identified canopy coverage and several DFs as important predictors, while stage-specific analyses showed differences in dominant predictors across growth stages. The integration of multi-source UAV-derived features and DFs with machine learning provides an effective approach for winter wheat AGB estimation, while model performance remains strongly dependent on growth stage.","准确估算冬小麦（Triticum aestivum L.）地上生物量（aboveground biomass, AGB）对作物生长监测和田间管理具有重要意义。然而，由于不同生育期冠层条件存在差异，且不同遥感特征能够捕捉与AGB估算相关的互补信息，基于无人机（unmanned aerial vehicle, UAV）的AGB估算仍面临挑战。本研究将多源无人机衍生特征，包括植被指数（vegetation indices, VIs）、纹理特征（texture features, TFs）、激光雷达（light detection and ranging, LiDAR）衍生特征以及由原始特征集生成的衍生特征（derived features, DFs），与四种机器学习算法相结合，用于冬小麦AGB估算。结果表明，TFs的预测性能优于其他单一来源特征集。与单一来源特征集相比，融合VIs、TFs、LiDAR衍生特征和DFs进一步提高了AGB估算精度，其中随机森林（random forest, RF）模型在所评估的模型中表现最佳（R² = 0.923，RMSE = 1.731 Mg ha⁻¹）。基于仅含生育期信息的基线模型进行的增量R²分析表明，RF模型在生育期信息之外解释了AGB的额外变异，增量R²为0.479。分生育期评估表明，模型性能在不同生育期之间存在差异，其中在孕穗期观察到最高精度（R² = 0.632，RMSE = 1.425 Mg ha⁻¹）。基于使用全部生育期数据的全特征融合模型进行的特征贡献分析识别出冠层覆盖度和若干DFs为重要预测因子，而分生育期分析则表明不同生育期的主导预测因子存在差异。将多源无人机衍生特征和DFs与机器学习相结合，为冬小麦AGB估算提供了一种有效方法，但模型性能仍强烈依赖于生育期。","Smart Agricultural Technology","2026-10-17T00:00:00Z",77,{"impact":17,"substance":18,"depth":19,"authority":122,"freshness":123,"relevant":22,"comment":124},13,8,"该研究融合无人机多源遥感特征与衍生特征，用随机森林估算冬小麦地上生物量，方法新颖、结论可靠，对作物长势监测有参考价值。",[126],{"name":118,"url":115},[27,128,30,129,130],"无人机","冬小麦","生物量估算",{"code":60,"slug":65,"name":64,"path":132,"parent_code":133},"\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[135],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":137},{"candidates":138,"agricultural_evidence":46},[139,145,150,157],{"code":60,"score":140,"supported":46,"title":141,"abstract":143,"tags":144},26,[142,65],"winter wheat",[64,142,65],[129],{"code":33,"score":17,"supported":46,"title":146,"abstract":147,"tags":149},[48],[148,48],"遥感",[130,30],{"code":151,"score":61,"supported":46,"title":152,"abstract":153,"tags":156},"I2",[],[154,155],"机器学习","machine learning",[],{"code":52,"score":22,"supported":14,"title":158,"abstract":159,"tags":160},[],[],[27],[162,163],"无人机 多源遥感 冬小麦 生物量","冬小麦 地上生物量 随机森林","无人机多源遥感冬小麦生物量-5367","10.1016\u002Fj.atech.2026.102656",{"doi":165,"openalex_id":167,"authors":168,"venue":118,"cited_by_count":101,"oa_url":115,"card":184,"direction":106,"ingested_from":108},"W7222806066",[169,171,173,175,178,180,182],{"name":170,"orcid":9},"Guangtao Wang",{"name":172,"orcid":9},"Sumei Yao",{"name":174,"orcid":9},"Yanzheng Xie",{"name":176,"orcid":177},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":179,"orcid":9},"Xiangdong Chen",{"name":181,"orcid":9},"Yuhao Zhang",{"name":183,"orcid":9},"Lina Jiang",{"tldr":185,"method":186,"finding":187,"direction":106,"opportunity":188},"融合无人机多源遥感特征与衍生特征，用机器学习估算冬小麦地上生物量。","无人机植被指数、纹理、LiDAR特征及衍生特征结合四种机器学习算法建模。","多源特征融合优于单一特征，随机森林最优（R²=0.923），精度随生育期变化。","可探索生育期自适应建模与特征选择，提升跨期生物量估算的稳健性与可迁移性。","2026-10-20T23:30:03.106267Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":197,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":203,"tags":205,"primary_topic":208,"secondary_topics":209,"classification":210,"search_phrases":233,"slug":236,"view_count":22,"doi":237,"paper":238,"created_at":263},5162,"Detection