[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2265":3,"related-2265":74},{"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":36,"classification":37,"search_phrases":59,"slug":62,"view_count":21,"doi":8,"paper":63,"created_at":73},2265,"王发明、陈度团队小麦收获机器人负荷监测在《农业工程学报》发表","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7680385626411123210\u002F","中国农业大学等单位王发明、国树金、王玲、陈度等在《农业工程学报》2026年第42卷第11期发表论文,提出基于功率流和SVR-NOA的小麦收获机器人负荷在线监测方法,SVR-NOA模型在测试集R²=0.96、MSE=0.23、MAE=0.39 kg\u002Fs,在线验证平均误差2.5%。",null,"中国农业大学 农业工程学报","2026-09-06T16:00:00Z","论文",10,false,76,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,17,14,5,1,"核心期刊论文，提出功率流结合SVR-NOA的小麦收获机器人负荷在线监测方法，数据指标扎实，属智慧农业细分领域实质进展，但时效性一般。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","小麦","收获机器人","负荷监测",{"code":32,"slug":33,"name":28,"path":34,"parent_code":35},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,49,54],{"code":32,"score":44,"supported":45,"title":46,"abstract":47,"tags":48},12,true,[28],[28],[28],{"code":50,"score":21,"supported":13,"title":51,"abstract":52,"tags":53},"I1",[],[],[26],{"code":55,"score":21,"supported":13,"title":56,"abstract":57,"tags":58},"I2",[],[],[27],[60,61],"农业人工智能 收获机器人 智慧农业 负荷监测","农业人工智能 收获机器人","农业人工智能收获机器人智慧农业负荷监测-2265",{"doi":8,"openalex_id":8,"authors":64,"venue":8,"cited_by_count":65,"oa_url":8,"card":66,"direction":70,"ingested_from":72},[],0,{"tldr":67,"method":68,"finding":69,"direction":70,"opportunity":71},"提出基于功率流与SVR-NOA的小麦收获机器人负荷在线监测方法。","功率流分析结合SVR-NOA优化算法，用收获机器人田间数据验证。","模型测试集R²=0.96，在线验证平均误差仅2.5%，精度高。","农业人工智能与决策模型","可拓展至多作物、变工况下的负荷监测，并与收获决策控制闭环融合。","agent","2026-09-13T00:04:03.604386Z",{"total":75,"page":21,"page_size":75,"items":76},6,[77,177,251,315,377,448],{"id":78,"title":79,"url":80,"summary":81,"summary_zh":82,"content":8,"source_name":83,"source_url":80,"published_at":84,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":85,"score_detail":86,"sources":89,"tags":91,"primary_topic":94,"secondary_topics":95,"classification":106,"search_phrases":144,"slug":147,"view_count":148,"doi":149,"paper":150,"created_at":176},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","2026-09-15T00:00:00Z",80,{"impact":16,"substance":17,"depth":16,"authority":19,"freshness":87,"relevant":21,"comment":88},8,"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。",[90],{"name":83,"url":80},[26,27,28,92,93],"遥感","精准灌溉",{"code":32,"slug":33,"name":28,"path":34,"parent_code":35},[96,102],{"code":97,"slug":98,"name":99,"path":100,"parent_code":101},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":55,"slug":103,"name":104,"path":105,"parent_code":101},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":38,"locked":13,"version":39,"method":40,"evidence":107},{"candidates":108,"agricultural_evidence":45},[109,115,121,128,137],{"code":32,"score":110,"supported":45,"title":111,"abstract":113,"tags":114},26,[112,33],"winter wheat",[28,112,33],[28],{"code":97,"score":16,"supported":45,"title":116,"abstract":118,"tags":120},[117],"remote sensing",[92,117,119],"multispectral",[92],{"code":55,"score":122,"supported":45,"title":123,"abstract":125,"tags":127},15,[124],"machine learning",[126,124],"机器学习",[27],{"code":129,"score":12,"supported":45,"title":130,"abstract":131,"tags":136},"H3",[],[132,133,134,135],"灌溉","水资源","irrigation","water resource",[93],{"code":50,"score":138,"supported":45,"title":139,"abstract":140,"tags":143},7,[],[141,142],"精准农业","precision agriculture",[26],[145,146],"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",3,"10.3390\u002Frs18183161",{"doi":149,"openalex_id":151,"authors":152,"venue":83,"cited_by_count":65,"oa_url":80,"card":169,"direction":173,"ingested_from":175},"W7213246708",[153,156,158,160,163,166],{"name":154,"orcid":155},"Fan Ding","https:\u002F\u002Forcid.org\u002F0000-0001-5482-8290",{"name":157,"orcid":8},"Qian