[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3807":3,"related-3807":93},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"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":72,"slug":75,"view_count":76,"doi":77,"paper":78,"created_at":92},3807,"Reliability Auditing and Capacity-Constrained Review for Wheat-Yield Decision Support under Temporal and Spatial Shift","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10698325\u002Fv1","Reliability Auditing and Capacity-Constrained Review for Wheat-Yield Decision Support under Temporal and Spatial Shift。Research Square",null,"Research Square","2026-09-28T00:00:00Z","论文",10,false,52,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},8,14,15,6,9,1,"预印本论文，聚焦小麦产量决策支持系统的可靠性审计与时空迁移问题，方法有一定新意但尚未经同行评审，公共影响有限。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","决策支持","遥感监测","小麦产量",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","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},[50,57,62,67],{"code":39,"score":51,"supported":52,"title":53,"abstract":55,"tags":56},13,true,[54],"decision support",[54],[27,28],{"code":32,"score":58,"supported":52,"title":59,"abstract":60,"tags":61},11,[33],[33],[],{"code":63,"score":21,"supported":13,"title":64,"abstract":65,"tags":66},"I1",[],[],[26],{"code":68,"score":21,"supported":13,"title":69,"abstract":70,"tags":71},"I3",[],[],[29],[73,74],"小麦产量 决策支持 遥感","Research Square 小麦 产量预测","小麦产量决策支持遥感-3807",3,"10.21203\u002Frs.3.rs-10698325\u002Fv1",{"doi":77,"openalex_id":79,"authors":80,"venue":9,"cited_by_count":89,"oa_url":6,"card":8,"direction":90,"ingested_from":91},"W7214624863",[81,84,87],{"name":82,"orcid":83},"Xin Li","https:\u002F\u002Forcid.org\u002F0009-0005-0670-5006",{"name":85,"orcid":86},"Bojian Guo","https:\u002F\u002Forcid.org\u002F0009-0005-8353-2436",{"name":88,"orcid":8},"Asel Kartanova",0,"农业人工智能与决策模型","openalex","2026-09-29T23:31:02.997580Z",{"total":19,"page":21,"page_size":19,"items":94},[95,187,273,344,404,477],{"id":96,"title":97,"url":98,"summary":99,"summary_zh":100,"content":8,"source_name":101,"source_url":98,"published_at":102,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":103,"score_detail":104,"sources":109,"tags":111,"primary_topic":114,"secondary_topics":115,"classification":117,"search_phrases":149,"slug":152,"view_count":153,"doi":154,"paper":155,"created_at":186},2670,"A knowledge-guided machine learning framework for cross-scale wheat harvest monitoring via sample augmentation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115671","Accurate monitoring of wheat harvest is crucial for precision agriculture and ensuring food security. However, rapid changes in land surface composition during the harvest period in intensive agricultural regions make it difficult to obtain sufficiently high-confidence ground samples, limiting the performance and generalization of data-driven remote sensing methods. Therefore, this study proposes a Knowledge-Guided Machine Learning (KGML) framework that integrates multi-satellite Earth observation data (PlanetScope, Sentinel-2, and MODIS) to monitor harvest from the field to regional scales. Ground data were collected using vehicle-mounted cameras and smartphones during the 2023 and 2024 wheat harvest periods. The