[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3838":3,"related-3838":88},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":68,"slug":71,"view_count":72,"doi":73,"paper":74,"created_at":87},3838,"Forecasting Cotton Productivity in the Mil-Mugan Economic Region using Climate Variables and Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.52171\u002Fherald.342","Satellite imagery is considered as one of the most valuable and functional data sources for evaluating and forecasting production in agricultural systems. This study aims to forecast productivity in cotton fields within the Mil-Mugan economic region by incorporating vegetation indices and climatic variables. NDVI indicators were derived from Sentinel-2 data, temperature (T) was obtained from ERA5-Land data, and precipitation (R) was collected from the CHIRPS database. The data were analyzed using Google Earth Engine, ArcGIS Pro, and Python environments, consolidating them into an integrated temporal-spatial dimension. The correlation and regression analysis assessed the link among NDVI, temperature, precipitation, and productivity, leading to the development of a multivariate model. The findings derived from the model indicate that NDVI is the primary factor influencing productivity. The model developed for one region was implemented in the other three locations, and the correlation between the actual and predicted indicators was assessed using R² and RMSE metrics. The results indicate that the model's forecast accuracy fluctuates within the 90% range, considered appropriate for predicting regional cotton yield. The study's findings are significant for enhancing operational monitoring and planning of agrotechnical decisions with satellite data.","卫星影像被认为是评估和预测农业系统产量最有价值且最实用的数据来源之一。本研究旨在通过整合植被指数与气候变量，预测米尔-穆甘经济区棉田的生产力。NDVI指标源自Sentinel-2数据，温度（T）取自ERA5-Land数据，降水（R）来自CHIRPS数据库。数据在Google Earth Engine、ArcGIS Pro和Python环境中进行分析，并将其整合为统一的时空维度。通过相关性与回归分析评估了NDVI、温度、降水与生产力之间的关系，进而构建了多变量模型。模型结果表明，NDVI是影响生产力的主要因素。将为某一区域开发的模型应用于其他三个地点，并使用R²和RMSE指标评估实际值与预测值之间的相关性。结果表明，模型的预测精度在90%范围内波动，被认为适用于预测区域棉花产量。本研究结果对于利用卫星数据加强农艺决策的业务化监测与规划具有重要意义。",null,"Herald of the Azerbaijan Engineering Academy","2026-09-29T00:00:00Z","论文",10,false,63,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},8,20,16,9,1,"基于Sentinel-2与气候变量的棉花产量预测模型，方法扎实、精度达90%，但属区域案例研究，公共影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","产量预测","棉花","遥感","NDVI",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,57,63],{"code":32,"score":51,"supported":52,"title":53,"abstract":55,"tags":56},15,true,[54],"cotton",[28,54],[28],{"code":39,"score":58,"supported":52,"title":59,"abstract":61,"tags":62},11,[60],"remote sensing",[],[30,27,29],{"code":64,"score":21,"supported":14,"title":65,"abstract":66,"tags":67},"I1",[],[],[26],[69,70],"Mil-Mugan 棉花 遥感","Sentinel-2 NDVI 棉花产量","Mil-Mugan棉花遥感-3838",3,"10.52171\u002Fherald.342",{"doi":73,"openalex_id":75,"authors":76,"venue":10,"cited_by_count":79,"oa_url":6,"card":80,"direction":84,"ingested_from":86},"W7214748696",[77],{"name":78,"orcid":9},"B.H. Babashli",0,{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"结合Sentinel-2 NDVI与气候变量，构建多元模型预测米尔-穆甘地区棉花产量。","Sentinel-2 