[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3490":3,"related-3490":119},{"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":37,"classification":38,"search_phrases":66,"slug":69,"view_count":70,"doi":71,"paper":72,"created_at":118},3490,"Balancing accuracy, completeness, and efficiency for rice 3D reconstruction through CBAM-UNet-based multi-view segmentation and camera configuration optimization","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1882534","Multi-view 3D reconstruction has been widely applied in plant phenotyping, but the complex canopy structure of rice plants poses significant challenges for reconstruction accuracy, completene7ss, and efficiency. In this study, we developed an optimized workflow for 3D reconstruction of rice using the self-developed Metatlas V1 multi-view imaging platform combined with the COLMAP + OpenMVS pipeline. A CBAM-UNet-based image segmentation model was used to extract plant regions from complex backgrounds, outperforming thresholding, U-Net, and U-Net++, and increasing the number of reconstructed points. Camera configuration optimization was performed by systematically combining equidistant and greedy strategies, resulting in an optimal six-camera setup (#2, #3, #4, #6, #7, and #8, corresponding to −30°, −15°, 0°, +30°, +45°, and +60° relative to the horizontal viewpoint), which provided complementary views of the canopy inner structure and stem base. This configuration reduced the average nearest-neighbor distance to 0.037–0.066 cm across different varieties and growth stages compared with the full 11-camera setup, retained 73–76% of the points, and decreased reconstruction duration by approximately 70–80%. The strong agreement between point-cloud-derived and manually measured plant height and canopy width ( R 2 = 0.989 and 0.946, respectively) supported the accuracy of the point-cloud-derived phenotypic measurements. Overall, integrating image segmentation with camera-configuration optimization offers an accurate and efficient solution for high-throughput 3D phenotyping of rice using Metatlas V1, balancing reconstruction accuracy, point-cloud completeness, and computational efficiency, and may provide a methodological reference for camera-configuration design in other rotational multi-view phenotyping platforms.","多视角三维重建已广泛应用于植物表型分析，但水稻复杂的冠层结构对重建精度、完整性和效率构成了重大挑战。本研究利用自主研发的Metatlas V1多视角成像平台，结合COLMAP + OpenMVS流程，开发了一套优化的水稻三维重建工作流。采用基于CBAM-UNet的图像分割模型从复杂背景中提取植株区域，其性能优于阈值分割、U-Net和U-Net++，并增加了重建点数量。通过系统组合等距策略和贪心策略进行相机配置优化，得到了最优的六相机方案（#2、#3、#4、#6、#7和#8，分别对应相对于水平视角的−30°、−15°、0°、+30°、+45°和+60°），该方案提供了冠层内部结构和茎基部的互补视角。与完整的11相机方案相比，该配置将不同品种和生育期的平均最近邻距离降至0.037–0.066 cm，保留了73–76%的点云，并将重建时间缩短了约70–80%。点云提取的株高和冠幅与人工测量结果高度一致（R²分别为0.989和0.946），验证了点云表型测量的准确性。总体而言，将图像分割与相机配置优化相结合，为利用Metatlas V1进行水稻高通量三维表型分析提供了一种准确高效的解决方案，在重建精度、点云完整性和计算效率之间取得了平衡，并可为其他旋转式多视角表型平台的相机配置设计提供方法学参考。",null,"Frontiers in Plant Science","2026-09-24T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,13,9,1,"该研究提出结合CBAM-UNet分割与相机配置优化的水稻三维重建流程，方法新颖、数据详实，对高通量作物表型分析有参考价值，但属细分领域进展，时效性高。