of Eulecanium giganteum Infestation in Jujube Orchards Based on UAV Multispectral Remote Sensing","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fhorticulturae12101286","The application of remote sensing technology to monitor agricultural pests and diseases provides an effective approach for rapid and non-destructive assessment of crop health. This study evaluated a UAV multispectral remote sensing framework integrating vegetation indices and SPAD information for assessing Eulecanium giganteum infestation in jujube orchards. Initially, 24 vegetation indices were extracted from canopy multispectral imagery, and Pearson correlation analysis was used for feature screening, resulting in a refined subset of 19 vegetation indices. Seven machine learning algorithms were evaluated during model development, with Grid Search combined with tree-level grouped 7-fold cross-validation for hyperparameter selection. Vegetation indices were used as predictor variables, while SPAD value and infestation severity were treated as response variables in the regression and classification tasks, respectively. During internal model development, the CatBoost-19opt model achieved an R2 of 0.81 and an RMSE of 1.84 for SPAD estimation. On the independent test set consisting of 58 jujube trees, the previously selected model achieved an R2 of 0.84 and an RMSE of 1.56. During internal model development, incorporating measured SPAD values improved pest classification performance, with XGBoost-19sopt achieving an OA of 0.89 and a Kappa coefficient of 0.84. On the independent test set, the measured-SPAD configuration achieved an OA of 0.88, whereas both the VI-only and VI combined with predicted-SPAD configurations achieved an OA of 0.71. Thus, incorporating predicted SPAD did not improve overall classification accuracy relative to using vegetation indices alone. Further model optimization and additional validation across different orchards, growing seasons, and phenological stages are required before the approach can be considered for operational large-scale monitoring.","遥感技术在农业病虫害监测中的应用，为作物健康的快速、无损评估提供了有效途径。本研究评估了一种集成植被指数与SPAD信息的无人机多光谱遥感框架，用于评估枣园中枣球蜡蚧（Eulecanium giganteum）的危害情况。首先，从冠层多光谱影像中提取了24个植被指数，并采用Pearson相关分析进行特征筛选，最终得到包含19个植被指数的精简子集。在模型开发过程中评估了7种机器学习算法，采用网格搜索结合树级分组7折交叉验证进行超参数选择。以植被指数作为预测变量，分别将SPAD值和危害程度作为回归任务和分类任务的响应变量。在内部模型开发中，CatBoost-19opt模型在SPAD估算中取得了R²为0.81、RMSE为1.84的结果。在由58株枣树构成的独立测试集上，先前选定的模型取得了R²为0.84、RMSE为1.56的结果。在内部模型开发中，引入实测SPAD值提升了病虫害分类性能，XGBoost-19sopt取得了OA为0.89、Kappa系数为0.84的结果。在独立测试集上，实测SPAD配置取得了OA为0.88的结果，而仅使用植被指数以及植被指数结合预测SPAD的配置均取得了OA为0.71的结果。因此，相对于仅使用植被指数，引入预测SPAD并未提升整体分类精度。