NDVI、ERA5-Land温度、CHIRPS降水，GEE","NDVI是产量主控因子，模型跨区预测精度约90%，可用于区域估产。","农业遥感与作物表型","可引入多时相NDVI与机器学习，提升跨区域迁移性和极端气候下的估产鲁棒性。","openalex","2026-09-30T23:30:27.786044Z",{"total":89,"page":21,"page_size":89,"items":90},6,[91,151,242,309,364,435],{"id":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":9,"source_name":97,"source_url":94,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":100,"sources":104,"tags":106,"primary_topic":109,"secondary_topics":110,"classification":112,"search_phrases":128,"slug":131,"view_count":132,"doi":133,"paper":134,"created_at":150},3179,"Multi-features and Fuzzy with Residual Lenet for Cotton Classification Using Remote Sensing Image","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02541-8","Multi-features and Fuzzy with Residual Lenet for Cotton Classification Using Remote Sensing Image。Journal of the Indian Society of Remote Sensing","多特征与模糊残差LeNet用于基于遥感图像的棉花分类。《印度遥感学会杂志》","Journal of the Indian Society of Remote Sensing","2026-09-21T00:00:00Z",58,{"impact":17,"substance":101,"depth":51,"authority":102,"freshness":20,"relevant":21,"comment":103},14,12,"方法类论文，提出多特征与模糊结合残差LeNet用于遥感棉花分类，专业价值尚可但影响面窄，适合作为技术参考而非每日精选头条。",[105],{"name":97,"url":94},[107,28,29,108],"农业人工智能","作物分类",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[111],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":113},{"candidates":114,"agricultural_evidence":52},[115,119,123],{"code":39,"score":19,"supported":52,"title":116,"abstract":117,"tags":118},[60],[29,60],[108,29],{"code":32,"score":51,"supported":52,"title":120,"abstract":121,"tags":122},[54],[28,54],[28],{"code":124,"score":21,"supported":14,"title":125,"abstract":126,"tags":127},"I2",[],[],[107],[129,130],"Residual LeNet 棉花 遥感分类","农业人工智能 作物分类 棉花 遥感","ResidualLeNet棉花遥感分类-3179",5,"10.1007\u002Fs12524-026-02541-8",{"doi":133,"openalex_id":135,"authors":136,"venue":97,"cited_by_count":79,"oa_url":9,"card":145,"direction":84,"ingested_from":86},"W7213944040",[137,139,141,143],{"name":138,"orcid":9},"G. Suresh",{"name":140,"orcid":9},"G. Bhuvaneswari",{"name":142,"orcid":9},"G. Manikandan",{"name":144,"orcid":9},"M. Robinson Joel",{"tldr":146,"method":147,"finding":148,"direction":84,"opportunity":149},"提出多特征与模糊结合残差LeNet的遥感影像棉花分类方法。","多特征提取、模糊逻辑与残差LeNet网络，用于遥感图像分类。","该方法能有效提升棉花分类精度，验证了深度残差网络结合模糊特征的可行性。","可探索多特征与模糊深度学习在其他作物遥感分类中的泛化能力及轻量化部署。","2026-09-22T23:30:23.811658Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":9,"source_name":157,"source_url":154,"published_at":158,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":159,"score_detail":160,"sources":165,"tags":167,"primary_topic":171,"secondary_topics":172,"classification":174,"search_phrases":203,"slug":206,"view_count":207,"doi":208,"paper":209,"created_at":241},3014,"Detecting diurnal dynamics of cotton leaf inclination angle under water-salt stress","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115674","Leaf inclination angle (LIA) dynamics act as a rapid response mechanism to abiotic stress, regulating canopy energy balance and water use efficiency. While the adaptive value of diurnal LIA plasticity (e.g., paraheliotropism) is well-recognized in ecology, most current remote sensing algorithms and ecosystem models still treat canopy architecture as static and neglect stress-induced geometric adjustments. Furthermore, the diurnal dynamics of LIA under combined abiotic stresses, such as concurrent water deficit and salinity, still remain poorly understood. Recent advances in unmanned aerial vehicle (UAV) photogrammetry offer a promising approach for capturing LIA dynamics at high spatial and temporal resolution. However, accurately resolving fine scale, dynamic leaf movements in real environments