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","水稻","作物表型","三维重建",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50,56,61],{"code":33,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},15,true,[34],[29,34],[29],{"code":51,"score":52,"supported":14,"title":53,"abstract":54,"tags":55},"I2",2,[],[],[31,28],{"code":57,"score":22,"supported":14,"title":58,"abstract":59,"tags":60},"I1",[],[],[27],{"code":62,"score":22,"supported":14,"title":63,"abstract":64,"tags":65},"I3",[],[],[30],[67,68],"Metatlas V1 水稻 三维重建","CBAM-UNet 水稻 图像分割","MetatlasV1水稻三维重建-3490",4,"10.3389\u002Ffpls.2026.1882534",{"doi":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":110,"oa_url":6,"card":111,"direction":115,"ingested_from":117},"W7214231705",[75,77,80,82,85,87,89,92,95,98,100,102,105,108],{"name":76,"orcid":9},"Haoyang Zhou",{"name":78,"orcid":79},"Rongjie Chen","https:\u002F\u002Forcid.org\u002F0009-0004-8078-2301",{"name":81,"orcid":9},"Hao Wang",{"name":83,"orcid":84},"Minglu Li","https:\u002F\u002Forcid.org\u002F0000-0003-1751-9418",{"name":86,"orcid":9},"Yongkang Teng",{"name":88,"orcid":9},"Shenghao Ye",{"name":90,"orcid":91},"Menglei Wei","https:\u002F\u002Forcid.org\u002F0009-0004-3070-8023",{"name":93,"orcid":94},"Kun Yu","https:\u002F\u002Forcid.org\u002F0000-0002-0190-8702",{"name":96,"orcid":97},"Pingping Fang","https:\u002F\u002Forcid.org\u002F0000-0002-9911-2963",{"name":99,"orcid":9},"Jing Cao",{"name":101,"orcid":9},"Fanglin Zhu",{"name":103,"orcid":104},"Wenyu Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-3322-9736",{"name":106,"orcid":107},"Ting Sun","https:\u002F\u002Forcid.org\u002F0000-0002-9387-4852",{"name":109,"orcid":9},"Min Jiang",0,{"tldr":112,"method":113,"finding":114,"direction":115,"opportunity":116},"提出CBAM-UNet分割与相机配置优化结合的水稻多视角三维重建流程，兼顾精度、完整性与效率。","Metatlas V1多视角平台、COLMAP+OpenMVS、CBAM-UNe","最优6相机配置将最近邻距离降至0.037–0.066 cm，保留73–76%点云，重建耗时减少约70","农业遥感与作物表型","可将相机配置优化策略迁移到其他旋转多视角平台，并探索自适应配置与分割模型联合优化。","openalex","2026-09-25T23:30:25.303016Z",{"total":120,"page":22,"page_size":120,"items":121},6,[122,197,268,345,401,477],{"id":123,"title":124,"url":125,"summary":126,"summary_zh":127,"content":9,"source_name":128,"source_url":125,"published_at":129,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":130,"score_detail":131,"sources":133,"tags":135,"primary_topic":137,"secondary_topics":138,"classification":148,"search_phrases":175,"slug":178,"view_count":52,"doi":179,"paper":180,"created_at":196},3164,"Spatiotemporal Deep Learning for Rice Plant Height Estimation from Multi-Temporal UAV RGB Imagery","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16182034","Accurate plant height estimation is important for monitoring crop growth and supporting precision agricultural management. Manual measurements are labor-intensive, while LiDAR-based methods are expensive and require complex processing. UAV photogrammetry provides a lower-cost alternative but remains challenging in flooded rice paddies because of canopy deformation and difficulties in terrain extraction. This study proposes Rice-STNet, a spatiotemporal deep learning framework for end-to-end rice plant height estimation using multi-temporal UAV RGB imagery. Rice-STNet integrates a convolutional neural network for spatial feature extraction, Time2Vec for temporal encoding, and a gated recurrent unit network for modeling temporal dependencies across observation dates. The framework was evaluated using field data collected from rice paddies over two growing seasons. Rice-STNet achieved an R2 of 0.97, a root mean squared error of 1.97 cm, and a mean absolute error of 1.14 cm. It outperformed random forest, support vector regression, a CNN-only baseline, and a UAV photogrammetry-based point-cloud approach. In addition, the framework generated high-resolution plant height maps for field-scale analysis of spatial growth variability. These results underscore the importance of jointly modeling spatial and temporal characteristics for continuously evolving crop traits. The proposed framework offers an accurate, scalable, and non-destructive solution for large-scale crop phenotyping and precision agriculture.","准确的株高估算对于监测作物生长和支持精准农业管理具有重要意义。