在该方法可被视为适用于业务化大规模监测之前，仍需进一步的模型优化，并在不同枣园、生长季和物候期开展额外验证。","Horticulturae","2026-10-16T00:00:00Z",73,{"impact":200,"substance":18,"depth":201,"authority":122,"freshness":21,"relevant":22,"comment":202},12,17,"方法扎实、数据完整的单果园研究，但作者自述尚未达到大规模业务化监测水平，属细分领域进展，可入精选但非重大突破。",[204],{"name":196,"url":193},[27,128,30,206,207],"病虫害防治","枣树",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":211},{"candidates":212,"agricultural_evidence":46},[213,219,223,229],{"code":33,"score":140,"supported":46,"title":214,"abstract":216,"tags":218},[48,215],"multispectral",[148,217,48,215],"无人机多光谱",[30],{"code":151,"score":61,"supported":46,"title":220,"abstract":221,"tags":222},[],[154,155],[],{"code":224,"score":225,"supported":46,"title":226,"abstract":227,"tags":228},"B2",3,[],[207],[],{"code":52,"score":22,"supported":14,"title":230,"abstract":231,"tags":232},[],[],[27],[234,235],"无人机 多光谱 枣园 虫害","Eulecanium giganteum 遥感 监测","无人机多光谱枣园虫害-5162","10.3390\u002Fhorticulturae12101286",{"doi":237,"openalex_id":239,"authors":240,"venue":196,"cited_by_count":101,"oa_url":193,"card":258,"direction":106,"ingested_from":108},"W7222575708",[241,243,245,248,251,253,255],{"name":242,"orcid":9},"Yueteng Chao",{"name":244,"orcid":9},"Yulong Wang",{"name":246,"orcid":247},"Zhuoyang Xu","https:\u002F\u002Forcid.org\u002F0000-0003-0344-3424",{"name":249,"orcid":250},"Ruohong He","https:\u002F\u002Forcid.org\u002F0009-0000-7404-9112",{"name":252,"orcid":9},"Rina Yangdao",{"name":254,"orcid":9},"Hongqiang Dong",{"name":256,"orcid":257},"Ping Li","https:\u002F\u002Forcid.org\u002F0009-0007-4465-1023",{"tldr":259,"method":260,"finding":261,"direction":106,"opportunity":262},"用无人机多光谱植被指数与SPAD估算枣园大球蚧发生程度。","24个植被指数筛选19个，7种机器学习+网格搜索与分组交叉验证。","CatBoost估SPAD R²达0.84；实测SPAD提升分类，预测SPAD无增益。","可探索预测SPAD失效原因及跨果园、物候期泛化与多源数据融合监测。","2026-10-17T23:30:28.025984Z",{"id":265,"title":266,"url":267,"summary":268,"summary_zh":269,"content":9,"source_name":270,"source_url":267,"published_at":271,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":272,"score_detail":273,"sources":276,"tags":278,"primary_topic":280,"secondary_topics":286,"classification":288,"search_phrases":329,"slug":332,"view_count":22,"doi":333,"paper":334,"created_at":346},4896,"Site-Specific Precision Cover Cropping for Soil Health and Environmental Sustainability Using Remote Sensing and Multi-Criteria Analysis","https:\u002F\u002Fdoi.org\u002F10.21663\u002Feeg-d-26-00041","ABSTRACT Cover crops, which are plants or mixtures of plants grown primarily to enhance and protect soil health, are a key strategy in sustainable agriculture. Traditionally, they have been uniformly planted in a field typically during off-season when the main crops are not in competition. However, advancements in precision agriculture, particularly the integration of remote-sensing technology, demand more efficient seeding strategies based on field variability. The aims of this study were to assess the specific needs of a field in Aurora County, SD, and determine the type and optimal combinations of the cover crop mix. The cover crop species considered in this study included legumes, grasses, and brassicas. The remote-sensing data were extracted from Sentinel-2 satellite imagery. Soil samples were collected from 0 to 30 cm depth, and soil properties were