[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2120":3,"related-2120":85},{"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":61,"slug":64,"view_count":52,"doi":65,"paper":66,"created_at":84},2120,"Wild rice kernel inspection using a prompt-guided vision language model with evidence-grounded reasoning","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112392","Wild rice kernel inspection using a prompt-guided vision language model with evidence-grounded reasoning。Computers and Electronics in Agriculture","使用提示引导的视觉语言模型结合基于证据的推理进行野生稻谷粒检验。《计算机与电子农业》",null,"Computers and Electronics in Agriculture","2026-09-10T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,20,17,14,8,1,"提出提示引导的视觉语言模型用于野生稻籽粒检测，方法新颖且发表于农业信息领域核心期刊，对智能育种与种子质检有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","计算机视觉","水稻","种子检测",{"code":33,"slug":34,"name":30,"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],{"code":33,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},12,true,[34],[34],[30],{"code":51,"score":52,"supported":14,"title":53,"abstract":54,"tags":55},"I2",2,[],[],[28,29],{"code":57,"score":22,"supported":14,"title":58,"abstract":59,"tags":60},"I1",[],[],[27],[62,63],"农业人工智能 计算机视觉 智慧农业 种子检测","农业人工智能 计算机视觉","农业人工智能计算机视觉智慧农业种子检测-2120","10.1016\u002Fj.compag.2026.112392",{"doi":65,"openalex_id":67,"authors":68,"venue":10,"cited_by_count":76,"oa_url":6,"card":77,"direction":81,"ingested_from":83},"W7212185866",[69,71,74],{"name":70,"orcid":9},"Yinka Sikiru",{"name":72,"orcid":73},"Angshuman Thakuria","https:\u002F\u002Forcid.org\u002F0000-0003-4196-3770",{"name":75,"orcid":9},"Chyngyz Erkinbaev",0,{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"用提示引导的视觉语言模型对野生稻米粒进行检测，并给出基于证据的推理。","提示引导的视觉语言模型，结合证据锚定推理，用于野生稻米粒图像检测。","该方法能实现野生稻米粒检测，并输出可解释的证据推理过程。","农业人工智能与决策模型","可探索视觉语言模型在作物种子\u002F米粒质检中的可解释性与少样本迁移能力。","openalex","2026-09-11T23:30:01.974562Z",{"total":86,"page":22,"page_size":86,"items":87},6,[88,169,252,311,387,446],{"id":89,"title":90,"url":91,"summary":92,"summary_zh":93,"content":9,"source_name":94,"source_url":91,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":96,"score_detail":97,"sources":101,"tags":103,"primary_topic":106,"secondary_topics":107,"classification":114,"search_phrases":149,"slug":152,"view_count":76,"doi":153,"paper":154,"created_at":168},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":98,"substance":99,"depth":98,"authority":45,"freshness":21,"relevant":22,"comment":100},18,22,"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。",[102],{"name":94,"url":91},[27,28,30,104,105],"产量预测","遥感",{"code":33,"slug":34,"name":30,"path":35,"parent_code":36},[108],{"code":109,"slug":110,"name":111,"path":112,"parent_code":113},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":115},{"candidates":116,"agricultural_evidence":46},[117,122,127,134,141,145],{"code":33,"score":118,"supported":46,"title":119,"abstract":120,"tags":121},15,[34],[30,34],[30],{"code":109,"score":21,"supported":46,"title":123,"abstract":124,"tags":126},[],[105,125],"remote sensing",[104,105],{"code":128,"score":86,"supported":46,"title":129,"abstract":130,"tags":133},"A8",[],[131,132],"粮食安全","food security",[],{"code":135,"score":136,"supported":46,"title":137,"abstract":138,"tags":140},"J1",3,[],[139],"农业规划",[],{"code":57,"score":22,"supported":14,"title":142,"abstract":143,"tags":144},[],[],[27],{"code":51,"score":22,"supported":14,"title":146,"abstract":147,"tags":148},[],[],[28],[150,151],"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":153,"openalex_id":155,"authors":156,"venue":94,"cited_by_count":76,"oa_url":91,"card":162,"direction":166,"ingested_from":83},"W7222942064",[157,159],{"name":158,"orcid":9},"Nayana