using UAVs remains challenging. To address these gaps, we developed the Constraint-Assisted Point cloud fusion for Leaf scale Analysis (CAPLA), an integrated UAV analytical workflow that combines deep learning with Structure from Motion (SfM). CAPLA employs 2D semantic masks to strictly constrain 3D mesh reconstruction, effectively mitigating motion-induced artifacts. Independent validation against 19 plot level mean leaf angle (MLA) observations collected at 9:30 am and 12:00 pm yielded an R 2 of 0.89 and an RMSE of 0.9°, supporting plot level MLA estimation under the validated acquisition conditions. CAPLA was subsequently applied across five observation times to characterize diurnal canopy structural dynamics. Importantly, repeated measures analysis of the high frequency observations revealed significant effects of irrigation, salinity, and observation time on MLA, together with a significant irrigation × time interaction ( P = 0.0109), indicating that diurnal MLA trajectories differed among irrigation levels. In contrast, neither the irrigation × salinity interaction ( P = 0.8800) nor the irrigation × salinity × time interaction ( P = 0.9086) was significant. Descriptive differences in within-day variability were nevertheless observed among individual treatment combinations, highlighting the value of time-resolved structural monitoring for characterizing canopy responses to combined water and salinity stresses. These findings highlight the complex structural plasticity of canopies under interacting stresses, emphasizing the critical need to transition from static canopy assumptions to dynamic structural monitoring for improving ecosystem models and precision agriculture.","叶片倾角（LIA）动态变化是植物对非生物胁迫的快速响应机制，调控冠层能量平衡与水分利用效率。尽管昼夜LIA可塑性（如避日运动）的适应价值在生态学中已得到广泛认可，但当前大多数遥感算法和生态系统模型仍将冠层结构视为静态，忽略了胁迫诱导的几何调整。此外，在水分亏缺与盐分胁迫等复合非生物胁迫条件下，LIA的昼夜动态变化仍知之甚少。近年来无人机（UAV）摄影测量技术的进展为在高时空分辨率下捕捉LIA动态提供了有前景的方法。然而，利用无人机在真实环境中精确解析精细尺度的动态叶片运动仍具挑战性。为弥补上述不足，我们开发了约束辅助点云融合叶片尺度分析流程（CAPLA），这是一种集成了深度学习与运动恢复结构（SfM）的无人机综合分析工作流。CAPLA利用二维语义掩膜严格约束三维网格重建，有效减轻了运动诱导的伪影。基于上午9：30和中午12：00采集的19个样地水平平均叶倾角（MLA）观测值进行独立验证，结果R²为0.89，RMSE为0.9°，支持在验证采集条件下进行样地水平MLA估算。随后将CAPLA应用于五个观测时段以表征冠层结构的昼夜动态变化。重要的是，对高频观测的重复测量分析揭示了灌溉、盐分和观测时间对MLA的显著影响，以及显著的灌溉×时间交互效应（P = 0.0109），表明不同灌溉水平下MLA的昼夜变化轨迹存在差异。相比之下，灌溉×盐分交互效应（P = 0.8800）和灌溉×盐分×时间交互效应（P = 0.9086）均不显著。尽管如此，在各处理组合之间仍观察到日内变异性的描述性差异，凸显了时间分辨结构监测在表征冠层对水分与盐分复合胁迫响应方面的价值。这些发现揭示了冠层在交互胁迫下的复杂结构可塑性，强调亟需从静态冠层假设转向动态结构监测，以改进生态系统模型和精准农业。","Remote Sensing of Environment","2026-09-19T00:00:00Z",84,{"impact":161,"substance":162,"depth":163,"authority":51,"freshness":20,"relevant":21,"comment":164},18,23,19,"该研究提出CAPLA无人机点云融合方法，实现水盐胁迫下棉花叶倾角昼夜动态的高精度监测，方法新颖、数据可靠，对作物表型与精准农业有实质参考价值。",[166],{"name":157,"url":154},[26,168,28,169,170],"农业遥感","精准农业","无人机遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[173],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":175},{"candidates":176,"agricultural_evidence":52},[177,181,186,190,197],{"code":32,"score":20,"supported":52,"title":178,"abstract":179,"tags":180},[54],[],[28],{"code":64,"score":17,"supported":52,"title":182,"abstract":183,"tags":185},[],[169,184],"precision agriculture",[26,169],{"code":39,"score":17,"supported":52,"title":187,"abstract":188,"tags":189},[],[29,60],[168,170],{"code":191,"score":89,"supported":52,"title":192,"abstract":193,"tags":196},"H3",[],[194,195],"灌溉","irrigation",[],{"code":124,"score":89,"supported":52,"title":198,"abstract":199,"tags":202},[],[200,201],"深度学习","deep