measured. Additionally, topographic maps such as slope and aspect were extracted from light detection and ranging (LiDAR) data. The results of precision cover crop analyses showed that 6.5 percent (7.8 ha), 20.6 percent (24.7 ha), 38.1 percent (45.7 ha), 29.6 percent (35.5 ha), and 5.3 percent (6.3 ha) of the studied field should planted in the following mixtures of crops: rye, radish, and vetch; triticale, turnip, and vetch; triticale, radish, and clover; triticale, rapeseed, and pea; and barley, flax, and clover, respectively. The mixtures significantly improved soil properties by reducing compaction, increasing residue C\u002FN ratio, and enhancing weed suppression compared with the control. The resulting map should be interpreted as a decision-support recommendation rather than a fully validated field prescription.","摘要 覆盖作物（cover crops）是指主要为了提升和保护土壤健康而种植的植物或植物混合体，是可持续农业的一项关键策略。传统上，覆盖作物通常在非主作物竞争的季节以均匀方式播种于田间。然而，精准农业的进步，尤其是遥感技术的整合，要求基于田块变异性制定更高效的播种策略。本研究旨在评估南达科他州奥罗拉县某田块的具体需求，并确定覆盖作物混合物的类型及最优组合。本研究考虑的覆盖作物种类包括豆科、禾本科和十字花科植物。遥感数据提取自Sentinel-2卫星影像。土壤样品采集深度为0至30 cm，并测定了土壤性质。此外，从激光雷达（LiDAR）数据中提取了坡度和坡向等地形图。精准覆盖作物分析结果表明，研究田块中分别有6.5%（7.8 ha）、20.6%（24.7 ha）、38.1%（45.7 ha）、29.6%（35.5 ha）和5.3%（6.3 ha）的面积应种植以下作物混合物：黑麦、萝卜和野豌豆；小黑麦、芜菁和野豌豆；小黑麦、萝卜和三叶草；小黑麦、油菜和豌豆；以及大麦、亚麻和三叶草。与对照相比，这些混合物通过降低紧实度、提高残体碳氮比和增强杂草抑制效果，显著改善了土壤性质。所得图谱应被解读为决策支持建议，而非经过充分验证的田间处方。","Environmental and Engineering Geoscience","2026-10-13T00:00:00Z",72,{"impact":200,"substance":274,"depth":201,"authority":122,"freshness":21,"relevant":22,"comment":275},21,"该研究将遥感与多准则分析结合用于精准覆盖作物制图，方法新颖、数据具体，对农业信息化与可持续农业有参考价值，但属单点田间案例，影响范围有限。",[277],{"name":270,"url":267},[27,56,148,279,29],"土壤健康",{"code":281,"slug":282,"name":283,"path":284,"parent_code":285},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[287],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":289},{"candidates":290,"agricultural_evidence":46},[291,297,301,305,312,319,325],{"code":281,"score":292,"supported":46,"title":293,"abstract":294,"tags":296},15,[282],[295,282],"土壤",[279],{"code":33,"score":200,"supported":46,"title":298,"abstract":299,"tags":300},[48],[148],[148],{"code":52,"score":123,"supported":46,"title":302,"abstract":303,"tags":304},[],[56,57],[27,56],{"code":306,"score":61,"supported":46,"title":307,"abstract":308,"tags":311},"A4",[],[309,310],"油菜","rapeseed",[],{"code":313,"score":61,"supported":46,"title":314,"abstract":315,"tags":318},"H8",[],[316,317],"可持续农业","sustainable agriculture",[],{"code":320,"score":225,"supported":46,"title":321,"abstract":322,"tags":324},"B1",[],[323],"萝卜",[],{"code":75,"score":22,"supported":14,"title":326,"abstract":327,"tags":328},[],[],[29],[330,331],"Sentinel-2 遥感 覆盖作物","精准覆盖作物 土壤健康","Sentinel-2遥感覆盖作物-4896","10.21663\u002Feeg-d-26-00041",{"doi":333,"openalex_id":335,"authors":336,"venue":270,"cited_by_count":101,"oa_url":9,"card":341,"direction":106,"ingested_from":108},"W7221669942",[337,339],{"name":338,"orcid":9},"Ali Mirzakhani Nafchi",{"name":340,"orcid":9},"Salman