Venkatesh",{"name":160,"orcid":161},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":163,"method":164,"finding":165,"direction":166,"opportunity":167},"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。","农业遥感与作物表型","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":170,"title":171,"url":172,"summary":173,"summary_zh":174,"content":9,"source_name":175,"source_url":172,"published_at":176,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":177,"score_detail":178,"sources":182,"tags":184,"primary_topic":187,"secondary_topics":188,"classification":189,"search_phrases":233,"slug":236,"view_count":76,"doi":237,"paper":238,"created_at":251},5388,"A digital twin framework for proactive disease management in paddy cultivation using spatio-temporal graph neural networks","https:\u002F\u002Fdoi.org\u002F10.1080\u002F20421338.2026.2707951","Rice production is highly affected by disease outbreaks, particularly in Asian agricultural systems, resulting in major yield losses and excessive pesticide usage. This study proposes a digital twin framework for paddy cultivation that integrates predictive disease modelling with real-time crop phenology simulation to support proactive disease management. The framework utilizes historical phenological records, IoT-based microclimatic sensor data, irrigation information, and field management records collected from 150 hectares of paddy fields in Maharashtra, India. A Spatio-Temporal Graph Neural Network (ST-GNN), integrating Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks, is employed to model spatial disease propagation and temporal crop dynamics for predicting blast, brown planthopper, and bacterial leaf blight outbreaks up to 14 days in advance. Experimental results demonstrate 87.3% prediction accuracy, 23.7% reduction in pesticide usage, and 31.2% reduction in crop losses, while outperforming traditional Random Forest and SVM models in F1-score performance. The proposed framework enables real-time decision support for sustainable precision agriculture and optimized resource management.","水稻生产极易受到病害暴发的影响，在亚洲农业体系中尤为突出，造成重大产量损失和农药过度使用。本研究提出一个水稻栽培数字孪生（digital twin）框架，将预测性病害建模与实时作物物候模拟相结合，以支持主动式病害管理。该框架利用从印度马哈拉施特拉邦150公顷稻田采集的历史物候记录、基于物联网的微气候传感器数据、灌溉信息及田间管理记录。采用融合图卷积网络（Graph Convolutional Networks, GCN）与长短期记忆网络（Long Short-Term Memory, LSTM）的时空图神经网络（Spatio-Temporal Graph Neural Network, ST-GNN），对病害空间传播与作物时序动态进行建模，以提前14天预测稻瘟病、褐飞虱和白叶枯病的暴发。实验结果表明，预测准确率达87.3%，农药使用量减少23.7%，作物损失减少31.2%，且在F1分数上优于传统随机森林（Random Forest）和支持向量机（SVM）模型。所提框架可为可持续精准农业和优化资源管理提供实时决策支持。","African Journal of Science Technology Innovation and Development","2026-10-19T00:00:00Z",80,{"impact":98,"substance":99,"depth":98,"authority":179,"freshness":180,"relevant":22,"comment":181},13,9,"方法新颖、数据规模明确且量化效果显著，属智慧农业细分领域实质进展，值得进入每日精选。",[183],{"name":175,"url":172},[27,28,30,185,186],"数字孪生","病虫害预警",{"code":33,"slug":34,"name":30,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":190},{"candidates":191,"agricultural_evidence":46},[192,200,206,213,220,227],{"code":57,"score":193,"supported":46,"title":194,"abstract":196,"tags":199},19,[195],"digital twin",[197,185,198,195],"精准农业","precision agriculture",[185,27],{"code":33,"score":98,"supported":46,"title":201,"abstract":203,"tags":205},[202],"paddy",[30,204,34,202],"稻田",[30],{"code":207,"score":86,"supported":46,"title":208,"abstract":209,"tags":212},"F4",[],[210,211],"农药","pesticide",[],{"code":214,"score":86,"supported":46,"title":215,"abstract":216,"tags":219},"H3",[],[217,218],"灌溉","irrigation",[],{"code":221,"score":86,"supported":46,"title":222,"abstract":223,"tags":226},"I5",[],[224,225],"物联网","IoT",[],{"code":51,"score":228,"supported":46,"title":229,"abstract":230,"tags":232},4,[],[231],"decision support",[28],[234,235],"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":237,"openalex_id":239,"authors":240,"venue":175,"cited_by_count":76,"oa_url":9,"card":245,"direction":250,"ingested_from":83},"W7222969403",[241,243],{"name":242,"orcid":9},"Jyoti Rani",{"name":244,"orcid":9},"Dr.  