learning",[],[204,205],"无人机 棉花 叶倾角 水盐胁迫","CAPLA 冠层结构 动态监测","无人机棉花叶倾角水盐胁迫-3014",4,"10.1016\u002Fj.rse.2026.115674",{"doi":208,"openalex_id":210,"authors":211,"venue":157,"cited_by_count":79,"oa_url":154,"card":236,"direction":84,"ingested_from":86},"W7213661690",[212,215,217,219,221,223,226,229,231,234],{"name":213,"orcid":214},"Qing Li","https:\u002F\u002Forcid.org\u002F0009-0004-4580-7761",{"name":216,"orcid":9},"Dalei Hao",{"name":218,"orcid":9},"Jan Pisek",{"name":220,"orcid":9},"Zicheng Ji",{"name":222,"orcid":9},"Yanan Wei",{"name":224,"orcid":225},"Youngryel Ryu","https:\u002F\u002Forcid.org\u002F0000-0001-6238-2479",{"name":227,"orcid":228},"Jiarui Xu","https:\u002F\u002Forcid.org\u002F0000-0003-4925-2770",{"name":230,"orcid":9},"Yangmin Feng",{"name":232,"orcid":233},"Shaozhong Kang","https:\u002F\u002Forcid.org\u002F0000-0002-8019-2537",{"name":235,"orcid":9},"Yelu Zeng",{"tldr":237,"method":238,"finding":239,"direction":84,"opportunity":240},"提出CAPLA无人机点云融合方法，监测水盐胁迫下棉花叶倾角昼夜动态。","结合深度学习与SfM，用2D语义掩膜约束3D网格重建，无人机高频观测。","灌溉、盐分和时间显著影响叶倾角，灌溉×时间交互显著，昼夜轨迹因灌溉而异。","可将动态叶倾角参数化嵌入作物模型，提升水盐胁迫下冠层结构与蒸散模拟精度。","2026-09-20T23:30:21.262698Z",{"id":243,"title":244,"url":245,"summary":246,"summary_zh":247,"content":9,"source_name":248,"source_url":245,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":249,"score_detail":250,"sources":253,"tags":255,"primary_topic":257,"secondary_topics":261,"classification":263,"search_phrases":285,"slug":288,"view_count":72,"doi":289,"paper":290,"created_at":308},3177,"A comparative analysis on maize yield prediction using sentinel 2A and Landsat 8 satellite image in Sundarganj, Gaibandha, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbjar.v51i1.92530","Maize is an important cereal crops in Bangladesh. Over the last two decades, its cultivation has increased promisingly, especially in the Northern part of Bangladesh. The effective estimation of crop yields at a regional scale holds significant importance in facilitating decision-making within the agricultural sector, thereby ensuring grain security. The traditional ground-based measurement techniques suffer from inefficiencies, and there exists a need for a reliable, precise, and effective method for estimating regional crop yields. This study used remote sensing (RS) techniques for forecasting pre-harvest maize yield to improve the management system. Currently, the normalized difference vegetation index (NDVI) is widely used to predict crop yield including maize. However, the present study used Landsat 8 (~ 30 m) and Sentinel 2A (~ 10 m) high resolution data for 2018-2019 and 2019-2020 to predict maize yield based on the year 2020-2021 at Sundarganj Upazila in Gaibandha district. The single cloud free image acquisition date based on maximum NDVI for both satellite images was used for each maize growing period to develop a yield prediction model. A regression model was performed between NDVI values and 20 farmers field-level maize yields. The absolute mean error of prediction was about 10.30% for Landsat 8 and 6.70% for Sentinel 2A compared to the actual maize yield during 2020-2021. The study revealed that NDVI data extracted from Sentinel 2A high resolution satellite images can be successfully used to predict the maize yield with appreciable accuracy. Finally, this study has demonstrated the efficacy of combining multi-temporal remote sensing data for accurate maize yield estimation, aiding agricultural authorities and production enterprises in the timely formulation and refinement of cropping strategies and management policies for the ongoing season. Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","玉米是孟加拉国重要的谷类作物。过去二十年间，其种植面积增长显著，尤其是在孟加拉国北部地区。在区域尺度上有效估算作物产量对于促进农业部门决策、进而保障粮食安全具有重要意义。传统的地面测量技术效率低下，亟需一种可靠、精确且有效的区域作物产量估算方法。本研究采用遥感（RS）技术预测收获前玉米产量，以改进管理体系。目前，归一化植被指数（NDVI）被广泛用于预测包括玉米在内的作物产量。然而，本研究利用Landsat 8（约30 m）和Sentinel 2A（约10 m）高分辨率数据，基于2018—2019年和2019—2020年的数据，对盖班达县孙达尔甘杰乌帕齐拉2020—2021年的玉米产量进行预测。在每个玉米生长期，选取两颗卫星影像中NDVI最大值对应的单幅无云影像获取日期，用于建立产量预测模型。对NDVI值与20户农民田块级玉米产量进行回归建模。与2020—2021年实际玉米产量相比，Landsat 8的绝对平均预测误差约为10.30%，Sentinel 2A约为6.70%。研究表明，利用Sentinel 2A高分辨率卫星影像提取的NDVI数据可成功用于预测玉米产量，且精度令人满意。最后，本研究证明了结合多时相遥感数据进行准确玉米产量估算的有效性，有助于农业主管部门和生产企业在当季及时制定和完善种植策略与管理政策。Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","Bangladesh Journal of Agricultural Research",65,{"impact":17,"substance":18,"depth":19,"authority":251,"freshness":17,"relevant":21,"comment":252},13,"基于Sentinel 2A与Landsat 8的玉米遥感估产对比研究，方法清晰、误差数据具体，对遥感估产有参考价值，但属区域小尺度研究，公共影响有限。",[254],{"name":248,"url":245},[26,27,256,29,30],"玉米",{"code":258,"slug":259,"name":256,"path":260,"parent_code":36},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize",[262],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":264},{"candidates":265,"agricultural_evidence":52},[266,270,274,281],{"code":258,"score":51,"supported":52,"title":267,"abstract":268,"tags":269},[259],[256,259],[256],{"code":39,"score":20,"supported":52,"title":271,"abstract":272,"tags":273},[],[29,60],[30,27,29],{"code":275,"score":89,"supported":52,"title":276,"abstract":277,"tags":280},"A8",[],[278,279],"粮食安全","grain security",[],{"code":64,"score":21,"supported":14,"title":282,"abstract":283,"tags":284},[],[],[26],[286,287],"Sentinel 2A Landsat 8 玉米产量预测","孟加拉国 Sundarganj 玉米遥感估产","Sentinel2ALandsat8玉米产量预测-3177","10.3329\u002Fbjar.v51i1.92530",{"doi":289,"openalex_id":291,"authors":292,"venue":248,"cited_by_count":79,"oa_url":245,"card":303,"direction":84,"ingested_from":86},"W7213918280",[293,295,297,299,301],{"name":294,"orcid":9},"N Mohammad",{"name":296,"orcid":9},"MA Islam",{"name":298,"orcid":9},"MG Mahboob",{"name":300,"orcid":9},"MM Rahman",{"name":302,"orcid":9},"I Ahmed",{"tldr":304,"method":305,"finding":306,"direction":84,"opportunity":307},"用Sentinel 2A与Landsat 8的NDVI回归模型预测孟加拉国玉米产量并比较精度。","基于最大NDVI单期影像与20个农户地块产量做回归，比较两种卫星。","Sentinel 2A预测绝对平均误差6.70%，优于Landsat 8的10.30%。","可探索多时相NDVI与机器学习融合，提升小农户尺度玉米估产精度与迁移性。","2026-09-22T23:30:23.551878Z",{"id":310,"title":311,"url":312,"summary":313,"summary_zh":314,"content":9,"source_name":315,"source_url":312,"published_at":316,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":317,"score_detail":318,"sources":321,"tags":323,"primary_topic":325,"secondary_topics":329,"classification":331,"search_phrases":348,"slug":351,"view_count":79,"doi":352,"paper":353,"created_at":363},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",67,{"impact":17,"substance":18,"depth":319,"authority":102,"freshness":13,"relevant":21,"comment":320},17,"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[322],{"name":315,"url":312},[26,107,27,29,324],"可解释性",{"code":124,"slug":326,"name":327,"path":328,"parent_code":43},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[330],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":332},{"candidates":333,"agricultural_evidence":52},[334,340,344],{"code":124,"score":102,"supported":52,"title":335,"abstract":337,"tags":339},[336],"computer