results showed that combining spectral knowledge rules with a Random Forest model (regional accuracy >0.80) generated numerous high-confidence augmented samples from PlanetScope imagery. The augmented dataset was used to train a Hybrid CNN-Transformer-LSTM (HCTL) model with two pathways: Sentinel-2 classification for field-level harvest mapping (overall accuracy = 0.93) and MODIS regression for sub-pixel harvest fraction estimation, which showed high agreement with PlanetScope-derived harvest fractions (R 2 = 0.97, RMSE = 0.07, rRMSE = 0.15). The harvest dates derived from the MODIS harvest fraction time series showed high consistency with field observations (R 2 = 0.82, RMSE = 1.30 days). This framework provides an effective solution for wheat harvest monitoring by bridging the gap between limited ground-truth data and multi-scale satellite observations, thereby supporting food security assessments and informed agricultural management decisions.","准确监测小麦收获对精准农业和保障粮食安全至关重要。然而，在集约化农业区域，收获期地表组成的快速变化使得获取足够高置信度的地面样本变得困难，限制了数据驱动遥感方法的性能和泛化能力。因此，本研究提出了一种知识引导机器学习（KGML）框架，集成多卫星地球观测数据（PlanetScope、Sentinel-2和MODIS），实现从田块到区域尺度的收获监测。地面数据通过车载摄像头和智能手机在2023年和2024年小麦收获期采集。结果表明，将光谱知识规则与随机森林模型相结合（区域精度>0.80），可从PlanetScope影像中生成大量高置信度增强样本。利用该增强数据集训练了混合CNN-Transformer-LSTM（HCTL）模型，该模型包含两条路径：Sentinel-2分类用于田块尺度收获制图（总体精度=0.93），MODIS回归用于亚像元收获比例估算，其结果与PlanetScope-derived收获比例高度一致（R²=0.97，RMSE=0.07，rRMSE=0.15）。由MODIS收获比例时间序列提取的收获日期与田间观测结果高度一致（R²=0.82，RMSE=1.30天）。该框架通过弥合有限地面真值数据与多尺度卫星观测之间的差距，为小麦收获监测提供了有效解决方案，从而支持粮食安全评估和农业管理决策。","Remote Sensing of Environment","2026-09-15T00:00:00Z",87,{"impact":105,"substance":106,"depth":107,"authority":18,"freshness":20,"relevant":21,"comment":108},22,23,18,"提出知识引导机器学习框架，融合多源卫星数据实现田块到区域尺度的跨尺度小麦收获监测，方法新颖、精度可靠，对精准农业与粮食安全评估有实质参考价值。",[110],{"name":101,"url":98},[26,27,112,29,113],"粮食安全","小麦收获",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[116],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":13,"version":46,"method":47,"evidence":118},{"candidates":119,"agricultural_evidence":52},[120,126,130,137,143],{"code":39,"score":18,"supported":52,"title":121,"abstract":123,"tags":125},[122],"machine learning",[124,122],"机器学习",[27],{"code":32,"score":17,"supported":52,"title":127,"abstract":128,"tags":129},[33],[34,33],[],{"code":131,"score":132,"supported":52,"title":133,"abstract":134,"tags":136},"A8",7,[],[112,135],"food security",[112],{"code":63,"score":132,"supported":52,"title":138,"abstract":139,"tags":142},[],[140,141],"精准农业","precision agriculture",[26],{"code":68,"score":132,"supported":52,"title":144,"abstract":145,"tags":148},[],[146,147],"遥感","remote sensing",[29],[150,151],"农业人工智能 小麦收获 智慧农业 粮食安全","农业人工智能 小麦收获","农业人工智能小麦收获智慧农业粮食安全-2670",4,"10.1016\u002Fj.rse.2026.115671",{"doi":154,"openalex_id":156,"authors":157,"venue":101,"cited_by_count":89,"oa_url":98,"card":180,"direction":184,"ingested_from":91},"W7213296259",[158,161,163,165,167,169,171,173,176,178],{"name":159,"orcid":160},"Mingchao Shao","https:\u002F\u002Forcid.org\u002F0000-0003-2619-4272",{"name":162,"orcid":8},"Chongya Jiang",{"name":164,"orcid":8},"Jingwei An",{"name":166,"orcid":8},"Haokai Zhu",{"name":168,"orcid":8},"Yue Li",{"name":170,"orcid":8},"Xia Yao",{"name":172,"orcid":8},"Tao Cheng",{"name":174,"orcid":175},"Hengbiao