Narendra S.  Jadhav",{"tldr":246,"method":247,"finding":248,"direction":81,"opportunity":249},"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。","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":253,"title":254,"url":255,"summary":256,"summary_zh":257,"content":9,"source_name":258,"source_url":255,"published_at":259,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":177,"score_detail":260,"sources":262,"tags":264,"primary_topic":267,"secondary_topics":268,"classification":273,"search_phrases":290,"slug":293,"view_count":76,"doi":294,"paper":295,"created_at":310},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":98,"substance":99,"depth":98,"authority":179,"freshness":180,"relevant":22,"comment":261},"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。",[263],{"name":258,"url":255},[27,28,30,265,266],"病虫害识别","联邦学习",{"code":33,"slug":34,"name":30,"path":35,"parent_code":36},[269],{"code":51,"slug":270,"name":271,"path":272,"parent_code":113},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":274},{"candidates":275,"agricultural_evidence":46},[276,280,286],{"code":33,"score":118,"supported":46,"title":277,"abstract":278,"tags":279},[202],[30,202],[30],{"code":51,"score":45,"supported":46,"title":281,"abstract":282,"tags":285},[],[283,284,231],"机器学习","machine learning",[28,265,266],{"code":57,"score":22,"supported":14,"title":287,"abstract":288,"tags":289},[],[],[27],[291,292],"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":294,"openalex_id":296,"authors":297,"venue":258,"cited_by_count":76,"oa_url":304,"card":305,"direction":250,"ingested_from":83},"W7221522859",[298,301],{"name":299,"orcid":300},"B Johnson","https:\u002F\u002Forcid.org\u002F0009-0009-8281-4051",{"name":302,"orcid":303},"Chandrakumar Thangavel","https:\u002F\u002Forcid.org\u002F0000-0002-1186-5988","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5486058",{"tldr":306,"method":307,"finding":308,"direction":81,"opportunity":309},"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":312,"title":313,"url":314,"summary":315,"summary_zh":9,"content":9,"source_name":316,"source_url":314,"published_at":317,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":318,"score_detail":319,"sources":321,"tags":323,"primary_topic":325,"secondary_topics":326,"classification":329,"search_phrases":349,"slug":352,"view_count":22,"doi":353,"paper":354,"created_at":386},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":98,"substance":18,"depth":98,"authority":13,"freshness":21,"relevant":22,"comment":320},"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。",[322],{"name":316,"url":314},[27,28,30,105,324],"病害检测",{"code":33,"slug":34,"name":30,"path":35,"parent_code":36},[327,328],{"code":51,"slug":270,"name":271,"path":272,"parent_code":113},{"code":109,"slug":110,"name":111,"path":112,"parent_code":113},{"status":39,"locked":14,"version":40,"method":41,"evidence":330},{"candidates":331,"agricultural_evidence":46},[332,336,341,345],{"code":33,"score":45,"supported":46,"title":333,"abstract":334,"tags":335},[34],[34],[30],{"code":51,"score":45,"supported":46,"title":337,"abstract":339,"tags":340},[338],"deep learning",[338],[28],{"code":109,"score":45,"supported":46,"title":342,"abstract":343,"tags":344},[125],[125],[105],{"code":57,"score":22,"supported":14,"title":346,"abstract":347,"tags":348},[],[],[27],[350,351],"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":353,"openalex_id":355,"authors":356,"venue":316,"cited_by_count":76,"oa_url":314,"card":9,"direction":166,"ingested_from":83},"W7221055917",[357,360,363,365,367,370,372,374,377,379,381,383],{"name":358,"orcid":359},"Carlito B. Balingbing","https:\u002F\u002Forcid.org\u002F0000-0002-3672-9253",{"name":361,"orcid":362},"Dmytro