人工测量劳动强度大，而基于激光雷达（LiDAR）的方法成本高昂且需要复杂的处理。无人机摄影测量提供了一种成本较低的替代方案，但在淹水稻田中仍面临挑战，原因在于冠层变形和地形提取困难。本研究提出了Rice-STNet，一种时空深度学习框架，用于利用多时相无人机RGB影像进行端到端水稻株高估算。Rice-STNet集成了用于空间特征提取的卷积神经网络、用于时间编码的Time2Vec，以及用于建模观测日期之间时间依赖关系的门控循环单元网络。该框架利用两个生长季从稻田采集的田间数据进行了评估。Rice-STNet取得了R²为0.97、均方根误差为1.97 cm、平均绝对误差为1.14 cm的结果。其性能优于随机森林、支持向量回归、仅使用CNN的基线方法以及基于无人机摄影测量的点云方法。此外，该框架生成了高分辨率株高图，用于田块尺度空间生长变异性分析。这些结果凸显了联合建模空间与时间特征对于持续变化的作物性状的重要性。所提出的框架为大规模作物表型分析和精准农业提供了一种准确、可扩展且非破坏性的解决方案。","Agriculture","2026-09-21T00:00:00Z",80,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":132},"提出时空深度学习框架Rice-STNet，用多时相无人机RGB影像实现水稻株高高精度估算，方法新颖、数据跨两个生长季，对作物表型与精准农业有实用价值。",[134],{"name":128,"url":125},[27,28,29,136,30],"遥感",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[139,144],{"code":51,"slug":140,"name":141,"path":142,"parent_code":143},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":62,"slug":145,"name":146,"path":147,"parent_code":143},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":14,"version":40,"method":41,"evidence":149},{"candidates":150,"agricultural_evidence":46},[151,156,162,168],{"code":33,"score":19,"supported":46,"title":152,"abstract":153,"tags":155},[34],[29,154,34],"稻田",[29],{"code":51,"score":45,"supported":46,"title":157,"abstract":159,"tags":161},[158],"deep learning",[160,158],"深度学习",[28],{"code":62,"score":163,"supported":46,"title":164,"abstract":165,"tags":167},8,[],[30,166],"crop phenotyping",[30,136],{"code":57,"score":169,"supported":46,"title":170,"abstract":171,"tags":174},7,[],[172,173],"精准农业","precision agriculture",[27],[176,177],"无人机 RGB 水稻株高","Rice-STNet 水稻表型","无人机RGB水稻株高-3164","10.3390\u002Fagriculture16182034",{"doi":179,"openalex_id":181,"authors":182,"venue":128,"cited_by_count":110,"oa_url":125,"card":191,"direction":115,"ingested_from":117},"W7213887432",[183,186,188],{"name":184,"orcid":185},"Weiguo Wang","https:\u002F\u002Forcid.org\u002F0009-0003-4028-9363",{"name":187,"orcid":9},"Noboru Noguchi",{"name":189,"orcid":190},"Liangliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5055-3987",{"tldr":192,"method":193,"finding":194,"direction":115,"opportunity":195},"提出Rice-STNet时空深度学习框架，用多时相无人机RGB影像估算水稻株高。","CNN提取空间特征，Time2Vec编码时间，GRU建模时序依赖，两季稻田数据验","R²达0.97、RMSE 1.97cm，优于随机森林、SVR、纯CNN及点云方法。","可迁移至其他作物与多源遥感融合，探索轻量化模型及实时田间部署。","2026-09-22T23:30:18.545958Z",{"id":198,"title":199,"url":200,"summary":201,"summary_zh":202,"content":9,"source_name":203,"source_url":200,"published_at":204,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":205,"sources":208,"tags":210,"primary_topic":212,"secondary_topics":213,"classification":215,"search_phrases":249,"slug":252,"view_count":110,"doi":253,"paper":254,"created_at":267},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",{"impact":19,"substance":18,"depth":19,"authority":206,"freshness":163,"relevant":22,"comment":207},12,"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。",[209],{"name":203,"url":200},[27,28,29,211,136],"产量预测",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[214],{"code":62,"slug":145,"name":146,"path":147,"parent_code":143},{"status":39,"locked":14,"version":40,"method":41,"evidence":216},{"candidates":217,"agricultural_evidence":46},[218,222,227,234,241,245],{"code":33,"score":45,"supported":46,"title":219,"abstract":220,"tags":221},[34],[29,34],[29],{"code":62,"score":163,"supported":46,"title":223,"abstract":224,"tags":226},[],[136,225],"remote sensing",[211,136],{"code":228,"score":120,"supported":46,"title":229,"abstract":230,"tags":233},"A8",[],[231,232],"粮食安全","food