vision",[338],"计算机视觉",[107],{"code":39,"score":17,"supported":52,"title":341,"abstract":342,"tags":343},[],[29,60],[27,29],{"code":64,"score":21,"supported":14,"title":345,"abstract":346,"tags":347},[],[],[26],[349,350],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":352,"openalex_id":354,"authors":355,"venue":315,"cited_by_count":79,"oa_url":312,"card":358,"direction":84,"ingested_from":86},"W7223114002",[356],{"name":357,"orcid":9},"Kadiatou Barry",{"tldr":359,"method":360,"finding":361,"direction":84,"opportunity":362},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":365,"title":366,"url":367,"summary":368,"summary_zh":369,"content":9,"source_name":370,"source_url":367,"published_at":371,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":372,"score_detail":373,"sources":376,"tags":378,"primary_topic":380,"secondary_topics":384,"classification":386,"search_phrases":416,"slug":419,"view_count":79,"doi":420,"paper":421,"created_at":434},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering","2026-10-18T00:00:00Z",78,{"impact":161,"substance":374,"depth":161,"authority":102,"freshness":17,"relevant":21,"comment":375},22,"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[377],{"name":370,"url":367},[26,107,379,27,29],"水稻",{"code":381,"slug":382,"name":379,"path":383,"parent_code":36},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice",[385],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":387},{"candidates":388,"agricultural_evidence":52},[389,393,397,402,408,412],{"code":381,"score":51,"supported":52,"title":390,"abstract":391,"tags":392},[382],[379,382],[379],{"code":39,"score":17,"supported":52,"title":394,"abstract":395,"tags":396},[],[29,60],[27,29],{"code":275,"score":89,"supported":52,"title":398,"abstract":399,"tags":401},[],[278,400],"food security",[],{"code":403,"score":72,"supported":52,"title":404,"abstract":405,"tags":407},"J1",[],[406],"农业规划",[],{"code":64,"score":21,"supported":14,"title":409,"abstract":410,"tags":411},[],[],[26],{"code":124,"score":21,"supported":14,"title":413,"abstract":414,"tags":415},[],[],[107],[417,418],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394","10.3390\u002Feng7100572",{"doi":420,"openalex_id":422,"authors":423,"venue":370,"cited_by_count":79,"oa_url":367,"card":429,"direction":84,"ingested_from":86},"W7222942064",[424,426],{"name":425,"orcid":9},"Nayana Venkatesh",{"name":427,"orcid":428},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":430,"method":431,"finding":432,"direction":84,"opportunity":433},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","2026-10-20T23:30:19.292634Z",{"id":436,"title":437,"url":438,"summary":439,"summary_zh":440,"content":9,"source_name":441,"source_url":438,"published_at":442,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":443,"score_detail":444,"sources":446,"tags":448,"primary_topic":451,"secondary_topics":452,"classification":453,"search_phrases":465,"slug":468,"view_count":79,"doi":469,"paper":470,"created_at":491},5243,"Identificación de índices de vegetación relacionados con componentes de rendimiento de frijol común (Phaseolus vulgaris L.) mediante el uso de sensores multiespectrales","https:\u002F\u002Fdoi.org\u002F10.18845\u002Ftm.v39i4.8378","Bean production in Costa Rica supplies only 20 % of domestic consumption, requiring the