[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2900":3,"related-2900":96},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":80,"slug":83,"view_count":84,"doi":8,"paper":85,"created_at":95},2900,"Crop-GPA 2.0:安徽农业大学团队发布跨物种基因-表型预测深度学习工具,登The Crop Journal","https:\u002F\u002Fwww.global-agriculture.com\u002Fag-tech-research-news\u002Fchinese-researchers-launch-ai-tool-that-predicts-crop-gene-trait-links-across-species\u002F","安徽农业大学岳振宇教授团队(第一作者高玉佳)开发的Crop-GPA 2.0跨物种基因-表型预测深度学习工具于2026年9月10日发表在The Crop Journal。该工具通过分层基因组表征、跨物种预训练、性状感知学习三种联动技术,在水稻、玉米、小麦测试中跨性状(产量、抗病性、抗逆性、籽粒品质)均优于现有方法,且能在新物种或训练数据有限时保持较好性能,在线平台为育种者提供免费SNP排名服务,极大降低标记辅助选择门槛。",null,"Global Agriculture \u002F The Crop Journal","2026-09-14T00:00:00Z","论文",10,false,83,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,23,18,14,6,1,"跨物种基因-表型预测工具登核心期刊，方法新颖、结论可靠，对智能育种有实质推动，值得进入每日精选。",[24],{"name":9,"url":6},[26,27,28,29,30,31],"智慧农业","农业人工智能","水稻","智能育种","玉米","基因-表型预测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":13,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,58,63,70,75],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},12,true,[49],"深度学习",[49],[27],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":57},"A1",4,[],[28],[28],{"code":59,"score":54,"supported":47,"title":60,"abstract":61,"tags":62},"A3",[],[30],[30],{"code":64,"score":65,"supported":47,"title":66,"abstract":67,"tags":69},"A2",3,[],[68],"小麦",[],{"code":71,"score":21,"supported":13,"title":72,"abstract":73,"tags":74},"F2",[],[],[29],{"code":76,"score":21,"supported":13,"title":77,"abstract":78,"tags":79},"I1",[],[],[26],[81,82],"安徽农业大学 Crop-GPA 2.0","Crop-GPA 2.0 基因-表型预测","安徽农业大学Crop-GPA2.0-2900",5,{"doi":8,"openalex_id":8,"authors":86,"venue":8,"cited_by_count":87,"oa_url":8,"card":88,"direction":92,"ingested_from":94},[],0,{"tldr":89,"method":90,"finding":91,"direction":92,"opportunity":93},"安徽农业大学团队发布跨物种基因-表型预测深度学习工具Crop-GPA 2.0，并在水稻、玉米、小麦中","分层基因组表征、跨物种预训练、性状感知学习，基于多物种SNP与表型数据。","跨性状预测均优于现有方法，新物种或小样本下仍表现良好，并提供免费在线SNP排名服务。","农业人工智能与决策模型","可探索将跨物种预训练模型与田间表型组、环境数据融合，提升复杂性状预测与育种决策的可解释性。","agent","2026-09-19T00:06:08.595080Z",{"total":20,"page":21,"page_size":20,"items":97},[98,185,229,289,347,430],{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":8,"source_name":104,"source_url":101,"published_at":105,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":106,"score_detail":107,"sources":112,"tags":114,"primary_topic":116,"secondary_topics":117,"classification":124,"search_phrases":162,"slug":165,"view_count":87,"doi":166,"paper":167,"created_at":184},5496,"Multi-Crop Irrigation Need Classification Based on Machine Learning with an Indonesian-Language GenAI-UX Recommendation Layer","https:\u002F\u002Fdoi.org\u002F10.30871\u002Fjaic.v10i5.13774","Inefficient irrigation water use remains a critical challenge in Indonesian agriculture, and the complexity of raw sensor data hinders adoption by smallholder farmers who lack data-analysis skills. This study develops an intelligent irrigation Decision Support System that integrates interpretable Machine Learning with an Indonesian-language GenAI-UX recommendation layer, using a publicly available multi-crop irrigation scheduling dataset (501 records, nine crops) with an analytical focus on paddy and maize. Four