security",[],{"code":235,"score":236,"supported":46,"title":237,"abstract":238,"tags":240},"J1",3,[],[239],"农业规划",[],{"code":57,"score":22,"supported":14,"title":242,"abstract":243,"tags":244},[],[],[27],{"code":51,"score":22,"supported":14,"title":246,"abstract":247,"tags":248},[],[],[28],[250,251],"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":253,"openalex_id":255,"authors":256,"venue":203,"cited_by_count":110,"oa_url":200,"card":262,"direction":115,"ingested_from":117},"W7222942064",[257,259],{"name":258,"orcid":9},"Nayana Venkatesh",{"name":260,"orcid":261},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":263,"method":264,"finding":265,"direction":115,"opportunity":266},"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":269,"title":270,"url":271,"summary":272,"summary_zh":273,"content":9,"source_name":274,"source_url":271,"published_at":275,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":130,"score_detail":276,"sources":278,"tags":280,"primary_topic":283,"secondary_topics":284,"classification":285,"search_phrases":325,"slug":328,"view_count":110,"doi":329,"paper":330,"created_at":344},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",{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":277},"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。",[279],{"name":274,"url":271},[27,28,29,281,282],"数字孪生","病虫害预警",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":286},{"candidates":287,"agricultural_evidence":46},[288,294,299,306,313,320],{"code":57,"score":289,"supported":46,"title":290,"abstract":292,"tags":293},19,[291],"digital twin",[172,281,173,291],[281,27],{"code":33,"score":19,"supported":46,"title":295,"abstract":297,"tags":298},[296],"paddy",[29,154,34,296],[29],{"code":300,"score":120,"supported":46,"title":301,"abstract":302,"tags":305},"F4",[],[303,304],"农药","pesticide",[],{"code":307,"score":120,"supported":46,"title":308,"abstract":309,"tags":312},"H3",[],[310,311],"灌溉","irrigation",[],{"code":314,"score":120,"supported":46,"title":315,"abstract":316,"tags":319},"I5",[],[317,318],"物联网","IoT",[],{"code":51,"score":70,"supported":46,"title":321,"abstract":322,"tags":324},[],[323],"decision support",[28],[326,327],"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":329,"openalex_id":331,"authors":332,"venue":274,"cited_by_count":110,"oa_url":9,"card":337,"direction":343,"ingested_from":117},"W7222969403",[333,335],{"name":334,"orcid":9},"Jyoti Rani",{"name":336,"orcid":9},"Dr.  Narendra S.  Jadhav",{"tldr":338,"method":339,"finding":340,"direction":341,"opportunity":342},"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。","智慧农业 \u002F 农业物联网","2026-10-20T23:30:12.636299Z",{"id":346,"title":347,"url":348,"summary":349,"summary_zh":350,"content":9,"source_name":351,"source_url":348,"published_at":352,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":130,"score_detail":353,"sources":355,"tags":357,"primary_topic":360,"secondary_topics":361,"classification":363,"search_phrases":380,"slug":383,"view_count":110,"doi":384,"paper":385,"created_at":400},4802,"A Privacy-Preserving Federated Learning Framework for Paddy Leaf Disease Detection Using an Enhanced Pretrained Novel EfficientNet-Lite4 Model","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1837705","The correct and prompt detection of leaf diseases in paddy crops remains essential for protecting agricultural output and yield stability and reducing major losses that occur during severe disease outbreaks. The widespread use of conventional centralized machine learning techniques becomes