Mirzaee",{"tldr":342,"method":343,"finding":344,"direction":106,"opportunity":345},"利用遥感与多准则分析为南达科他州田块制定精准覆盖作物混播方案。","Sentinel-2影像、LiDAR地形与土壤采样，结合多准则分析。","田块被划分为5个管理区，各推荐不同覆盖作物混播，显著改善土壤紧实度、残体C\u002FN比和抑草效果。","可验证该决策支持地图的田间效果，并融合实时物联网数据优化动态推荐。","2026-10-14T23:30:27.077312Z",{"id":348,"title":349,"url":350,"summary":351,"summary_zh":352,"content":9,"source_name":353,"source_url":350,"published_at":354,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":120,"score_detail":355,"sources":357,"tags":359,"primary_topic":362,"secondary_topics":367,"classification":373,"search_phrases":397,"slug":400,"view_count":22,"doi":401,"paper":402,"created_at":423},4808,"Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1898342","Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.","农业病害是种植者面临的重大问题，会对果实和叶片造成严重损害，导致产量损失。因此，开发植物病害早期检测系统是一个至关重要的问题。本研究提出了一种基于深度学习的新型混合模型，用于植物病害的检测与分割，具体针对苹果园中的苹果黑星病和葡萄园中的霜霉病。该早期病害检测系统的开发使用了2023年5月至2024年12月期间通过无人机在土耳其泰基尔达省农业区域采集的多光谱影像数据。数据集包含3625幅苹果园图像和5625幅葡萄园图像。所用DJI Phantom 4多光谱无人机配备五个光谱波段（蓝、绿、红、红边和近红外），用于捕捉光谱丰富度和时间变化过程。本研究提出了一种专门开发的深度学习架构，用于基于时间序列的多光谱无人机影像病害检测与分割。植物随时间的变化提供了关于植物病害发生的重要信息。因此，本研究使用时间序列从植物随时间的变化中提取特征。所提出的模型架构专门设计以利用时间序列信息，从而能够稳健地跟踪作物健康状况随时间的变化。使用苹果病害数据集训练的模型达到了82.05%的平均交并比（IoU）值，而使用葡萄园病害数据集训练的模型达到了79.67%的IoU值。研究结果凸显了将无人机获取的多光谱时间序列数据与先进深度学习模型相结合，在精准农业中实现可扩展、准确且及时的植物病害检测方面的有效性。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",{"impact":17,"substance":274,"depth":19,"authority":122,"freshness":21,"relevant":22,"comment":356},"该研究提出基于多光谱时序影像的新型深度学习模型，实现果园与葡萄园病害早期检测，数据规模大、方法新颖，对精准农业病害预警具有参考价值。",[358],{"name":353,"url":350},[27,128,360,30,361],"农业人工智能","病害预警",{"code":224,"slug":363,"name":364,"path":365,"parent_code":366},"fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[368,372],{"code":151,"slug":369,"name":370,"path":371,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":374},{"candidates":375,"agricultural_evidence":46},[376,383,389,393],{"code":224,"score":201,"supported":46,"title":377,"abstract":379,"tags":382},[378],"apple",[380,381,378],"苹果","葡萄",[],{"code":151,"score":292,"supported":46,"title":384,"abstract":386,"tags":388},[385],"deep learning",[387,385],"深度学习",[360],{"code":33,"score":200,"supported":46,"title":390,"abstract":391,"tags":392},[215],[215],[30],{"code":52,"score":53,"supported":46,"title":394,"abstract":395,"tags":396},[],[56,57],[27],[398,399],"苹果黑星病 无人机 多光谱","葡萄园 霜霉病 深度学习","苹果黑星病无人机多光谱-4808","10.15832\u002Fankutbd.1898342",{"doi":401,"openalex_id":403,"authors":404,"venue":353,"cited_by_count":101,"oa_url":416,"card":417,"direction":422,"ingested_from":108},"W7221558726",[405,408,411,413],{"name":406,"orcid":407},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":409,"orcid":410},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":412,"orcid":9},"Fatih Sinan Bir",{"name":414,"orcid":415},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":418,"method":419,"finding":420,"direction":106,"opportunity":421},"提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。","DJI Phantom 4多光谱无人机采集时序影像，构建时序深度学习分割模型。","苹果病IoU达82.05%，葡萄病达79.67%，验证多光谱时序数据有效性。","可探索多作物、多病害的时序多光谱通用模型，并融合气象与地面数据提升早期预警能力。","智慧农业 \u002F 农业物联网","2026-10-13T23:30:10.242467Z",{"id":425,"title":426,"url":427,"summary":428,"summary_zh":9,"content":429,"source_name":430,"source_url":9,"published_at":431,"category":432,"cover_url":9,"hotness":13,"is_selected":14,"score":433,"score_detail":434,"sources":437,"tags":439,"primary_topic":443,"secondary_topics":444,"classification":445,"search_phrases":462,"slug":465,"view_count":225,"doi":9,"paper":9,"created_at":466},4612,"国家级改革试验任务,为何选中了苏州高新区的作物云?","http:\u002F\u002Fwww.snd.gov.cn\u002Fhqqrmzf\u002Fzwxw\u002F202610\u002F89525c03b91346fc86441fa304fcf2d4.shtml","农业农村部、国家发展改革委等10部委批复2026年全国农村改革试验区拓展试验任务,苏州市申报的探索应用作物云数智管理技术精准服务粮食生产成功入选。该平台用1部手机、2张照片、3类信息,围绕适期播栽、适情管理、适熟估产、适时收获四个环节提供精准调度;2025年稻麦季面向苏州8个涉农县区累计发布农情简报96期,服务指导农户4.1万户次。核查准确度接近100%,惠农补贴发放、农技指导环节实现升级。","陆洲介绍，在惠农补贴发放环节，系统能够精准识别作物种类并核查面积，核查准确度接近100%，有效防范虚报造假。在农技指导环节，“作物云”平台依托“AI+遥感+气象数据”，开展稻麦全生命周期观测与预测，为农户提供“一田一策”个性化种植方案。此外，平台联合农业保险机构，实现作物“精准承保、精准理赔”，从源头构建可信、可溯的数据底座，使每一亩地都有清晰的身份画像；构建“天空地”一体化查勘定损体系，实现灾害应对的快速响应、精准核损。","苏州高新区管委会 2026-10-09","2026-10-09T00:00:00Z","报道",75,{"impact":18,"substance":435,"depth":17,"authority":21,"freshness":123,"relevant":22,"comment":436},20,"国家级农村改革试验区落地案例，作物云在补贴核查、农技指导与保险理赔上给出可量化成效，具备行业示范价值。",[438],{"name":430,"url":427},[27,440,30,441,442],"农业保险","农村改革试验区","作物云",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":446},{"candidates":447,"agricultural_evidence":46},[448,453,458],{"code":33,"score":69,"supported":46,"title":449,"abstract":450,"tags":452},[],[451],"估产",[30],{"code":454,"score":22,"supported":14,"title":455,"abstract":456,"tags":457},"G3",[],[],[440],{"code":52,"score":22,"supported":14,"title":459,"abstract":460,"tags":461},[],[],[27],[463,464],"苏州高新区 作物云","作物云 精准承保 遥感","苏州高新区作物云-4612","2026-10-11T00:09:35.550412Z",{"id":468,"title":469,"url":470,"summary":471,"summary_zh":472,"content":9,"source_name":473,"source_url":470,"published_at":474,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":475,"score_detail":476,"sources":479,"tags":481,"primary_topic":484,"secondary_topics":485,"classification":492,"search_phrases":536,"slug":539,"view_count":540,"doi":541,"paper":542,"created_at":560},4575,"A review of remote sensing for agricultural drought: technologies, indices, modelling advances and future prospects","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2738981","Agricultural drought is a complex hazard that jeopardises food security and progress toward the Sustainable Development Goals. We systematically reviewed peer-reviewed English-language literature published between 2015 and 2025, focusing on Remote Sensing (RS)-driven agricultural drought monitoring and prediction. Eligible studies were identified from the Web of Science and Scopus databases (last searched in August 2025); review articles, duplicates, and irrelevant studies were excluded, resulting in 181 articles. This review critically examined the evolution of sensors, drought indices, modelling approaches, and emerging technologies used to assess agricultural drought across diverse agro-ecological regions. Specifically, we (1) systematically evaluated the use of optical, thermal, and microwave RS platforms, along with satellite-based, model-based, and reanalysis products for agricultural drought monitoring; (2) reviewed widely used RS-based drought indices, including single indices grouped into nine categories, as well as composite indices; (3) reviewed software or programming languages applied; (4) assessed the progression of modelling approaches from traditional statistical and time-series methods to machine learning (ML) and deep learning (DL) frameworks; and (5) examined validation strategies applied to agricultural drought assessment. The analysis reveals a growing research interest in this topic, with a strong geographic concentration in Asia, particularly China. Optical and thermal platforms (e.g. MODIS and Landsat) dominate the literature (~54%) due to their accessibility, followed by satellite-derived precipitation products (17%), while microwave soil-moisture missions remain underutilized (~7%). Regarding RS-based indices, most studies employed more than one index: 44% analysed multiple individual indices, 51% used composite indices (either alone or in combination with individual indices), and only 5% relied on a single index. Vegetation indices, such as NDVI, remain workhorses; however, their sensitivity to soil background effects and canopy saturation has driven increased adoption of thermal composites (e.g. VHI), as well as adaptive weighting schemes and principal component analysis-based approaches. Moreover, computational tools used in drought research can be broadly grouped into geospatial and RS platforms for large-scale data processing and analysis (e.g. QGIS, GEE), and general-purpose programming languages (e.g. R, Python) for model development and advanced analytics. Regarding the methodology, statistical and time-series approaches account for 64% of the reviewed studies, followed by ML models (20%) and DL methods (15%), while explainable artificial intelligence (XAI) remains nascent. Techniques, such as random forests, support vector machines, and CNN and LSTM architectures, generally outperform traditional models but require large training datasets and raise concerns related to interpretability and ethics. In terms of validation, hydro-climatic indices such as SPI and SPEI are most commonly used (32%), followed by crop yield and yield anomaly data (19%). This review further identifies key research gaps across sensors, indices, and modelling approaches and proposes a roadmap for developing equitable, interpretable, and resilient agricultural drought monitoring systems. Emerging opportunities include context-aware and interpretable AI, multi-sensor and multi-scale data fusion across optical, thermal, and microwave domains, integration of physiologically meaningful indicators such as sun-induced chlorophyll fluorescence (SIF), robust uncertainty quantification, and the development of hybrid and physics-informed modelling frameworks.","农业干旱是一种复杂的灾害，威胁粮食安全和可持续发展目标的实现。我们系统综述了2015年至2025年间发表的同行评议英文文献，聚焦于遥感（RS）驱动的农业干旱监测与预测。