[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2491":3,"related-2491":89},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":74,"slug":77,"view_count":21,"doi":8,"paper":78,"created_at":88},2491,"DASO-RiceNet: 细粒度水稻病害与损伤分类的双注意力序列优化网络(准确率 96.5%)","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1945","台湾朝阳科技大学 Yung-Fa Huang 等 2026 年 9 月 9 日在《Agriculture》发表(专刊'Applying Artificial Intelligence to Sustainable Crop Protection: Managing Pests and Diseases')。提出 DASO-RiceNet(Dual-Attention Semantic Optimization Network)深度学习框架,采用多阶段残差骨干进行特征提取,序列双注意力模块融合通道与空间注意力以强调诊断相关特征并抑制背景信息。在 10 类水稻病害与损伤数据集上,DASO-RiceNet 准确率 0.965、宏精确率 0.963、宏召回率 0.966、宏 F1 0.964,优于所对比的 CNN 与 Transformer 基线模型。Grad-CAM 和 LIME 提供了模型预测的定性洞察。",null,"MDPI Agriculture 2026-09-09","2026-09-08T16:00:00Z","论文",10,false,75,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,21,17,13,8,1,"细粒度水稻病害识别新框架，指标扎实且具可解释性，对作物病虫害智能诊断有参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","可解释AI","深度学习","水稻病害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":45,"locked":13,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,56,62,69],{"code":32,"score":51,"supported":52,"title":53,"abstract":54,"tags":55},12,true,[34],[34],[30],{"code":39,"score":57,"supported":52,"title":58,"abstract":59,"tags":61},9,[],[29,60],"artificial intelligence",[27,28,29],{"code":63,"score":64,"supported":52,"title":65,"abstract":66,"tags":68},"F4",3,[],[67],"crop protection",[],{"code":70,"score":21,"supported":13,"title":71,"abstract":72,"tags":73},"I1",[],[],[26],[75,76],"农业人工智能 智慧农业 水稻病害 深度学习","农业人工智能 智慧农业","农业人工智能智慧农业水稻病害深度学习-2491",{"doi":8,"openalex_id":8,"authors":79,"venue":8,"cited_by_count":80,"oa_url":8,"card":81,"direction":85,"ingested_from":87},[],0,{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"提出双注意力序列优化网络DASO-RiceNet，实现10类水稻病害与损伤的高精度分类。","多阶段残差骨干+序列双注意力（通道与空间），在10类水稻病害数据集上训练评估。","准确率96.5%、宏F1 0.964，优于CNN与Transformer基线，Grad-CAM\u002FLI","农业人工智能与决策模型","可探索轻量化部署与田间复杂背景下的跨域泛化，并结合多模态数据提升早期病害预警能力。","agent","2026-09-15T00:04:27.118669Z",{"total":90,"page":21,"page_size":90,"items":91},6,[92,152,229,302,357,439],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":8,"source_name":98,"source_url":95,"published_at":99,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":100,"score_detail":101,"sources":104,"tags":106,"primary_topic":108,"secondary_topics":109,"classification":111,"search_phrases":131,"slug":134,"view_count":135,"doi":136,"paper":137,"created_at":151},2068,"Leakage-Proof Reliability-Aware Deep Learning Framework for Rice Leaf Disease Diagnosis","https:\u002F\u002Fdoi.org\u002F10.14445\u002F22315381\u002Fijett-v74i8p113","Timely and accurate diagnosis of rice leaf diseases is significant in order to achieve reduced loss and precision agriculture management. In this paper, a leakage-aware and reliability-oriented deep learning framework is proposed to classify rice leaf diseases based on the Mendeley rice leaf disease dataset. In order to avoid the problem of similar and duplicate images in the training and validation set, the framework incorporates perceptual hashing and stratified group K-fold cross-validation. The evaluation measures the accuracy of the in-domain classification, robustness to controlled image corruption, evaluation of the Expected Calibration Error and temperature scaling methods, selective prediction using risk-coverage analysis, external-dataset validation, and computational efficiency profiling. The in-domain evaluation resulted in an almost perfect score. The accuracy achieved by the cross-validation method applied to the folds evaluated showed high values, while the external UCI rice leaf dataset presented significantly lower values, with an accuracy