Chebotarov","https:\u002F\u002Forcid.org\u002F0000-0003-1351-9453",{"name":364,"orcid":9},"Ian  Carlo Perilla",{"name":366,"orcid":9},"Stephen Klassen",{"name":368,"orcid":369},"Nancy Castilla","https:\u002F\u002Forcid.org\u002F0000-0002-9143-4933",{"name":371,"orcid":9},"Raphael  Gabriel Carvajal",{"name":373,"orcid":9},"Kendrix Ortega",{"name":375,"orcid":376},"Joseph M. Sandro","https:\u002F\u002Forcid.org\u002F0000-0002-8311-2299",{"name":378,"orcid":9},"Nurmi Pangesti",{"name":380,"orcid":9},"Caesar Arloo R. Centeno",{"name":382,"orcid":9},"Arnel Rala",{"name":384,"orcid":385},"Li-Yu  Daisy Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6997-8101","2026-10-09T23:30:18.390549Z",{"id":388,"title":389,"url":390,"summary":391,"summary_zh":392,"content":9,"source_name":393,"source_url":390,"published_at":394,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":395,"score_detail":396,"sources":398,"tags":400,"primary_topic":403,"secondary_topics":404,"classification":406,"search_phrases":427,"slug":430,"view_count":52,"doi":431,"paper":432,"created_at":445},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":98,"substance":99,"depth":98,"authority":20,"freshness":180,"relevant":22,"comment":397},"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。",[399],{"name":393,"url":390},[27,28,30,401,402],"遥感监测","作物长势",{"code":33,"slug":34,"name":30,"path":35,"parent_code":36},[405],{"code":51,"slug":270,"name":271,"path":272,"parent_code":113},{"status":39,"locked":14,"version":40,"method":41,"evidence":407},{"candidates":408,"agricultural_evidence":46},[409,413,418,423],{"code":33,"score":98,"supported":46,"title":410,"abstract":411,"tags":412},[34],[30,204,34],[30],{"code":51,"score":118,"supported":46,"title":414,"abstract":415,"tags":417},[338],[416,338],"深度学习",[28],{"code":109,"score":419,"supported":46,"title":420,"abstract":421,"tags":422},7,[],[105,125],[401],{"code":57,"score":22,"supported":14,"title":424,"abstract":425,"tags":426},[],[],[27],[428,429],"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":431,"openalex_id":433,"authors":434,"venue":393,"cited_by_count":76,"oa_url":390,"card":440,"direction":166,"ingested_from":83},"W7220838898",[435,438],{"name":436,"orcid":437},"Shashank Gupta","https:\u002F\u002Forcid.org\u002F0000-0003-1291-7951",{"name":439,"orcid":9},"Shefali Arora Chouhan",{"tldr":441,"method":442,"finding":443,"direction":166,"opportunity":444},"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",{"id":447,"title":448,"url":449,"summary":450,"summary_zh":451,"content":9,"source_name":452,"source_url":449,"published_at":453,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":454,"sources":457,"tags":459,"primary_topic":461,"secondary_topics":462,"classification":464,"search_phrases":487,"slug":490,"view_count":228,"doi":491,"paper":492,"created_at":520},4356,"Dual‐Branch Network With Enhanced Feature Fusion for Improved Rice Classification Accuracy","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsae2.70218","ABSTRACT Accurate mapping of rice, one of the world's most important food crops, is critical for monitoring its growth, estimating yields, and supporting global food security. Most deep learning methods for crop classification rely on multi‐temporal optical remote sensing data. However, it is difficult to obtain sufficient ground‐truth samples for time‐series deep learning models through conducting large‐scale field surveys, and persistent cloud and rainfall often limit optical observations during the rice‐growing seasons in subtropical regions. Rice classification based solely on time‐series optical images may therefore be less accurate and reliable. Here, we developed two dual‐branch deep learning networks for effective rice identification: one combining a long short‐term memory network running in parallel with a multi‐layer perceptron (LSTM + MLP), and