Zheng","https:\u002F\u002Forcid.org\u002F0009-0008-4778-0450",{"name":177,"orcid":8},"Weixing Cao",{"name":179,"orcid":8},"Yan Zhu",{"tldr":181,"method":182,"finding":183,"direction":184,"opportunity":185},"提出知识引导机器学习框架，用样本增强实现田块到区域尺度的冬小麦收获监测。","融合PlanetScope、Sentinel-2、MODIS与车载相机地面数据，","增强样本训练的HCTL模型田块分类精度0.93，区域收获比例R²=0.97，收获日期误差约1.3天。","农业遥感与作物表型","可迁移至其他作物收获监测，并探索知识规则自动化构建与跨区域泛化能力。","2026-09-16T23:30:30.474537Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":8,"source_name":193,"source_url":190,"published_at":194,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":195,"score_detail":196,"sources":201,"tags":203,"primary_topic":206,"secondary_topics":207,"classification":215,"search_phrases":249,"slug":252,"view_count":89,"doi":253,"paper":254,"created_at":272},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z",77,{"impact":197,"substance":198,"depth":199,"authority":51,"freshness":12,"relevant":21,"comment":200},16,21,17,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[202],{"name":193,"url":190},[26,124,29,204,205],"土壤墒情","冬小麦",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[208,214],{"code":209,"slug":210,"name":211,"path":212,"parent_code":213},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":13,"version":46,"method":47,"evidence":216},{"candidates":217,"agricultural_evidence":52},[218,224,229,233,238,245],{"code":32,"score":219,"supported":52,"title":220,"abstract":222,"tags":223},26,[221,33],"winter wheat",[34,221,33],[205],{"code":209,"score":18,"supported":52,"title":225,"abstract":226,"tags":228},[210],[227,210],"土壤",[204],{"code":39,"score":18,"supported":52,"title":230,"abstract":231,"tags":232},[122],[124,122],[124],{"code":68,"score":12,"supported":52,"title":234,"abstract":235,"tags":237},[],[146,147,236],"multispectral",[29],{"code":239,"score":19,"supported":52,"title":240,"abstract":241,"tags":244},"H3",[],[242,243],"灌溉","irrigation",[],{"code":63,"score":153,"supported":52,"title":246,"abstract":247,"tags":248},[],[26],[26],[250,251],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":253,"openalex_id":255,"authors":256,"venue":193,"cited_by_count":89,"oa_url":190,"card":266,"direction":271,"ingested_from":91},"W7223156204",[257,260,262,264],{"name":258,"orcid":259},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":261,"orcid":8},"Yi Zhang",{"name":263,"orcid":8},"WEI Lei",{"name":265,"orcid":8},"Chengcai Zhang",{"tldr":267,"method":268,"finding":269,"direction":184,"opportunity":270},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.887301Z",{"id":274,"title":275,"url":276,"summary":277,"summary_zh":278,"content":8,"source_name":279,"source_url":276,"published_at":280,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":281,"score_detail":282,"sources":284,"tags":286,"primary_topic":290,"secondary_topics":291,"classification":293,"search_phrases":311,"slug":314,"view_count":21,"doi":315,"paper":316,"created_at":343},4806,"A study on the identification of wheat powdery mildew using chlorophyll fluorescence imaging","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1837843","Introduction Wheat powdery mildew poses a serious threat to global wheat production, and its accurate identification is key to smart