of 54.17% and macro-F1 of 48.98%. This contrast suggests that high in-domain accuracy should be considered with caution if there is a domain shift. In addition, efficiency and reliability analysis indicated that mid-scale convolutional models, especially ResNet-based models, achieved a better trade-off between predictive performance and computational cost compared to larger high-capacity models. The suggested assessment scheme in the proposed framework then focuses not only on accuracy but also on deployment-oriented evaluation in the context of rice leaf disease diagnosis with the help of AI.","及时、准确地诊断水稻叶片病害对于减少损失和实现精准农业管理具有重要意义。本文基于Mendeley水稻叶片病害数据集，提出了一种感知泄漏且面向可靠性的深度学习框架，用于水稻叶片病害分类。为避免训练集与验证集中出现相似和重复图像的问题，该框架引入了感知哈希（perceptual hashing）和分层分组K折交叉验证。评估内容包括域内分类准确率、对受控图像损坏的鲁棒性、期望校准误差（Expected Calibration Error）与温度缩放方法的评估、基于风险-覆盖率分析的选择性预测、外部数据集验证以及计算效率分析。域内评估取得了近乎完美的分数。应用于各折评估的交叉验证方法所达到的准确率较高，而外部UCI水稻叶片数据集则呈现出显著较低的值，准确率为54.17%，宏F1为48.98%。这一对比表明，若存在域偏移，高域内准确率应谨慎看待。此外，效率与可靠性分析表明，中等规模的卷积模型，尤其是基于ResNet的模型，相较于更大的高容量模型，在预测性能与计算成本之间实现了更好的权衡。因此，所提框架中的建议评估方案不仅关注准确率，还关注在人工智能辅助水稻叶片病害诊断背景下面向部署的评估。","International Journal of Engineering Trends and Technology","2026-09-09T00:00:00Z",70,{"impact":51,"substance":102,"depth":18,"authority":51,"freshness":57,"relevant":21,"comment":103},20,"提出防数据泄漏与可靠性评估的水稻叶病诊断框架，外部数据集准确率骤降至54.17%，对农业AI落地评估有实质参考价值。",[105],{"name":98,"url":95},[26,27,29,30,107],"模型可靠性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[110],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":13,"version":46,"method":47,"evidence":112},{"candidates":113,"agricultural_evidence":52},[114,119,124],{"code":39,"score":16,"supported":52,"title":115,"abstract":117,"tags":118},[116],"deep learning",[29,116],[27,29],{"code":32,"score":120,"supported":52,"title":121,"abstract":122,"tags":123},15,[33],[34,33],[30],{"code":70,"score":125,"supported":52,"title":126,"abstract":127,"tags":130},7,[],[128,129],"精准农业","precision agriculture",[26],[132,133],"农业人工智能 模型可靠性 智慧农业 水稻病害","农业人工智能 模型可靠性","农业人工智能模型可靠性智慧农业水稻病害-2068",2,"10.14445\u002F22315381\u002Fijett-v74i8p113",{"doi":136,"openalex_id":138,"authors":139,"venue":98,"cited_by_count":80,"oa_url":144,"card":145,"direction":85,"ingested_from":150},"W7212016871",[140,142],{"name":141,"orcid":8},"Sujeet Kumar",{"name":143,"orcid":8},"Prajeet Sharma","https:\u002F\u002Fijettjournal.org\u002FVolume-74\u002FIssue-8\u002FIJETT-V74I8P113.pdf",{"tldr":146,"method":147,"finding":148,"direction":85,"opportunity":149},"提出防泄漏、可靠性导向的水稻叶病诊断深度学习框架，并系统评估其泛化与部署表现。","感知哈希与分层组K折交叉验证，结合校准、选择性预测及外部数据集验证。","域内准确率近完美，但外部UCI数据集仅54.17%，域偏移下高精度需谨慎。","可研究跨域自适应与轻量模型校准，提升叶病诊断在真实田间场景的泛化可靠性。","openalex","2026-09-10T23:30:43.799587Z",{"id":153,"title":154,"url":155,"summary":156,"summary_zh":157,"content":8,"source_name":158,"source_url":155,"published_at":159,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":160,"score_detail":161,"sources":165,"tags":167,"primary_topic":170,"secondary_topics":171,"classification":173,"search_phrases":197,"slug":200,"view_count":21,"doi":201,"paper":202,"created_at":228},4993,"Effects of within-season temporal aggregation of weather variables on performance and interpretability in rice yield machine learning models","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75646-2","While crop responses are inherently stage-dependent, machine learning yield models often rely on weather variables aggregated over the entire growing season. This study evaluates how within-season temporal aggregation of weather variables affects both predictive performance and interpretability in machine learning-based rice yield modeling. Using CYGMA (Crop Yield Growth Model with Assumptions on Climate and Socioeconomics)-simulated