Cheng",{"name":159,"orcid":8},"Fuyi Duan",{"name":161,"orcid":162},"Shuaipeng Fei","https:\u002F\u002Forcid.org\u002F0000-0002-8774-7929",{"name":164,"orcid":165},"Junjie Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8900-2691",{"name":167,"orcid":168},"Zhen Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2847-0042",{"tldr":170,"method":171,"finding":172,"direction":173,"opportunity":174},"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。","农业遥感与作物表型","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。","openalex","2026-09-16T23:30:29.163192Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":8,"source_name":183,"source_url":180,"published_at":184,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":185,"score_detail":186,"sources":190,"tags":192,"primary_topic":195,"secondary_topics":196,"classification":198,"search_phrases":217,"slug":220,"view_count":148,"doi":221,"paper":222,"created_at":250},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":122,"substance":187,"depth":18,"authority":188,"freshness":87,"relevant":21,"comment":189},20,13,"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。",[191],{"name":183,"url":180},[26,27,28,193,194],"高通量表型","种子检测",{"code":32,"slug":33,"name":28,"path":34,"parent_code":35},[197],{"code":55,"slug":103,"name":104,"path":105,"parent_code":101},{"status":38,"locked":13,"version":39,"method":40,"evidence":199},{"candidates":200,"agricultural_evidence":45},[201,209,213],{"code":55,"score":110,"supported":45,"title":202,"abstract":205,"tags":208},[203,204],"deep learning","YOLO",[206,207,203,204],"目标检测","深度学习",[27],{"code":32,"score":122,"supported":45,"title":210,"abstract":211,"tags":212},[33],[28,33],[28],{"code":50,"score":21,"supported":13,"title":214,"abstract":215,"tags":216},[],[],[26],[218,219],"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":221,"openalex_id":223,"authors":224,"venue":183,"cited_by_count":65,"oa_url":180,"card":245,"direction":173,"ingested_from":175},"W7212562005",[225,228,230,233,235,237,239,241,243],{"name":226,"orcid":227},"Faisal Shahzad","https:\u002F\u002Forcid.org\u002F0009-0003-8413-2456",{"name":229,"orcid":8},"Hafiza Ayesha Arshad",{"name":231,"orcid":232},"Habib‐ur‐Rehman Athar","https:\u002F\u002Forcid.org\u002F0000-0002-8733-3865",{"name":234,"orcid":8},"Israr Hanif",{"name":236,"orcid":8},"Iqra Shokat",{"name":238,"orcid":8},"Ayesha Maryam",{"name":240,"orcid":8},"Laiba Urooj",{"name":242,"orcid":8},"Jaweria Maqbool",{"name":244,"orcid":8},"Aleena Akram",{"tldr":246,"method":247,"finding":248,"direction":173,"opportunity":249},"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",{"id":252,"title":253,"url":254,"summary":255,"summary_zh":8,"content":8,"source_name":256,"source_url":8,"published_at":257,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":258,"score_detail":259,"sources":261,"tags":263,"primary_topic":265,"secondary_topics":266,"classification":269,"search_phrases":303,"slug":306,"view_count":148,"doi":8,"paper":307,"created_at":314},1705,"VGG21 Deep Learning + Drone Multispectral Imagery Detects Wheat Waterlogging Stress and Silicon Fertilizer Effects","https:\u002F\u002Fagritechinsights.com\u002Findex.php\u002F2026\u002F09\u002F04\u002Fdrones-ai-spot-wheat-waterlogging-stress-in-real-time","江苏省农科院农业信息研究所梁万杰团队发表于《Smart Agriculture》。研究展示了一种基于多光谱相机的无人机快速检测小麦涝害并评估硅肥或氨基酸是否有助于作物恢复的方法。研究使用 DJI P4 多光谱无人机，在五个光谱波段捕获冠层图像。VGG21 模型识别胁迫和处理效应的整体准确率超过 91%，识别硅肥处理小麦的精确度达到 96.42%。