components are combined into one pipeline: (i) irrigation-need classification compared across six algorithms; (ii) regression of a continuous water-requirement score; (iii) sensor-anomaly detection using Isolation Forest cross-validated with the Interquartile Range method; and (iv) a rule-based natural-language recommendation layer whose operating point is optimized by a Genetic Algorithm. Validity was safeguarded through stratified five-fold cross-validation and leakage-controlled preprocessing. Gradient Boosting achieved the best classification performance (F1-score 0.980; cross-validated F1 0.963), Random Forest Regressor reached R square of 0.807, Isolation Forest agreed with IQR outliers at 95.2%, and the Genetic Algorithm attained a confidence of 0.993 within realistic agronomic bounds. In an illustrative usability evaluation, the GenAI-UX interface obtained the highest System Usability Scale score (Good category). The results indicate that pairing accurate, interpretable models with a language-based delivery layer can transform complex sensor data into actionable irrigation recommendations, lowering the adoption barrier for non-technical users. Field validation with real respondents and edge deployment are identified as the next steps.","灌溉用水效率低下仍是印度尼西亚农业面临的一项关键挑战，而原始传感器数据的复杂性阻碍了缺乏数据分析技能的小农户采用相关技术。本研究开发了一种智能灌溉决策支持系统（Decision Support System），将可解释的机器学习与印度尼西亚语生成式人工智能用户体验（GenAI-UX）推荐层相结合，使用公开可用的多作物灌溉调度数据集（501条记录，九种作物），并以水稻和玉米为分析重点。四个组件被整合为一条流水线：（i）在六种算法之间比较灌溉需求分类；（ii）对连续需水量得分进行回归；（iii）使用孤立森林（Isolation Forest）进行传感器异常检测，并与四分位距（IQR）方法交叉验证；（iv）基于规则的自然语言推荐层，其操作点由遗传算法优化。通过分层五折交叉验证和防泄漏预处理保障了有效性。梯度提升（Gradient Boosting）取得了最佳分类性能（F1分数0.980；交叉验证F1为0.963），随机森林回归器（Random Forest Regressor）达到R平方0.807，孤立森林与IQR异常值的一致率为95.2%，遗传算法在现实农艺范围内达到0.993的置信度。在一项示例性可用性评估中，GenAI-UX界面获得了最高的系统可用性量表（System Usability Scale）得分（良好类别）。结果表明，将准确、可解释的模型与基于语言的交付层相结合，能够将复杂的传感器数据转化为可操作的灌溉建议，从而降低非技术用户的采用门槛。与真实受访者进行田间验证以及边缘部署被确定为下一步工作。","Journal of Applied Informatics and Computing","2026-10-19T00:00:00Z",69,{"impact":46,"substance":108,"depth":109,"authority":46,"freshness":110,"relevant":21,"comment":111},20,17,8,"方法组合完整、指标扎实的农业AI决策支持论文，但属印尼小农户场景的实验室级研究，公共影响有限，适合作为技术参考而非每日精选头条。",[113],{"name":104,"url":101},[26,27,28,30,115],"灌溉决策",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[118],{"code":119,"slug":120,"name":121,"path":122,"parent_code":123},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":40,"locked":13,"version":41,"method":42,"evidence":125},{"candidates":126,"agricultural_evidence":47},[127,134,140,146,151,158],{"code":33,"score":18,"supported":47,"title":128,"abstract":130,"tags":133},[129],"machine learning",[131,129,132],"机器学习","decision support",[27],{"code":119,"score":135,"supported":47,"title":136,"abstract":137,"tags":139},15,[120],[138,120],"灌溉",[115],{"code":53,"score":141,"supported":47,"title":142,"abstract":143,"tags":145},7,[],[28,144],"paddy",[28],{"code":59,"score":141,"supported":47,"title":147,"abstract":148,"tags":150},[],[30,149],"maize",[30],{"code":152,"score":20,"supported":47,"title":153,"abstract":154,"tags":157},"G2",[],[155,156],"小农户","smallholder",[],{"code":76,"score":21,"supported":13,"title":159,"abstract":160,"tags":161},[],[],[26],[163,164],"印尼 多作物 灌溉决策","GenAI 