agriculture. Methods This study combines chlorophyll fluorescence imaging with deep learning algorithms to investigate the temporal dynamics of key fluorescence parameters (such as maximum photochemical efficiency, QYmax) during the progression of powderymildew. A QYmax-based chlorophyll fluorescence image dataset for wheat powdery mildew was constructed. Building on this, the LCRNet model was proposed, which effectively processes fluorescence image data through a collaborative mechanism integrating the Large Selective Adaptive Fusion (LSAF) and Coordinated Attention (CA) modules. Results On an independent test set, the model achieved an accuracy of 94% and an F1 score of 94.1%, outperforming comparison models. Discussion This study proposes a method for classifying the severity of powdery mildew in wheat that reduces the computational cost of fluorescence data processing, providing important methodological support and technical insights for advancing the intelligent, precise, and green transformation of crop disease prevention and control systems.","引言 小麦白粉病对全球小麦生产构成严重威胁，其准确识别是实现智慧农业的关键。方法 本研究将叶绿素荧光成像与深度学习算法相结合，探究白粉病发展过程中关键荧光参数（如最大光化学效率QYmax）的时间动态变化，构建了基于QYmax的小麦白粉病叶绿素荧光图像数据集。在此基础上，提出了LCRNet模型，该模型通过融合大选择性自适应融合（LSAF）模块与协调注意力（CA）模块的协同机制，有效处理荧光图像数据。结果 在独立测试集上，该模型达到了94%的准确率和94.1%的F1分数，优于对比模型。讨论 本研究提出了一种小麦白粉病严重程度分类方法，降低了荧光数据处理的计算成本，为推动作物病害防控体系向智能化、精准化和绿色化转型提供了重要的方法学支撑和技术启示。","Frontiers in Plant Science","2026-10-12T00:00:00Z",81,{"impact":107,"substance":105,"depth":107,"authority":17,"freshness":20,"relevant":21,"comment":283},"将叶绿素荧光成像与深度学习结合用于小麦白粉病严重度识别，方法新颖、数据可靠，对智慧植保有实质参考价值。",[285],{"name":279,"url":276},[26,27,287,288,289],"病害识别","叶绿素荧光","小麦白粉病",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[292],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":13,"version":46,"method":47,"evidence":294},{"candidates":295,"agricultural_evidence":52},[296,300,306],{"code":32,"score":17,"supported":52,"title":297,"abstract":298,"tags":299},[33],[34,33],[],{"code":39,"score":16,"supported":52,"title":301,"abstract":302,"tags":305},[],[303,304],"深度学习","deep learning",[27,287],{"code":63,"score":132,"supported":52,"title":307,"abstract":308,"tags":310},[],[26,309],"smart agriculture",[26],[312,313],"小麦白粉病 叶绿素荧光 深度学习","LCRNet 小麦病害识别","小麦白粉病叶绿素荧光深度学习-4806","10.3389\u002Ffpls.2026.1837843",{"doi":315,"openalex_id":317,"authors":318,"venue":279,"cited_by_count":89,"oa_url":337,"card":338,"direction":271,"ingested_from":91},"W7221549180",[319,321,324,327,329,331,333,335],{"name":320,"orcid":8},"Yijia Chen",{"name":322,"orcid":323},"Chengyuan Hao","https:\u002F\u002Forcid.org\u002F0000-0002-6002-3963",{"name":325,"orcid":326},"Zhiyu Li","https:\u002F\u002Forcid.org\u002F0000-0003-3771-0199",{"name":328,"orcid":8},"Xupeng An",{"name":330,"orcid":8},"Jingxiang Huang",{"name":332,"orcid":8},"Xinchang Chen",{"name":334,"orcid":8},"Shidong Wang",{"name":336,"orcid":8},"ZhiChao