impractical for real-world agricultural applications, as their effectiveness diminishes under three main conditions: data privacy limitations, delayed transmission of information, along with reduced performance accuracy from non-independent and identically distributed (non-IID) data properties across different agro-climatic regions. This research develops a decentralized diagnostic system which combines Federated Learning with an Enhanced Pretrained Novel EfficientNet-Lite4 model designed for paddy leaf disease classification. The Paddy Doctor dataset served as training material for the system which contained 16,225 RGB images that classified 12 disease types alongside healthy categories. The proposed model reached 98.7% classification accuracy and 98.5% precision and 98.6% F1-score while maintaining a final loss of 0.21 after 100 communication rounds. The system underwent field testing which demonstrated its ability to generate accurate diagnostic results in real time in various working conditions. The study demonstrates how privacy preserving AI-based paddy disease management works in practice and recommends local agricultural institutions to build decentralized learning systems. The suggested system will give farmers direct access to field level decision support and facilitate the creation of sustainable digital pest management systems required to ensure food supply.","正确且及时地检测水稻作物叶片病害，对于保护农业产出和产量稳定、减少病害严重暴发期间发生的重大损失仍然至关重要。传统集中式机器学习技术的广泛使用在现实农业应用中变得不切实际，因为在三种主要条件下其有效性会下降：数据隐私限制、信息传输延迟，以及不同农业气候区域间非独立同分布（non-IID）数据特性导致的性能准确率降低。本研究开发了一种去中心化诊断系统，将联邦学习与一种增强的预训练新型高效网络模型EfficientNet-Lite4相结合，用于水稻叶片病害分类。Paddy Doctor数据集作为该系统的训练材料，包含16,225张RGB图像，涵盖12种病害类型以及健康类别。所提出的模型在100轮通信后达到了98.7%的分类准确率、98.5%的精确率和98.6%的F1分数，同时最终损失保持在0.21。该系统经过实地测试，证明其能够在各种工作条件下实时生成准确的诊断结果。该研究展示了基于隐私保护人工智能的水稻病害管理如何在实践中运作，并建议地方农业机构构建去中心化学习系统。所建议的系统将为农民提供直接的田间决策支持，并促进建立确保粮食供应所需的可持续数字病虫害管理系统。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":354},"将联邦学习与轻量级EfficientNet-Lite4结合用于水稻叶病识别，兼顾隐私保护与98.7%高精度，对智慧农业落地有参考价值。",[356],{"name":351,"url":348},[27,28,29,358,359],"病虫害识别","联邦学习",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[362],{"code":51,"slug":140,"name":141,"path":142,"parent_code":143},{"status":39,"locked":14,"version":40,"method":41,"evidence":364},{"candidates":365,"agricultural_evidence":46},[366,370,376],{"code":33,"score":45,"supported":46,"title":367,"abstract":368,"tags":369},[296],[29,296],[29],{"code":51,"score":206,"supported":46,"title":371,"abstract":372,"tags":375},[],[373,374,323],"机器学习","machine learning",[28,358,359],{"code":57,"score":22,"supported":14,"title":377,"abstract":378,"tags":379},[],[],[27],[381,382],"Paddy Doctor 数据集 水稻病害","EfficientNet-Lite4 水稻叶片病害","PaddyDoctor数据集水稻病害-4802","10.15832\u002Fankutbd.1837705",{"doi":384,"openalex_id":386,"authors":387,"venue":351,"cited_by_count":110,"oa_url":394,"card":395,"direction":343,"ingested_from":117},"W7221522859",[388,391],{"name":389,"orcid":390},"B Johnson","https:\u002F\u002Forcid.org\u002F0009-0009-8281-4051",{"name":392,"orcid":393},"Chandrakumar Thangavel","https:\u002F\u002Forcid.org\u002F0000-0002-1186-5988","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5486058",{"tldr":396,"method":397,"finding":398,"direction":341,"opportunity":399},"提出联邦学习结合改进EfficientNet-Lite4的水稻叶片病害检测框架，兼顾隐私与精度。","联邦学习+预训练EfficientNet-Lite4，用Paddy Doctor","12类病害分类准确率达98.7%，100轮通信后损失0.21，支持实时诊断。","可探索非IID数据下联邦聚合优化与轻量化模型在边缘设备的部署研究。","2026-10-13T23:30:08.809781Z",{"id":402,"title":403,"url":404,"summary":405,"summary_zh":9,"content":9,"source_name":406,"source_url":404,"published_at":407,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":408,"score_detail":409,"sources":412,"tags":414,"primary_topic":416,"secondary_topics":417,"classification":420,"search_phrases":439,"slug":442,"view_count":22,"doi":443,"paper":444,"created_at":476},4500,"Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11163942\u002Fv1","Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice。Research Square","Research Square","2026-10-08T00:00:00Z",74,{"impact":19,"substance":410,"depth":19,"authority":13,"freshness":163,"relevant":22,"comment":411},20,"将无人机遥感与深度学习结合用于水稻白叶枯病早期检测，方法有创新、结论具应用价值，但属预印本且为细分技术进展，适合进入主题聚合而非头条精选。",[413],{"name":406,"url":404},[27,28,29,136,415],"病害检测",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[418,419],{"code":51,"slug":140,"name":141,"path":142,"parent_code":143},{"code":62,"slug":145,"name":146,"path":147,"parent_code":143},{"status":39,"locked":14,"version":40,"method":41,"evidence":421},{"candidates":422,"agricultural_evidence":46},[423,427,431,435],{"code":33,"score":206,"supported":46,"title":424,"abstract":425,"tags":426},[34],[34],[29],{"code":51,"score":206,"supported":46,"title":428,"abstract":429,"tags":430},[158],[158],[28],{"code":62,"score":206,"supported":46,"title":432,"abstract":433,"tags":434},[225],[225],[136],{"code":57,"score":22,"supported":14,"title":436,"abstract":437,"tags":438},[],[],[27],[440,441],"无人机遥感 水稻 白叶枯病","深度学习 水稻病害 早期检测","无人机遥感水稻白叶枯病-4500","10.21203\u002Frs.3.rs-11163942\u002Fv1",{"doi":443,"openalex_id":445,"authors":446,"venue":406,"cited_by_count":110,"oa_url":404,"card":9,"direction":115,"ingested_from":117},"W7221055917",[447,450,453,455,457,460,462,464,467,469,471,473],{"name":448,"orcid":449},"Carlito B. Balingbing","https:\u002F\u002Forcid.org\u002F0000-0002-3672-9253",{"name":451,"orcid":452},"Dmytro Chebotarov","https:\u002F\u002Forcid.org\u002F0000-0003-1351-9453",{"name":454,"orcid":9},"Ian  Carlo Perilla",{"name":456,"orcid":9},"Stephen Klassen",{"name":458,"orcid":459},"Nancy Castilla","https:\u002F\u002Forcid.org\u002F0000-0002-9143-4933",{"name":461,"orcid":9},"Raphael  Gabriel Carvajal",{"name":463,"orcid":9},"Kendrix Ortega",{"name":465,"orcid":466},"Joseph M. Sandro","https:\u002F\u002Forcid.org\u002F0000-0002-8311-2299",{"name":468,"orcid":9},"Nurmi Pangesti",{"name":470,"orcid":9},"Caesar Arloo R. Centeno",{"name":472,"orcid":9},"Arnel Rala",{"name":474,"orcid":475},"Li-Yu  Daisy Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6997-8101","2026-10-09T23:30:18.390549Z",{"id":478,"title":479,"url":480,"summary":481,"summary_zh":482,"content":9,"source_name":483,"source_url":480,"published_at":484,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":485,"score_detail":486,"sources":489,"tags":491,"primary_topic":494,"secondary_topics":495,"classification":497,"search_phrases":516,"slug":519,"view_count":52,"doi":520,"paper":521,"created_at":534},4431,"A multimodal deep learning for rice crop health monitoring using vegetation index feature selection","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74895-5","Crop health monitoring is essential for sustaining agricultural productivity and ensuring stable production, particularly in major crop-producing regions that face challenges due to climate variability, resource constraints, and spatial heterogeneity. Traditional methods struggle to effectively integrate spatial heterogeneity and environmental variability from multi-source remote sensing (RS) data. To address this problem, the proposed work explores the role of satellite imagery in monitoring rice crop health with the help of