符合条件的研究通过Web of Science和Scopus数据库检索确定（最后检索时间为2025年8月）；综述文章、重复文献及不相关研究被排除，最终纳入181篇文献。本综述批判性地审视了用于评估不同农业生态区农业干旱的传感器、干旱指数、建模方法及新兴技术的演变。具体而言，我们（1）系统评估了光学、热红外和微波遥感平台，以及基于卫星、基于模型和再分析产品在农业干旱监测中的应用；（2）综述了广泛使用的基于遥感的干旱指数，包括归为九类的单一指数以及复合指数；（3）综述了所应用的软件或编程语言；（4）评估了建模方法从传统统计和时间序列方法向机器学习（ML）和深度学习（DL）框架的演进；（5）考察了应用于农业干旱评估的验证策略。分析显示，该主题的研究兴趣日益增长，地理上高度集中于亚洲，尤其是中国。光学和热红外平台（如MODIS和Landsat）因其可获取性在文献中占主导地位（约54%），其次是卫星降水产品（17%），而微波土壤水分任务仍未得到充分利用（约7%）。在基于遥感的指数方面，大多数研究采用了不止一种指数：44%分析了多个单一指数，51%使用了复合指数（单独使用或与单一指数结合使用），仅5%依赖单一指数。植被指数（如NDVI）仍是主力工具；然而，其对土壤背景效应和冠层饱和的敏感性推动了热红外复合指数（如VHI）以及自适应权重方案和基于主成分分析的方法得到更多采用。此外，干旱研究中使用的计算工具可大致分为用于大规模数据处理和分析的地理空间与遥感平台（如QGIS、GEE），以及用于模型开发的通用编程语言（如R、Python）。","GIScience & Remote Sensing","2026-10-08T00:00:00Z",81,{"impact":19,"substance":18,"depth":477,"authority":20,"freshness":123,"relevant":22,"comment":478},19,"系统综述181篇文献，梳理遥感干旱监测的传感器、指数与建模进展，方法学价值高，但属学术综述，产业落地影响有限。",[480],{"name":473,"url":470},[27,482,154,30,483],"粮食安全","农业干旱",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[486,491],{"code":487,"slug":488,"name":489,"path":490,"parent_code":285},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":151,"slug":369,"name":370,"path":371,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":493},{"candidates":494,"agricultural_evidence":46},[495,499,505,510,516,520,526,532],{"code":33,"score":292,"supported":46,"title":496,"abstract":497,"tags":498},[48],[148,48],[30],{"code":487,"score":20,"supported":46,"title":500,"abstract":502,"tags":504},[501],"drought",[503,501],"干旱",[],{"code":151,"score":13,"supported":46,"title":506,"abstract":507,"tags":509},[],[387,154,155,385,508],"artificial intelligence",[154],{"code":511,"score":53,"supported":46,"title":512,"abstract":513,"tags":515},"A8",[],[482,514],"food security",[482],{"code":281,"score":61,"supported":46,"title":517,"abstract":518,"tags":519},[],[295,282],[],{"code":521,"score":225,"supported":46,"title":522,"abstract":523,"tags":525},"D",[],[524],"水产",[],{"code":527,"score":225,"supported":46,"title":528,"abstract":529,"tags":531},"H6",[],[530],"农业生态",[],{"code":52,"score":22,"supported":14,"title":533,"abstract":534,"tags":535},[],[],[27],[537,538],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575",2,"10.1080\u002F15481603.2026.2738981",{"doi":541,"openalex_id":543,"authors":544,"venue":473,"cited_by_count":101,"oa_url":554,"card":555,"direction":422,"ingested_from":108},"W7221081979",[545,548,551],{"name":546,"orcid":547},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":549,"orcid":550},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":552,"orcid":553},"Olena Dubovyk","https:\u002F\u002Forcid.org\u002F0000-0002-7338-3167","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15481603.2026.2738981?needAccess=true",{"tldr":556,"method":557,"finding":558,"direction":106,"opportunity":559},"系统综述2015-2025年遥感农业干旱监测与预测的传感器、指数、建模及验证进展。","基于Web of Science与Scopus的181篇文献系统综述，涵盖光学\u002F","光学热红外平台占主导，微波土壤水分利用不足；复合指数与机器学习建模成为趋势。","微波土壤水分数据融合与深度学习干旱预测模型在数据稀缺区验证是潜在研究空白。","2026-10-10T23:30:14.933078Z"]