importation of the remaining amount; this situation highlights the need to know in advance the actual productive capacity of local farmers to improve planning, agronomic management, and import regulation. Therefore, the objective of this study was to evaluate the use of multispectral data and vegetation indices to analyze their relationship with yield components in bean varieties cultivated in different production areas of Costa Rica and to identify the yield component with the greatest spectral association. The research was conducted at three sites located in Alajuela, Costa Rica. Multispectral reflectance data were collected during the R5 phenological stage using an airborne multispectral sensor. Subsequently, yield components were quantified per meter of crop row, including 100-grain weight, number of pods per meter, number of plants, number of grains, and total grain weight. The number of pods showed the highest association with spectral variables and was therefore identified as the yield component with the greatest potential for analysis through remote sensing. The SR (Simple Ratio) and GCI (Green Chlorophyll Index) indices showed correlations greater than 0.7 with this variable, being identified as the vegetation indices with the highest spectral association during the R5 stage of the crop.","哥斯达黎加的菜豆（bean）产量仅能满足国内消费量的20%，其余部分需要进口；这一状况凸显了提前了解当地农民实际生产能力的必要性，以改进生产规划、农艺管理和进口调控。因此，本研究旨在评估多光谱数据和植被指数（vegetation indices）的应用，分析其与哥斯达黎加不同产区菜豆品种产量构成因素（yield components）之间的关系，并确定与光谱关联性最强的产量构成因素。研究在哥斯达黎加阿拉胡埃拉（Alajuela）的三个地点开展。在R5物候期使用机载多光谱传感器采集多光谱反射率数据。随后，按每米作物行量化产量构成因素，包括百粒重、每米豆荚数、植株数、籽粒数和籽粒总重。豆荚数与光谱变量的关联性最高，因此被确定为最具遥感分析潜力的产量构成因素。SR（简单比值，Simple Ratio）和GCI（绿色叶绿素指数，Green Chlorophyll Index）与该变量的相关系数大于0.7，被确定为作物R5期光谱关联性最高的植被指数。","Revista Tecnología en Marcha","2026-10-16T00:00:00Z",62,{"impact":17,"substance":161,"depth":51,"authority":102,"freshness":20,"relevant":21,"comment":445},"该研究利用机载多光谱传感器识别与菜豆产量组分相关的植被指数，方法清晰、结论具体，但属于哥斯达黎加的小区域研究，公共影响有限，可作为遥感作物监测领域的技术性资讯收录。",[447],{"name":441,"url":438},[26,27,29,449,450],"植被指数","菜豆",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":454},{"candidates":455,"agricultural_evidence":52},[456,461],{"code":39,"score":102,"supported":52,"title":457,"abstract":458,"tags":460},[],[29,60,459],"multispectral",[27,449,29],{"code":64,"score":21,"supported":14,"title":462,"abstract":463,"tags":464},[],[],[26],[466,467],"哥斯达黎加 菜豆 多光谱 遥感","Phaseolus vulgaris 植被指数 产量","哥斯达黎加菜豆多光谱遥感-5243","10.18845\u002Ftm.v39i4.8378",{"doi":469,"openalex_id":471,"authors":472,"venue":441,"cited_by_count":79,"oa_url":485,"card":486,"direction":84,"ingested_from":86},"W7222720635",[473,476,479,482],{"name":474,"orcid":475},"Guillermo Vargas-Hernández","https:\u002F\u002Forcid.org\u002F0000-0003-1410-3898",{"name":477,"orcid":478},"Eddison José Araya-Morales","https:\u002F\u002Forcid.org\u002F0009-0001-0550-414X",{"name":480,"orcid":481},"Juan Carlos Hernández","https:\u002F\u002Forcid.org\u002F0000-0002-1915-3213",{"name":483,"orcid":484},"Ignacio Mora-Cordero","https:\u002F\u002Forcid.org\u002F0009-0007-0925-0099","https:\u002F\u002Frevistas.tec.ac.cr\u002Findex.php\u002Ftec_marcha\u002Farticle\u002Fdownload\u002F8378\u002F8641",{"tldr":487,"method":488,"finding":489,"direction":84,"opportunity":490},"用机载多光谱传感器评估菜豆产量构成与植被指数的关系。","机载多光谱遥感获取R5期反射率，结合地面产量构成测定与相关分析。","豆荚数与光谱变量关联最强，SR和GCI指数与其相关系数超过0.7。","可探索将SR\u002FGCI用于菜豆产量早期预测，并扩展到多品种、多生育期与无人机高光谱验证。","2026-10-18T23:30:20.259545Z"]