灌溉推荐","印尼多作物灌溉决策-5496","10.30871\u002Fjaic.v10i5.13774",{"doi":166,"openalex_id":168,"authors":169,"venue":104,"cited_by_count":87,"oa_url":101,"card":177,"direction":92,"ingested_from":183},"W7223016583",[170,173,175],{"name":171,"orcid":172},"Muhammad Rusdi","https:\u002F\u002Forcid.org\u002F0000-0002-1048-5000",{"name":174,"orcid":8},"Zaenuddin Zaenuddin",{"name":176,"orcid":8},"Hendra Sanjaya",{"tldr":178,"method":179,"finding":180,"direction":181,"opportunity":182},"构建融合可解释机器学习与印尼语GenAI推荐层的智能灌溉决策支持系统。","六种分类算法、回归、孤立森林异常检测与遗传算法优化规则推荐层。","梯度提升分类F1达0.980，GenAI界面可用性最佳，降低非技术用户使用门槛。","智慧农业 \u002F 农业物联网","可探索多语言本地化GenAI推荐层与边缘部署，并开展真实农户田间验证。","openalex","2026-10-21T23:30:46.441864Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":8,"content":8,"source_name":190,"source_url":8,"published_at":191,"category":192,"cover_url":8,"hotness":12,"is_selected":13,"score":193,"score_detail":194,"sources":197,"tags":199,"primary_topic":202,"secondary_topics":203,"classification":204,"search_phrases":224,"slug":227,"view_count":87,"doi":8,"paper":8,"created_at":228},5331,"农民日报报道 WAFI 2026:AI 为农业注入新动能 推动智能育种精准种植全产业链转型","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20261014\u002F20261014_8\u002Fnmrb_20261014_13425_8_2110120867111473164.html","2026 世界农业科技创新大会(WAFI)在北京举行,中国农业大学校长陈卫表示 AI 正在推动智能育种、精准种植和全产业链转型,小农户迎来手机端病虫害预警、本地化气象预警等新机会。中国农业大学王耀君宣布在肯尼亚发布神农大模型,针对 250 个肯尼亚问题通用大语言模型准确率 70%、神农达 96%、平均成本仅 0.1 美分。盖茨基金会高级项目官斯图尔特·科里斯认为 AI 农技咨询有望在亚非 7 个重点地区触达 4200 万农户,每年带来超 30 亿美元收益。北大荒 2025 年粮食产量达 463 亿斤,数字农服平台服务60 万种植户。","农民日报 2026-10-14","2026-10-14T00:00:00Z","报道",84,{"impact":195,"substance":16,"depth":109,"authority":19,"freshness":84,"relevant":21,"comment":196},26,"世界农业科技创新大会的权威报道，含神农大模型海外发布、AI农技咨询覆盖数据等实质增量，产业级影响，值得精选。",[198],{"name":190,"url":188},[26,27,200,29,201],"神农大模型","数字农服",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":13,"version":41,"method":42,"evidence":205},{"candidates":206,"agricultural_evidence":47},[207,212,216,220],{"code":33,"score":84,"supported":47,"title":208,"abstract":209,"tags":211},[],[210],"大模型",[27,200],{"code":152,"score":65,"supported":47,"title":213,"abstract":214,"tags":215},[],[155],[],{"code":71,"score":21,"supported":13,"title":217,"abstract":218,"tags":219},[],[],[29],{"code":76,"score":21,"supported":13,"title":221,"abstract":222,"tags":223},[],[],[26],[225,226],"神农大模型 肯尼亚","WAFI 2026 中国农业大学","神农大模型肯尼亚-5331","2026-10-20T02:47:36.680168Z",{"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":242,"tags":244,"primary_topic":247,"secondary_topics":251,"classification":253,"search_phrases":268,"slug":271,"view_count":87,"doi":272,"paper":273,"created_at":288},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",80,{"impact":18,"substance":16,"depth":18,"authority":239,"freshness":240,"relevant":21,"comment":241},13,9,"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。",[243],{"name":235,"url":232},[26,27,28,245,246],"病虫害识别","联邦学习",{"code":53,"slug":248,"name":28,"path":249,"parent_code":250},"rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[252],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":13,"version":41,"method":42,"evidence":254},{"candidates":255,"agricultural_evidence":47},[256,260,264],{"code":53,"score":135,"supported":47,"title":257,"abstract":258,"tags":259},[144],[28,144],[28],{"code":33,"score":46,"supported":47,"title":261,"abstract":262,"tags":263},[],[131,129,132],[27,245,246],{"code":76,"score":21,"supported":13,"title":265,"abstract":266,"tags":267},[],[],[26],[269,270],"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":272,"openalex_id":274,"authors":275,"venue":235,"cited_by_count":87,"oa_url":282,"card":283,"direction":181,"ingested_from":183},"W7221522859",[276,279],{"name":277,"orcid":278},"B