the other replacing the LSTM with a one‐dimensional convolutional neural network (Conv1d + MLP). These models process temporal sequences in parallel with auxiliary data through the MLP, allowing them to automatically extract phenology information while incorporating auxiliary variables such as texture features, digital elevation model (DEM) data, and the standard deviation of the vegetation index time series. We evaluated these models in a representative county in the subtropical region of China, and compared the accuracies with standalone LSTM, Conv1d, MLP, and random forest (RF) models. The results revealed that LSTM + MLP had the highest accuracy, with an F1‐score of 95.5%, which can automatically extract rice growth patterns and successfully identify the key phenological information of flood signals during the rice transplanting period. By incorporating auxiliary variables, the model achieved an average accuracy improvement of 8.1% relative to the models using only temporal features. The proposed method provides a useful approach for rice mapping in subtropical regions.","准确绘制水稻这一全球最重要的粮食作物之一，对于监测其生长、估算产量以及保障全球粮食安全至关重要。大多数作物分类的深度学习方法依赖于多时相光学遥感数据。然而，通过开展大规模实地调查来获取足够的地面真值样本以用于时间序列深度学习模型十分困难，而且亚热带地区水稻生长季期间持续的云层和降雨往往限制了光学观测。因此，仅基于时间序列光学图像的水稻分类可能精度较低且可靠性不足。本研究开发了两种双分支深度学习网络以实现有效的水稻识别：一种将长短期记忆网络与多层感知机并行运行（LSTM + MLP），另一种则用一维卷积神经网络替代LSTM（Conv1d + MLP）。这些模型通过MLP将时间序列与辅助数据并行处理，使其能够自动提取物候信息，同时纳入纹理特征、数字高程模型（DEM）数据以及植被指数时间序列标准差等辅助变量。我们在中国亚热带地区一个代表性县对这些模型进行了评估，并将精度与单独的LSTM、Conv1d、MLP和随机森林（RF）模型进行了比较。结果表明，LSTM + MLP精度最高，F1分数达到95.5%，能够自动提取水稻生长模式，并成功识别水稻移栽期洪水信号的关键物候信息。通过纳入辅助变量，该模型相对于仅使用时间特征的模型平均精度提高了8.1%。所提出的方法为亚热带地区水稻制图提供了一种有效途径。","Journal of Sustainable Agriculture and Environment","2026-10-06T00:00:00Z",{"impact":118,"substance":455,"depth":19,"authority":179,"freshness":180,"relevant":22,"comment":456},21,"提出双分支深度学习网络融合时序与辅助数据，水稻识别F1达95.5%，方法新颖且结论可靠，对亚热带多云雨区作物遥感制图有实用价值。",[458],{"name":452,"url":449},[27,28,30,105,460],"作物分类",{"code":33,"slug":34,"name":30,"path":35,"parent_code":36},[463],{"code":109,"slug":110,"name":111,"path":112,"parent_code":113},{"status":39,"locked":14,"version":40,"method":41,"evidence":465},{"candidates":466,"agricultural_evidence":46},[467,471,475,479,483],{"code":33,"score":118,"supported":46,"title":468,"abstract":469,"tags":470},[34],[30,34],[30],{"code":109,"score":21,"supported":46,"title":472,"abstract":473,"tags":474},[],[105,125],[460,105],{"code":51,"score":419,"supported":46,"title":476,"abstract":477,"tags":478},[],[416,338],[28],{"code":128,"score":86,"supported":46,"title":480,"abstract":481,"tags":482},[],[131,132],[],{"code":57,"score":22,"supported":14,"title":484,"abstract":485,"tags":486},[],[],[27],[488,489],"水稻 遥感 分类 深度学习","亚热带 水稻 制图","水稻遥感分类深度学习-4356","10.1002\u002Fsae2.70218",{"doi":491,"openalex_id":493,"authors":494,"venue":452,"cited_by_count":76,"oa_url":514,"card":515,"direction":166,"ingested_from":83},"W7220691059",[495,497,500,503,505,508,511],{"name":496,"orcid":9},"Rongfei Ma",{"name":498,"orcid":499},"Wenjiao Shi","https:\u002F\u002Forcid.org\u002F0000-0001-6199-9733",{"name":501,"orcid":502},"Minglei Wang","https:\u002F\u002Forcid.org\u002F0000-0002-4174-9224",{"name":504,"orcid":9},"Xiwei Chen",{"name":506,"orcid":507},"Chenchen Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0458-6232",{"name":509,"orcid":510},"Baihong Pan","https:\u002F\u002Forcid.org\u002F0009-0004-1100-6798",{"name":512,"orcid":513},"Xiangming Xiao","https:\u002F\u002Forcid.org\u002F0000-0003-0956-7428","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fsae2.70218",{"tldr":516,"method":517,"finding":518,"direction":166,"opportunity":519},"提出双分支深度学习网络融合时序与辅助数据，提升亚热带水稻分类精度。","LSTM\u002FConv1d与MLP并行双分支网络，融合时序植被指数、纹理、DEM等辅","LSTM+MLP模型F1达95.5%，辅助变量使精度平均提升8.1%，能识别插秧期淹水信号。","可探索多源遥感与气象数据融合，解决多云雨地区时序缺失下的作物精细制图问题。","2026-10-07T23:30:25.945309Z"]