Chen","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1837843\u002Fpdf",{"tldr":339,"method":340,"finding":341,"direction":90,"opportunity":342},"该研究利用叶绿素荧光成像与深度学习识别小麦白粉病并分级。","构建QYmax荧光图像数据集，提出融合LSAF与CA模块的LCRNet模型。","模型在独立测试集上准确率达94%、F1为94.1%，优于对比模型。","可探索多病害、多作物荧光图像通用模型，并融合田间时序数据实现早期预警。","2026-10-13T23:30:09.060156Z",{"id":345,"title":346,"url":347,"summary":348,"summary_zh":8,"content":8,"source_name":349,"source_url":8,"published_at":102,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":350,"score_detail":351,"sources":353,"tags":355,"primary_topic":358,"secondary_topics":359,"classification":365,"search_phrases":390,"slug":393,"view_count":394,"doi":8,"paper":395,"created_at":403},2854,"面向冬小麦水分含量的无人机遥感自动机器学习预测——MDPI Remote Sensing","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3161","本研究探索了无人机遥感快速准确评估冬小麦水分含量的潜力。在开花期和灌浆期使用配备多光谱、RGB和热红外相机的无人机获取高分辨率冠层遥感图像。集成地面真值采样数据与无人机遥感数据,使用自动机器学习(AutoML)框架建立回归模型预测冬小麦水分含量(MC)。结果表明,MC预测在灌浆期表现最佳,TIR传感器精度最高(R²=0.812,MAE=0.0204,RMSE=0.0274)。多传感器融合相比单传感器方法进一步提升预测性能,MC预测的R²达0.876、MAE 0.0191、RMSE 0.0259。来自中国农业科学院农田灌溉研究所。","MDPI Remote Sensing",74,{"impact":18,"substance":198,"depth":199,"authority":51,"freshness":16,"relevant":21,"comment":352},"中国农科院团队用AutoML融合多光谱、RGB与热红外无人机数据预测冬小麦水分含量，多传感器融合R²达0.876，方法新颖、结论可靠，对精准灌溉有实用价值，值得进入每日精选。",[354],{"name":349,"url":347},[26,27,356,205,357],"无人机遥感","多传感器融合",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[360,364],{"code":68,"slug":361,"name":362,"path":363,"parent_code":43},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":13,"version":46,"method":47,"evidence":366},{"candidates":367,"agricultural_evidence":52},[368,373,378,382,386],{"code":68,"score":369,"supported":52,"title":370,"abstract":371,"tags":372},20,[146,147],[146],[356],{"code":32,"score":374,"supported":52,"title":375,"abstract":376,"tags":377},12,[34],[34],[205],{"code":39,"score":374,"supported":52,"title":379,"abstract":380,"tags":381},[124],[124],[27],{"code":239,"score":76,"supported":52,"title":383,"abstract":384,"tags":385},[],[242],[],{"code":63,"score":21,"supported":13,"title":387,"abstract":388,"tags":389},[],[],[26],[391,392],"农业人工智能 多传感器融合 无人机遥感 智慧农业","农业人工智能 多传感器融合","农业人工智能多传感器融合无人机遥感智慧农业-2854",2,{"doi":8,"openalex_id":8,"authors":396,"venue":8,"cited_by_count":89,"oa_url":8,"card":397,"direction":184,"ingested_from":402},[],{"tldr":398,"method":399,"finding":400,"direction":184,"opportunity":401},"用无人机多传感器遥感结合AutoML预测冬小麦水分含量。","无人机多光谱、RGB、热红外图像+地面真值，AutoML回归建模。","灌浆期热红外精度最高，多传感器融合将R²提升至0.876。","可探索不同生育期与品种的泛化性，及将水分预测接入灌溉决策系统。","agent","2026-09-18T00:03:30.758419Z",{"id":405,"title":406,"url":407,"summary":408,"summary_zh":409,"content":8,"source_name":410,"source_url":407,"published_at":102,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":411,"score_detail":412,"sources":414,"tags":416,"primary_topic":418,"secondary_topics":419,"classification":422,"search_phrases":447,"slug":450,"view_count":76,"doi":451,"paper":452,"created_at":476},2666,"Automated Machine Learning-Driven UAV Remote Sensing for Accurate Winter Wheat Water Content Prediction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183161","Crop water content is a critical indicator of crop growth status, and its efficient and accurate monitoring is essential for agricultural water resource management. Conventional methods for monitoring winter wheat water content, however, rely mainly on destructive sampling and are labor-intensive and time-consuming. To address these limitations, this study explored the potential of unmanned aerial vehicle (UAV) remote sensing for the rapid and accurate