potential yield as a synthetic response variable, we minimized confounding effects from observational noise and management variability, enabling a clearer evaluation of the role of within-season weather aggregation. Daily weather variables were averaged over the rice growing period after dividing each season into multiple equal-length segments, and several machine learning algorithms were trained and evaluated using temporally independent validation. Increasing within-season temporal resolution improved model performance up to a moderate level, after which gains diminished. Interpretation using SHAP (SHapley Additive exPlanations) and response diagnostics consistently indicated that yield sensitivity concentrates in specific windows of the season, with temperature-related effects emerging most strongly early in the season and humidity-related effects later. Finer temporal aggregation sharpened the identification of window-specific nonlinear and threshold-like responses that were obscured under coarse seasonal averages. Overall, moderate within-season aggregation provides a practical balance between predictive skill and mechanistic interpretability, supporting more reliable identification of phenology-relevant meteorological risks for climate-impact assessment and decision support.","尽管作物响应本质上具有阶段依赖性，但机器学习产量模型往往依赖于整个生长季聚合的天气变量。本研究评估了天气变量的季节内时间聚合如何影响基于机器学习的水稻产量建模的预测性能与可解释性。以CYGMA（Crop Yield Growth Model with Assumptions on Climate and Socioeconomics，气候与社会经济假设下的作物产量生长模型）模拟的潜在产量作为合成响应变量，我们最大限度地减少了观测噪声和管理变异性带来的混杂效应，从而能够更清晰地评估季节内天气聚合的作用。将每个生长季划分为多个等长时段后，对水稻生长期内的逐日天气变量进行平均，并采用时间独立验证对多种机器学习算法进行训练和评估。提高季节内时间分辨率可提升模型性能，直至达到中等水平，此后增益递减。利用SHAP（SHapley Additive exPlanations，沙普利加性解释）和响应诊断进行的解释一致表明，产量敏感性集中在季节的特定窗口，温度相关效应在季节早期最为显著，湿度相关效应则在后期出现。更精细的时间聚合增强了对窗口特异性非线性和阈值型响应的识别，而这些响应在粗粒度季节平均下被掩盖。总体而言，中等程度的季节内聚合在预测能力与机制可解释性之间提供了实用平衡，有助于更可靠地识别与物候相关的气象风险，以支持气候影响评估和决策支持。","Scientific Reports","2026-10-13T00:00:00Z",72,{"impact":51,"substance":102,"depth":162,"authority":163,"freshness":20,"relevant":21,"comment":164},18,14,"该研究通过CYGMA模拟数据系统评估了生长季内气象变量时间聚合对水稻产量机器学习模型性能与可解释性的影响，方法新颖、结论可靠，对农业气象风险识别与决策支持有参考价值，但属学术论文，公共影响范围有限。",[166],{"name":158,"url":155},[26,27,28,168,169],"农业气象","水稻产量预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[172],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":13,"version":46,"method":47,"evidence":174},{"candidates":175,"agricultural_evidence":52},[176,184,188,193],{"code":39,"score":177,"supported":52,"title":178,"abstract":180,"tags":183},19,[179],"machine learning",[181,179,182],"机器学习","decision support",[27,28],{"code":32,"score":163,"supported":52,"title":185,"abstract":186,"tags":187},[33],[34,33],[],{"code":189,"score":21,"supported":13,"title":190,"abstract":191,"tags":192},"H7",[],[],[168],{"code":70,"score":21,"supported":13,"title":194,"abstract":195,"tags":196},[],[],[26],[198,199],"水稻产量 机器学习 气象","SHAP 水稻 产量预测","水稻产量机器学习气象-4993","10.1038\u002Fs41598-026-75646-2",{"doi":201,"openalex_id":203,"authors":204,"venue":158,"cited_by_count":80,"oa_url":221,"card":222,"direction":227,"ingested_from":150},"W7221657053",[205,207,209,211,213,215,218],{"name":206,"orcid":8},"Noh-Hyun Lee",{"name":208,"orcid":8},"Wonjae Jeong",{"name":210,"orcid":8},"Hyeon-Ji Yang",{"name":212,"orcid":8},"Hana Cho",{"name":214,"orcid":8},"Hyeon-Yeong Ju",{"name":216,"orcid":217},"Toshichika Iizumi","https:\u002F\u002Forcid.org\u002F0000-0002-0611-4637",{"name":219,"orcid":220},"Ki‐Tae Kim","https:\u002F\u002Forcid.org\u002F0000-0002-8249-5283","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75646-2_reference.pdf",{"tldr":223,"method":224,"finding":225,"direction":85,"opportunity":226},"评估生长季内天气变量时间聚合粒度对水稻产量机器学习模型预测与可解释性的影响。","用CYGMA模拟潜在产量作响应变量，按等长时段聚合逐日天气，训练多种机器学习模型","适度提高季内时间分辨率可提升性能，产量敏感性集中在特定窗口，温度效应偏早、湿度效应偏晚。","可探索不同作物与气候区下最优聚合粒度，并将窗口特异性气象风险嵌入气候影响评估与决策支持系统。","智慧农业 \u002F 农业物联网","2026-10-15T23:30:10.483881Z",{"id":230,"title":231,"url":232,"summary":233,"summary_zh":234,"content":8,"source_name":235,"source_url":232,"published_at":236,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":237,"score_detail":238,"sources":241,"tags":243,"primary_topic":246,"secondary_topics":247,"classification":254,"search_phrases":275,"slug":278,"view_count":135,"doi":279,"paper":280,"created_at":301},2646,"IoT-RiceMobileNet: An improved lightweight MobileNetV2 Model for real-time multi-class rice disease detection using IoT","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0356383","Early and real-time detection of rice leaf diseases (RLD) poses a significant challenge for farmers, especially in rural regions with limited access to advanced technology. Conventional deep learning models often require substantial computational resources, rendering them impractical for deployment on mobile or edge devices commonly used in agricultural environments. Although models such as ResNet50, VGG16, and InceptionV3 can achieve high accuracy, they are computationally expensive and may be less suitable for real-time deployment in smart agricultural systems. Furthermore, many existing studies rely on limited datasets, which can restrict model generalizability under diverse real-world conditions. Although transfer learning can improve classification performance, developing lightweight models suitable for real-time IoT deployment remains challenging. To address these limitations, we curated a hybrid dataset of 7,092 rice leaf images by combining self-collected and Kaggle samples and proposed IoT-RiceMobileNet, a lightweight improved MobileNetV2-based model that can be effectively integrated into our developed IoT system. The proposed model outperformed transfer learning and deep learning baseline models, achieving 99.19% test accuracy and 99.20% precision while maintaining low computational complexity. We further confirmed model stability using stratified 5-fold and 10-fold cross-validation on both the constructed RLD and multi-source datasets, achieving mean accuracies of 98.04% and 98.13% on the constructed RLD dataset and 98.11% and 98.58% on the multi-source dataset, respectively. Moreover, our models compact size and 85.17 FPS inference speed support real-time deployment. Finally, the model was integrated into an IoT-enabled mobile, web, and cloud-based inference framework, demonstrating its practical potential for scalable rice disease detection in smart agriculture.","水稻叶片病害（RLD）的早期和实时检测对农民而言是一项重大挑战，尤其是在难以获取先进技术的农村地区。传统的深度学习模型通常需要大量计算资源，使其难以在实际农业环境中常用的移动或边缘设备上部署。尽管ResNet50、VGG16和InceptionV3等模型可以达到较高精度，但其计算成本高昂，可能不太适合在智慧农业系统中实时部署。此外，许多现有研究依赖有限的数据集，这可能限制模型在多样化真实条件下的泛化能力。尽管迁移学习可以提升分类性能，但开发适合实时物联网部署的轻量级模型仍具有挑战性。为解决这些局限，我们通过整合自采集样本和Kaggle样本，构建了一个包含7092张水稻叶片图像的混合数据集，并提出了IoT-RiceMobileNet，这是一种基于MobileNetV2改进的轻量级模型，可有效集成到我们开发的物联网系统中。所提出的模型优于迁移学习和深度学习基线模型，在保持低计算复杂度的同时，实现了99.19%的测试准确率和99.20%的精确率。我们进一步通过在构建的RLD数据集和多源数据集上采用分层5折和10折交叉验证确认了模型稳定性，在构建的RLD数据集上分别达到98.04%和98.13%的平均准确率，在多源数据集上分别达到98.11%和98.58%的平均准确率。此外，该模型紧凑的规模和85.17 FPS的推理速度支持实时部署。