Johnson","https:\u002F\u002Forcid.org\u002F0009-0009-8281-4051",{"name":280,"orcid":281},"Chandrakumar Thangavel","https:\u002F\u002Forcid.org\u002F0000-0002-1186-5988","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5486058",{"tldr":284,"method":285,"finding":286,"direction":92,"opportunity":287},"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":290,"title":291,"url":292,"summary":293,"summary_zh":294,"content":8,"source_name":295,"source_url":292,"published_at":296,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":297,"score_detail":298,"sources":301,"tags":303,"primary_topic":305,"secondary_topics":307,"classification":309,"search_phrases":327,"slug":330,"view_count":87,"doi":331,"paper":332,"created_at":346},4759,"Machine Learning-based Prediction of SVI Parameter in Maize: A Comparative Study and Feature Importance Analysis","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i10.2870-2879.9043","Background: Accurate estimation of seedling vigor indices (SVI) is critical for monitoring plant health and predicting yields in precision agriculture. This study comparatively evaluates the performance of different machine learning algorithms in estimating the SVI parameter. Methods: Germination data were collected from 39 maize genotypes under three different salinity conditions (0 mM, 100 mM, and 200 mM NaCl), including the parameters Gmax, MGT, GI, SL, RL, SdL, SMR, RMR, and SVI. The dataset was preprocessed through normalization to the [0–1] range. The SVI parameter was estimated using four different machine learning methods: Random Forest, CatBoost, LightGBM, and Support Vector Regression (SVR). To evaluate the performance of the models, both 10-fold cross-validation and 80%–20% hold-out validation techniques were employed. The model performance was examined using RMSE, MSE, MAE, and R² metrics. Results: The findings show that all models have a high explanatory power in the training dataset. Specifically, the CatBoost model showed the highest success in the training phase (R²=0.9962), whereas the SVR model achieved the highest generalization performance on the test dataset (R²=0.9712, RMSE=0.0582). Feature importance analysis results revealed that the 'App' parameter had the highest impact on the target variable in most models, but different structural interactions were prominent in the LightGBM model. The results demonstrate that machine learning techniques provide high accuracy in SVI estimation and that the tendency towards overfitting is limited. Conclusions: In conclusion, this study confirms that machine learning techniques are powerful and reliable tools for estimating spectral indices in precision agriculture applications. The integration of SVR and ensemble-based models into agricultural decision support systems is expected to significantly contribute to optimizing resource use and improving yield predictions.","背景：准确估计幼苗活力指数（SVI）对于精准农业中监测植物健康和预测产量至关重要。