assessment of winter wheat water content. High-resolution canopy remote sensing images were acquired using UAVs equipped with multispectral (MS), RGB, and thermal infrared (TIR) cameras during the flowering and filling stages under six irrigation treatments. Ground-truth sampling data were integrated with the UAV-derived remote sensing data, and an automated machine learning (AutoML) framework—which automatically searches over a range of candidate algorithms and hyperparameters to select the optimal model—was employed to establish regression models for predicting winter wheat moisture content (MC). All models were evaluated using five-fold cross-validation. The results demonstrated that MC prediction performed best during the filling stage, with the TIR sensor achieving the highest accuracy (R2 = 0.812, MAE = 0.0204, RMSE = 0.0274). Compared with single-sensor approaches, multi-sensor fusion further improved predictive performance, achieving an R2 of 0.876, an MAE of 0.0191, and an RMSE of 0.0259 for MC prediction. These findings indicate that UAV-based multi-sensor remote sensing provides an effective means of monitoring winter wheat water content, facilitating timely assessment of crop growth status and optimized irrigation management. Moreover, the use of AutoML enables high-accuracy prediction with minimal human intervention, enhancing the precision of crop water monitoring and advancing precision agriculture.","作物含水量是反映作物生长状况的关键指标，对其进行高效、准确的监测对农业水资源管理至关重要。然而，传统冬小麦含水量监测方法主要依赖破坏性采样，费时费力。为解决这些局限，本研究探索了无人机（UAV）遥感在快速准确评估冬小麦含水量方面的潜力。在六种灌溉处理下，利用搭载多光谱（MS）、RGB和热红外（TIR）相机的无人机在开花期和灌浆期获取了高分辨率冠层遥感图像。将地面实测采样数据与无人机遥感数据相结合，采用自动化机器学习（AutoML）框架——该框架可在一系列候选算法和超参数中自动搜索以选择最优模型——建立预测冬小麦含水量（MC）的回归模型。所有模型均采用五折交叉验证进行评估。结果表明，灌浆期MC预测表现最佳，其中TIR传感器精度最高（R2 = 0.812，MAE = 0.0204，RMSE = 0.0274）。与单传感器方法相比，多传感器融合进一步提升了预测性能，MC预测的R2达到0.876，MAE为0.0191，RMSE为0.0259。这些发现表明，基于无人机的多传感器遥感为监测冬小麦含水量提供了有效手段，有助于及时评估作物生长状况并优化灌溉管理。此外，AutoML的使用使得在最少人工干预下实现高精度预测成为可能，提升了作物水分监测的精度，推动了精准农业发展。","Remote Sensing",80,{"impact":107,"substance":105,"depth":107,"authority":17,"freshness":16,"relevant":21,"comment":413},"AutoML结合无人机多传感器遥感预测冬小麦含水量，方法新颖、数据扎实，对精准灌溉有实用价值，值得进入每日精选。",[415],{"name":410,"url":407},[26,27,34,146,417],"精准灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[420,421],{"code":68,"slug":361,"name":362,"path":363,"parent_code":43},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":13,"version":46,"method":47,"evidence":423},{"candidates":424,"agricultural_evidence":52},[425,429,433,437,443],{"code":32,"score":219,"supported":52,"title":426,"abstract":427,"tags":428},[221,33],[34,221,33],[34],{"code":68,"score":107,"supported":52,"title":430,"abstract":431,"tags":432},[147],[146,147,236],[146],{"code":39,"score":18,"supported":52,"title":434,"abstract":435,"tags":436},[122],[124,122],[27],{"code":239,"score":12,"supported":52,"title":438,"abstract":439,"tags":442},[],[242,440,243,441],"水资源","water resource",[417],{"code":63,"score":132,"supported":52,"title":444,"abstract":445,"tags":446},[],[140,141],[26],[448,449],"农业人工智能 智慧农业 精准灌溉 小麦","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉小麦-2666","10.3390\u002Frs18183161",{"doi":451,"openalex_id":453,"authors":454,"venue":410,"cited_by_count":89,"oa_url":407,"card":471,"direction":184,"ingested_from":91},"W7213246708",[455,458,460,462,465,468],{"name":456,"orcid":457},"Fan