最后，该模型被集成到一个支持物联网的移动端、网页端和云端推理框架中，展示了其在智慧农业中可扩展水稻病害检测的实际潜力。","PLoS ONE","2026-09-15T00:00:00Z",80,{"impact":162,"substance":239,"depth":162,"authority":19,"freshness":57,"relevant":21,"comment":240},22,"提出轻量级MobileNetV2水稻病害检测模型并集成物联网推理框架，实测精度与推理速度俱佳，方法新颖、数据规模可观，对边缘端智慧农业落地有参考价值。",[242],{"name":235,"url":232},[26,27,244,30,245],"物联网","轻量化模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[248,253],{"code":249,"slug":250,"name":251,"path":252,"parent_code":43},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":13,"version":46,"method":47,"evidence":255},{"candidates":256,"agricultural_evidence":52},[257,261,266,270],{"code":32,"score":120,"supported":52,"title":258,"abstract":259,"tags":260},[33],[34,33],[30],{"code":249,"score":120,"supported":52,"title":262,"abstract":264,"tags":265},[263],"IoT",[244,263],[244],{"code":39,"score":20,"supported":52,"title":267,"abstract":268,"tags":269},[],[29,116],[27,245],{"code":70,"score":125,"supported":52,"title":271,"abstract":272,"tags":274},[],[26,273],"smart agriculture",[26],[276,277],"农业人工智能 轻量化模型 智慧农业 水稻病害","农业人工智能 轻量化模型","农业人工智能轻量化模型智慧农业水稻病害-2646","10.1371\u002Fjournal.pone.0356383",{"doi":279,"openalex_id":281,"authors":282,"venue":235,"cited_by_count":80,"oa_url":232,"card":296,"direction":227,"ingested_from":150},"W7213328321",[283,285,287,289,291,293],{"name":284,"orcid":8},"Khawja Imran Masud",{"name":286,"orcid":8},"Md Yasin Zihad",{"name":288,"orcid":8},"Mehedi Hasan Shuvo",{"name":290,"orcid":8},"Mst Raonik Jannat",{"name":292,"orcid":8},"Jia Uddin",{"name":294,"orcid":295},"Sahara Ali","https:\u002F\u002Forcid.org\u002F0000-0002-8578-948X",{"tldr":297,"method":298,"finding":299,"direction":227,"opportunity":300},"提出轻量级IoT-RiceMobileNet模型，实现水稻病害实时多类检测。","改进MobileNetV2，融合自采与Kaggle共7092张图像，IoT端部署","测试准确率99.19%，推理速度85.17 FPS，适合边缘设备实时检测。","可探索多作物病害泛化、田间复杂光照下轻量模型鲁棒性及边缘联邦学习。","2026-09-16T23:30:12.197386Z",{"id":303,"title":304,"url":305,"summary":306,"summary_zh":307,"content":8,"source_name":308,"source_url":305,"published_at":309,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":237,"score_detail":310,"sources":312,"tags":314,"primary_topic":317,"secondary_topics":318,"classification":320,"search_phrases":336,"slug":339,"view_count":80,"doi":340,"paper":341,"created_at":356},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":162,"substance":239,"depth":162,"authority":19,"freshness":57,"relevant":21,"comment":311},"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。",[313],{"name":308,"url":305},[26,27,34,315,316],"病虫害识别","联邦学习",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[319],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":13,"version":46,"method":47,"evidence":321},{"candidates":322,"agricultural_evidence":52},[323,328,332],{"code":32,"score":120,"supported":52,"title":324,"abstract":326,"tags":327},[325],"paddy",[34,325],[34],{"code":39,"score":51,"supported":52,"title":329,"abstract":330,"tags":331},[],[181,179,182],[27,315,316],{"code":70,"score":21,"supported":13,"title":333,"abstract":334,"tags":335},[],[],[26],[337,338],"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":340,"openalex_id":342,"authors":343,"venue":308,"cited_by_count":80,"oa_url":350,"card":351,"direction":227,"ingested_from":150},"W7221522859",[344,347],{"name":345,"orcid":346},"B Johnson","https:\u002F\u002Forcid.org\u002F0009-0009-8281-4051",{"name":348,"orcid":349},"Chandrakumar Thangavel","https:\u002F\u002Forcid.org\u002F0000-0002-1186-5988","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5486058",{"tldr":352,"method":353,"finding":354,"direction":85,"opportunity":355},"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":358,"title":359,"url":360,"summary":361,"summary_zh":8,"content":8,"source_name":362,"source_url":360,"published_at":363,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":364,"score_detail":365,"sources":367,"tags":369,"primary_topic":372,"secondary_topics":373,"classification":380,"search_phrases":400,"slug":403,"view_count":21,"doi":404,"paper":405,"created_at":438},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":162,"substance":102,"depth":162,"authority":12,"freshness":20,"relevant":21,"comment":366},"