本研究比较评估了不同机器学习算法在估计SVI参数方面的性能。方法：从39个玉米基因型在三种不同盐度条件（0 mM、100 mM和200 mM NaCl）下收集发芽数据，包括Gmax、MGT、GI、SL、RL、SdL、SMR、RMR和SVI等参数。数据集通过归一化至[0–1]范围进行预处理。采用四种不同的机器学习方法估计SVI参数：随机森林、CatBoost、LightGBM和支持向量回归（SVR）。为评估模型性能，采用了10折交叉验证和80%–20%留出验证技术。使用RMSE、MSE、MAE和R²指标检验模型性能。结果：研究结果表明，所有模型在训练数据集中均具有较高的解释力。具体而言，CatBoost模型在训练阶段表现出最高的成功率（R²=0.9962），而SVR模型在测试数据集上取得了最高的泛化性能（R²=0.9712，RMSE=0.0582）。特征重要性分析结果显示，在大多数模型中，“App”参数对目标变量的影响最大，但在LightGBM模型中不同的结构交互作用更为突出。结果表明，机器学习技术在SVI估计中提供了高精度，且过拟合倾向有限。结论：总之，本研究证实机器学习技术是精准农业应用中估计光谱指数的强大而可靠的工具。将SVR和基于集成的模型整合到农业决策支持系统中，预计将显著有助于优化资源利用和改善产量预测。","Turkish Journal of Agriculture - Food Science and Technology","2026-10-11T00:00:00Z",63,{"impact":110,"substance":18,"depth":299,"authority":46,"freshness":240,"relevant":21,"comment":300},16,"方法对比扎实、结论可靠，但属细分领域论文，产业影响有限，适合主题聚合而非头条精选。",[302],{"name":295,"url":292},[26,27,131,30,304],"耐盐育种",{"code":59,"slug":149,"name":30,"path":306,"parent_code":250},"\u002Ftopic\u002Ffield-crops\u002Fmaize",[308],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":13,"version":41,"method":42,"evidence":310},{"candidates":311,"agricultural_evidence":47},[312,317,321],{"code":33,"score":313,"supported":47,"title":314,"abstract":315,"tags":316},19,[129],[131,129,132],[27,131],{"code":59,"score":135,"supported":47,"title":318,"abstract":319,"tags":320},[149],[30,149],[30],{"code":76,"score":141,"supported":47,"title":322,"abstract":323,"tags":326},[],[324,325],"精准农业","precision agriculture",[26],[328,329],"玉米 SVI 机器学习","玉米 耐盐 幼苗活力指数","玉米SVI机器学习-4759","10.24925\u002Fturjaf.v14i10.2870-2879.9043",{"doi":331,"openalex_id":333,"authors":334,"venue":295,"cited_by_count":87,"oa_url":292,"card":341,"direction":92,"ingested_from":183},"W7221462188",[335,338],{"name":336,"orcid":337},"Mustafa Turan Arslan","https:\u002F\u002Forcid.org\u002F0000-0001-5498-6571",{"name":339,"orcid":340},"Gülay Zülkadir","https:\u002F\u002Forcid.org\u002F0000-0003-3488-4011",{"tldr":342,"method":343,"finding":344,"direction":92,"opportunity":345},"用四种机器学习模型基于39个玉米基因型盐胁迫发芽数据预测幼苗活力指数SVI。","39个玉米基因型在三种盐浓度下的发芽数据，归一化后用RF、CatBoost、Li","SVR泛化最优（R²=0.9712），CatBoost训练拟合最高；App参数对SVI影响最大。","可扩展到多作物多胁迫环境，结合光谱或图像表型数据提升SVI预测的田间适用性。","2026-10-12T23:30:44.762497Z",{"id":348,"title":349,"url":350,"summary":351,"summary_zh":8,"content":8,"source_name":352,"source_url":350,"published_at":353,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":354,"score_detail":355,"sources":357,"tags":359,"primary_topic":362,"secondary_topics":363,"classification":370,"search_phrases":391,"slug":394,"view_count":21,"doi":395,"paper":396,"created_at":429},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":18,"substance":108,"depth":18,"authority":12,"freshness":110,"relevant":21,"comment":356},"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。",[358],{"name":352,"url":350},[26,27,28,360,361],"遥感","病害检测",{"code":53,"slug":248,"name":28,"path":249,"parent_code":250},[364,365],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":366,"slug":367,"name":368,"path":369,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":13,"version":41,"method":42,"evidence":371},{"candidates":372,"agricultural_evidence":47},[373,377,382,387],{"code":53,"score":46,"supported":47,"title":374,"abstract":375,"tags":376},[248],[248],[28],{"code":33,"score":46,"supported":47,"title":378,"abstract":380,"tags":381},[379],"deep