Ding","https:\u002F\u002Forcid.org\u002F0000-0001-5482-8290",{"name":459,"orcid":8},"Qian Cheng",{"name":461,"orcid":8},"Fuyi Duan",{"name":463,"orcid":464},"Shuaipeng Fei","https:\u002F\u002Forcid.org\u002F0000-0002-8774-7929",{"name":466,"orcid":467},"Junjie Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8900-2691",{"name":469,"orcid":470},"Zhen Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2847-0042",{"tldr":472,"method":473,"finding":474,"direction":184,"opportunity":475},"用无人机多光谱、RGB和热红外遥感结合AutoML预测冬小麦含水量。","无人机多传感器影像与地面采样，AutoML自动选模型，五折交叉验证。","灌浆期热红外精度最高R²=0.812，多传感器融合提升至R²=0.876。","可探索AutoML与多时相\u002F多源卫星遥感融合，实现区域尺度作物水分精准监测。","2026-09-16T23:30:29.163192Z",{"id":478,"title":479,"url":480,"summary":481,"summary_zh":482,"content":8,"source_name":483,"source_url":480,"published_at":484,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":485,"score_detail":486,"sources":488,"tags":490,"primary_topic":493,"secondary_topics":494,"classification":496,"search_phrases":513,"slug":516,"view_count":76,"doi":517,"paper":518,"created_at":546},2523,"Deep learning-based detection and counting of wheat seeds: Comparative benchmarking of YOLO models","https:\u002F\u002Fdoi.org\u002F10.56612\u002Fijaaeb.v6i1.249","Automated wheat-seed detection and counting play important roles in high-throughput plant phenotyping, seed characterization, and agricultural research. Conventional manual counting is labor-intensive, time-consuming, and susceptible to human error when processing large numbers of seed samples. Recent advances in deep learning and object detection provide opportunities to automate these tasks using conventional RGB images. This study focused on a deep learning-based framework for wheat-seed detection and detection-based counting using a custom red-green-blue (RGB) image dataset. A dataset comprising 832 RGB images containing 3,649 manually annotated wheat-seed instances was developed, with images representing one to ten detached wheat seeds per image. All seed instances were manually annotated using bounding boxes and formulated as a single-class object detection problem. Two lightweight object detection models, YOLOv8n and YOLO11n, were trained and evaluated under identical experimental conditions. The model performance was assessed using precision, recall, mean Average Precision at an Intersection over Union (IoU) threshold of 0.5 (mAP@0.5), mean Average Precision averaged across IoU thresholds from 0.5 to 0.95 (mAP@0.5:0.95), training loss curves, confidence-based performance curves, confusion matrices, and qualitative detection outputs. Both models achieved excellent detection performance on the custom wheat seed dataset. YOLOv8n achieved a precision of 0.9947, recall of 0.9963, mAP@0.5 of 0.9940, and mAP@0.5:0.95 of 0.5284. YOLO11n produced slightly higher performance, achieving a precision of 0.9988, recall of 0.9984, mAP@0.5 of 0.9950, and mAP@0.5:0.95 of 0.5385. Overall, the performance of the two models was similar at mAP@0.5, but at mAP@0.5:0.95 stricter criterion, YOLO11n consistently demonstrated the strongest overall performance. The findings show that lightweight YOLO