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。",[368],{"name":362,"url":360},[26,27,34,370,371],"遥感","病害检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[374,375],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":376,"slug":377,"name":378,"path":379,"parent_code":43},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":45,"locked":13,"version":46,"method":47,"evidence":381},{"candidates":382,"agricultural_evidence":52},[383,387,391,396],{"code":32,"score":51,"supported":52,"title":384,"abstract":385,"tags":386},[33],[33],[34],{"code":39,"score":51,"supported":52,"title":388,"abstract":389,"tags":390},[116],[116],[27],{"code":376,"score":51,"supported":52,"title":392,"abstract":394,"tags":395},[393],"remote sensing",[393],[370],{"code":70,"score":21,"supported":13,"title":397,"abstract":398,"tags":399},[],[],[26],[401,402],"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":404,"openalex_id":406,"authors":407,"venue":362,"cited_by_count":80,"oa_url":360,"card":8,"direction":437,"ingested_from":150},"W7221055917",[408,411,414,416,418,421,423,425,428,430,432,434],{"name":409,"orcid":410},"Carlito B. Balingbing","https:\u002F\u002Forcid.org\u002F0000-0002-3672-9253",{"name":412,"orcid":413},"Dmytro Chebotarov","https:\u002F\u002Forcid.org\u002F0000-0003-1351-9453",{"name":415,"orcid":8},"Ian  Carlo Perilla",{"name":417,"orcid":8},"Stephen Klassen",{"name":419,"orcid":420},"Nancy Castilla","https:\u002F\u002Forcid.org\u002F0000-0002-9143-4933",{"name":422,"orcid":8},"Raphael  Gabriel Carvajal",{"name":424,"orcid":8},"Kendrix Ortega",{"name":426,"orcid":427},"Joseph M. Sandro","https:\u002F\u002Forcid.org\u002F0000-0002-8311-2299",{"name":429,"orcid":8},"Nurmi Pangesti",{"name":431,"orcid":8},"Caesar Arloo R. Centeno",{"name":433,"orcid":8},"Arnel Rala",{"name":435,"orcid":436},"Li-Yu  Daisy Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6997-8101","农业遥感与作物表型","2026-10-09T23:30:18.390549Z",{"id":440,"title":441,"url":442,"summary":443,"summary_zh":444,"content":8,"source_name":158,"source_url":442,"published_at":445,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":446,"score_detail":447,"sources":449,"tags":451,"primary_topic":454,"secondary_topics":455,"classification":457,"search_phrases":477,"slug":480,"view_count":135,"doi":481,"paper":482,"created_at":495},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。","2026-10-07T00:00:00Z",81,{"impact":162,"substance":239,"depth":162,"authority":163,"freshness":57,"relevant":21,"comment":448},"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。",[450],{"name":158,"url":442},[26,27,34,452,453],"遥感监测","作物长势",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[456],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":13,"version":46,"method":47,"evidence":458},{"candidates":459,"agricultural_evidence":52},[460,465,469,473],{"code":32,"score":162,"supported":52,"title":461,"abstract":462,"tags":464},[33],[34,463,33],"稻田",[34],{"code":39,"score":120,"supported":52,"title":466,"abstract":467,"tags":468},[116],[29,116],[27],{"code":376,"score":125,"supported":52,"title":470,"abstract":471,"tags":472},[],[370,393],[452],{"code":70,"score":21,"supported":13,"title":474,"abstract":475,"tags":476},[],[],[26],[478,479],"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":481,"openalex_id":483,"authors":484,"venue":158,"cited_by_count":80,"oa_url":442,"card":490,"direction":437,"ingested_from":150},"W7220838898",[485,488],{"name":486,"orcid":487},"Shashank Gupta","https:\u002F\u002Forcid.org\u002F0000-0003-1291-7951",{"name":489,"orcid":8},"Shefali Arora Chouhan",{"tldr":491,"method":492,"finding":493,"direction":437,"opportunity":494},"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"]