learning",[379],[27],{"code":366,"score":46,"supported":47,"title":383,"abstract":385,"tags":386},[384],"remote sensing",[384],[360],{"code":76,"score":21,"supported":13,"title":388,"abstract":389,"tags":390},[],[],[26],[392,393],"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":395,"openalex_id":397,"authors":398,"venue":352,"cited_by_count":87,"oa_url":350,"card":8,"direction":428,"ingested_from":183},"W7221055917",[399,402,405,407,409,412,414,416,419,421,423,425],{"name":400,"orcid":401},"Carlito B. Balingbing","https:\u002F\u002Forcid.org\u002F0000-0002-3672-9253",{"name":403,"orcid":404},"Dmytro Chebotarov","https:\u002F\u002Forcid.org\u002F0000-0003-1351-9453",{"name":406,"orcid":8},"Ian  Carlo Perilla",{"name":408,"orcid":8},"Stephen Klassen",{"name":410,"orcid":411},"Nancy Castilla","https:\u002F\u002Forcid.org\u002F0000-0002-9143-4933",{"name":413,"orcid":8},"Raphael  Gabriel Carvajal",{"name":415,"orcid":8},"Kendrix Ortega",{"name":417,"orcid":418},"Joseph M. Sandro","https:\u002F\u002Forcid.org\u002F0000-0002-8311-2299",{"name":420,"orcid":8},"Nurmi Pangesti",{"name":422,"orcid":8},"Caesar Arloo R. Centeno",{"name":424,"orcid":8},"Arnel Rala",{"name":426,"orcid":427},"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":431,"title":432,"url":433,"summary":434,"summary_zh":435,"content":8,"source_name":436,"source_url":433,"published_at":437,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":438,"score_detail":439,"sources":441,"tags":443,"primary_topic":446,"secondary_topics":447,"classification":449,"search_phrases":469,"slug":472,"view_count":473,"doi":474,"paper":475,"created_at":488},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":18,"substance":16,"depth":18,"authority":19,"freshness":240,"relevant":21,"comment":440},"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。",[442],{"name":436,"url":433},[26,27,28,444,445],"遥感监测","作物长势",{"code":53,"slug":248,"name":28,"path":249,"parent_code":250},[448],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":13,"version":41,"method":42,"evidence":450},{"candidates":451,"agricultural_evidence":47},[452,457,461,465],{"code":53,"score":18,"supported":47,"title":453,"abstract":454,"tags":456},[248],[28,455,248],"稻田",[28],{"code":33,"score":135,"supported":47,"title":458,"abstract":459,"tags":460},[379],[49,379],[27],{"code":366,"score":141,"supported":47,"title":462,"abstract":463,"tags":464},[],[360,384],[444],{"code":76,"score":21,"supported":13,"title":466,"abstract":467,"tags":468},[],[],[26],[470,471],"Sentinel-2 水稻 遥感监测","Swin Transformer 作物健康","Sentinel-2水稻遥感监测-4431",2,"10.1038\u002Fs41598-026-74895-5",{"doi":474,"openalex_id":476,"authors":477,"venue":436,"cited_by_count":87,"oa_url":433,"card":483,"direction":428,"ingested_from":183},"W7220838898",[478,481],{"name":479,"orcid":480},"Shashank Gupta","https:\u002F\u002Forcid.org\u002F0000-0003-1291-7951",{"name":482,"orcid":8},"Shefali Arora Chouhan",{"tldr":484,"method":485,"finding":486,"direction":428,"opportunity":487},"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"]