models combined with RGB imaging provide an effective and easy way for automated localization and counting of wheat seeds. ​The framework provides a manually annotated RGB wheat-seed dataset and a reproducible benchmark to compare lightweight YOLO models for detection. This study provides a practical foundation for future research on automated seed phenotyping, with future work focusing on external validation using more diverse datasets, multi-class seed-quality assessment, and quantitative evaluation of counting performance.","自动化小麦种子检测与计数在高通量植物表型分析、种子表征和农业研究中发挥着重要作用。传统的人工计数在处理大量种子样本时劳动强度大、耗时长且易受人为误差影响。近年来深度学习和目标检测的进展为利用常规RGB图像实现这些任务的自动化提供了机遇。本研究聚焦于基于深度学习的框架，利用自定义红绿蓝（RGB）图像数据集进行小麦种子检测及基于检测的计数。构建了一个包含832张RGB图像、3，649个手动标注小麦种子实例的数据集，图像中每张包含一到十粒脱离的小麦种子。所有种子实例均使用边界框进行手动标注，并形式化为单类目标检测问题。在相同实验条件下训练和评估了两种轻量级目标检测模型YOLOv8n和YOLO11n。采用精确率、召回率、交并比（IoU）阈值为0.5时的平均精度均值（mAP@0.5）、IoU阈值从0.5到0.95的平均精度均值（mAP@0.5:0.95）、训练损失曲线、基于置信度的性能曲线、混淆矩阵以及定性检测输出对模型性能进行了评估。两种模型在自定义小麦种子数据集上均取得了优异的检测性能。YOLOv8n的精确率为0.9947，召回率为0.9963，mAP@0.5为0.9940，mAP@0.5:0.95为0.5284。YOLO11n的性能略高，精确率为0.9988，召回率为0.9984，mAP@0.5为0.9950，mAP@0.5:0.95为0.5385。总体而言，两种模型在mAP@0.5上的性能相似，但在mAP@0.5:0.95这一更严格的指标下，YOLO11n始终展现出最强的整体性能。研究结果表明，轻量级YOLO模型结合RGB成像为小麦种子的自动化定位和计数提供了一种有效且简便的方法。该框架提供了一个手动标注的RGB小麦种子数据集和一个可重复的基准，用于比较轻量级YOLO模型的检测性能。本研究为未来自动化种子表型分析研究提供了实用基础，未来工作将侧重于使用更多样化数据集进行外部验证、多类种子质量评估以及计数性能的定量评价。","International Journal of Applied and Experimental Biology","2026-09-14T00:00:00Z",73,{"impact":18,"substance":369,"depth":199,"authority":51,"freshness":16,"relevant":21,"comment":487},"基于自建RGB数据集对YOLOv8n与YOLO11n进行小麦种子检测计数对比，方法清晰、指标完整，属细分领域可复现基准研究，对自动化种子表型有实用参考价值。",[489],{"name":483,"url":480},[26,27,34,491,492],"高通量表型","种子检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[495],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":13,"version":46,"method":47,"evidence":497},{"candidates":498,"agricultural_evidence":52},[499,505,509],{"code":39,"score":219,"supported":52,"title":500,"abstract":502,"tags":504},[304,501],"YOLO",[503,303,304,501],"目标检测",[27],{"code":32,"score":18,"supported":52,"title":506,"abstract":507,"tags":508},[33],[34,33],[34],{"code":63,"score":21,"supported":13,"title":510,"abstract":511,"tags":512},[],[],[26],[514,515],"农业人工智能 高通量表型 智慧农业 种子检测","农业人工智能 高通量表型","农业人工智能高通量表型智慧农业种子检测-2523","10.56612\u002Fijaaeb.v6i1.249",{"doi":517,"openalex_id":519,"authors":520,"venue":483,"cited_by_count":89,"oa_url":480,"card":541,"direction":184,"ingested_from":91},"W7212562005",[521,524,526,529,531,533,535,537,539],{"name":522,"orcid":523},"Faisal Shahzad","https:\u002F\u002Forcid.org\u002F0009-0003-8413-2456",{"name":525,"orcid":8},"Hafiza Ayesha Arshad",{"name":527,"orcid":528},"Habib‐ur‐Rehman Athar","https:\u002F\u002Forcid.org\u002F0000-0002-8733-3865",{"name":530,"orcid":8},"Israr Hanif",{"name":532,"orcid":8},"Iqra Shokat",{"name":534,"orcid":8},"Ayesha Maryam",{"name":536,"orcid":8},"Laiba Urooj",{"name":538,"orcid":8},"Jaweria Maqbool",{"name":540,"orcid":8},"Aleena Akram",{"tldr":542,"method":543,"finding":544,"direction":184,"opportunity":545},"用自建RGB小麦种子数据集对比YOLOv8n与YOLO11n的检测计数性能。","832张RGB图像、3649个标注框，训练YOLOv8n与YOLO11n并多指标","两模型mAP@0.5均超0.994，YOLO11n在严格指标mAP@0.5:0.95上更优。","可扩展多品种、多类别种子质量评估，并引入更复杂背景与计数精度量化验证。","2026-09-15T23:30:17.202179Z"]