Vegetation Indices (VIs) under the impact of changing weather conditions. Multi-temporal Sentinel-1 and Sentinel-2 satellite data were acquired from a rice cultivation field in Gorakhpur, Uttar Pradesh, India, during three Kharif growing seasons (2023-2025). In this work, we propose a supervised MI-based feature selection framework employing Otsu’s adaptive thresholding for selection of VIs that strongly determine the health of rice crop. To ensure data integrity, cloud-contaminated observations were systematically excluded through a two-stage cleaning process. Further, we propose a multimodal deep learning (DL) framework that fuses a Swin Transformer (Swin-T) backbone for spatial feature extraction from vegetation index imagery with a Multi-Layer Perceptron (MLP) for structured numerical modeling of meteorological variables. The proposed framework achieved 94.05% accuracy on an independent held-out test set and a mean accuracy of \\(92.12 \\pm 5.28\\) % under 10-fold cross-validation, consistently outperforming competing hybrid deep learning baselines. These results highlight that integrating transformer-based spatial representations with environmental information provides an efficient approach for rice crop health classification. The proposed framework can be extended to multi-region crop monitoring by incorporating additional vegetation index modalities and diverse climatic conditions.","作物健康监测对于维持农业生产力、保障稳定生产至关重要，尤其是在面临气候变异性、资源约束和空间异质性挑战的主要作物产区。传统方法难以有效整合多源遥感（RS）数据中的空间异质性和环境变异性。为解决这一问题，本研究探索了卫星影像在天气条件变化影响下借助植被指数（VIs）监测水稻作物健康的作用。研究在印度北方邦戈勒克布尔的稻田中，于三个水稻生长季（2023—2025年）获取了多时相Sentinel-1和Sentinel-2卫星数据。在本研究中，我们提出了一种基于互信息（MI）的监督式特征选择框架，采用Otsu自适应阈值法筛选对水稻作物健康具有强判别能力的植被指数。为确保数据完整性，通过两阶段清洗流程系统性地剔除了受云污染的观测数据。此外，我们提出了一种多模态深度学习（DL）框架，该框架融合了Swin Transformer（Swin-T）主干网络用于从植被指数影像中提取空间特征，以及多层感知机（MLP）用于气象变量的结构化数值建模。所提框架在独立留出测试集上达到了94.05%的准确率，在10折交叉验证下平均准确率为\\(92.12 \\pm 5.28\\)%，持续优于竞争的混合深度学习基线模型。这些结果表明，将基于Transformer的空间表征与环境信息相结合，为水稻作物健康分类提供了一种高效方法。所提框架可通过纳入更多植被指数模态和多样化气候条件，扩展至多区域作物监测。","Scientific Reports","2026-10-07T00:00:00Z",81,{"impact":19,"substance":18,"depth":19,"authority":487,"freshness":21,"relevant":22,"comment":488},14,"方法新颖、数据跨三个生长季且精度可靠，属农业遥感与AI交叉的细分领域进展，值得进入每日精选。",[490],{"name":483,"url":480},[27,28,29,492,493],"遥感监测","作物长势",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[496],{"code":51,"slug":140,"name":141,"path":142,"parent_code":143},{"status":39,"locked":14,"version":40,"method":41,"evidence":498},{"candidates":499,"agricultural_evidence":46},[500,504,508,512],{"code":33,"score":19,"supported":46,"title":501,"abstract":502,"tags":503},[34],[29,154,34],[29],{"code":51,"score":45,"supported":46,"title":505,"abstract":506,"tags":507},[158],[160,158],[28],{"code":62,"score":169,"supported":46,"title":509,"abstract":510,"tags":511},[],[136,225],[492],{"code":57,"score":22,"supported":14,"title":513,"abstract":514,"tags":515},[],[],[27],[517,518],"Sentinel-2 水稻 遥感监测","Swin Transformer 作物健康","Sentinel-2水稻遥感监测-4431","10.1038\u002Fs41598-026-74895-5",{"doi":520,"openalex_id":522,"authors":523,"venue":483,"cited_by_count":110,"oa_url":480,"card":529,"direction":115,"ingested_from":117},"W7220838898",[524,527],{"name":525,"orcid":526},"Shashank Gupta","https:\u002F\u002Forcid.org\u002F0000-0003-1291-7951",{"name":528,"orcid":9},"Shefali Arora Chouhan",{"tldr":530,"method":531,"finding":532,"direction":115,"opportunity":533},"融合Sentinel-1\u002F2植被指数与气象数据，用多模态深度学习监测水稻健康。","Sentinel-1\u002F2多时相数据、MI特征选择、Otsu阈值、Swin-T+M","独立测试准确率94.05%，10折交叉验证92.12%，优于混合基线。","可扩展至多区域多作物，探索更多植被指数模态与极端气候下的泛化能力。","2026-10-08T23:30:29.254071Z"]