研究指出早期涝害症状微妙多变导致健康与涝害小麦常被混淆，未来工作将量化胁迫检测与实际产量增益的相关性。","Agritech Insights","2026-09-04T00:00:00Z",78,{"impact":16,"substance":17,"depth":16,"authority":44,"freshness":87,"relevant":21,"comment":260},"省级农科院团队在核心期刊发表的研究，方法新颖且精度高，对小麦涝害监测有实际价值。",[262],{"name":256,"url":254},[26,27,264,28,92],"多光谱无人机",{"code":32,"slug":33,"name":28,"path":34,"parent_code":35},[267,268],{"code":97,"slug":98,"name":99,"path":100,"parent_code":101},{"code":55,"slug":103,"name":104,"path":105,"parent_code":101},{"status":38,"locked":13,"version":39,"method":40,"evidence":270},{"candidates":271,"agricultural_evidence":45},[272,276,280,285,291,297],{"code":32,"score":44,"supported":45,"title":273,"abstract":274,"tags":275},[33],[28],[28],{"code":97,"score":12,"supported":45,"title":277,"abstract":278,"tags":279},[119],[],[264,92],{"code":55,"score":281,"supported":45,"title":282,"abstract":283,"tags":284},9,[203],[],[27],{"code":286,"score":87,"supported":45,"title":287,"abstract":289,"tags":290},"F3",[288],"fertilizer",[],[],{"code":292,"score":87,"supported":45,"title":293,"abstract":295,"tags":296},"H7",[294],"waterlogging",[],[],{"code":50,"score":298,"supported":45,"title":299,"abstract":300,"tags":302},4,[],[301],"smart agriculture",[26],[304,305],"农业人工智能 多光谱无人机 智慧农业 小麦","农业人工智能 多光谱无人机","农业人工智能多光谱无人机智慧农业小麦-1705",{"doi":8,"openalex_id":8,"authors":308,"venue":8,"cited_by_count":65,"oa_url":8,"card":309,"direction":173,"ingested_from":72},[],{"tldr":310,"method":311,"finding":312,"direction":173,"opportunity":313},"用无人机多光谱和VGG21深度学习检测小麦涝害并评估硅肥效果。","DJI P4多光谱无人机采集五波段图像，VGG21模型分类。","VGG21识别胁迫与处理效应准确率超91%，硅肥处理精确度96.42%。","早期涝害症状微妙，可研究多时相数据或结合产量数据量化胁迫与恢复效果。","2026-09-05T00:04:40.858350Z",{"id":316,"title":317,"url":318,"summary":319,"summary_zh":320,"content":8,"source_name":321,"source_url":318,"published_at":322,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":323,"score_detail":324,"sources":326,"tags":328,"primary_topic":330,"secondary_topics":331,"classification":333,"search_phrases":360,"slug":362,"view_count":148,"doi":363,"paper":364,"created_at":376},1430,"Hyperspectral Imaging for Disease Detection in Wheat Crops: A Comprehensive Survey","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fijare.a-6533","Hyperspectral imaging (HSI) has emerged as a game-changing technology for precision agriculture because it captures detailed spectral signatures over hundreds of finely-resolved, continuous wavelength bands. This feature makes possible a detailed description of species-specific plant physiological states to enable early, precise and non-destructive identification of diseases in crops like wheat. With the rising demands worldwide for food security and healthy cultivation, the application of HSI in agro-diagnostics has expanded considerably. This paper provides a comprehensive and state-of-the-art review of hyperspectral imaging strategies devoted to wheat disease discrimination. It essentially introduces the theoretical backgrounds of HSI, such as data acquisition, preprocessing and spectral-spatial feature extraction. The survey further details a plethora of analytical approaches from classical statistical learning methods to deep learning and their utility in modelling HSI data. In particular, a comparative study of these methods is carried out and discussed for distinguishing healthy and diseased wheat, as well as different types of diseases under diverse environmental conditions. The paper also considers the benchmark datasets, sensors and platforms (field\u002Flaboratory-based) for which there are practical limitations. Key challenges are addressed, such as data dimensionality, variability as a consequence of atmospheric conditions and the requirement for online implementation in open-field scenarios. A set of suggestions for applying HSI in other domains has been proposed, such as the fusion of HSI with additional remote sensing information, introduction of compact models to edge devices and construction of a scalable and economical solution of HSI in commercial agriculture.","高光谱成像（HSI）已成为精准农业领域的一项变革性技术，因为它能够在数百个精细分辨、连续分布的波段上捕获详细的光谱特征。这一特性使得对物种特异性植物生理状态进行细致描述成为可能，从而能够对小麦等作物病害实现早期、精准且非破坏性的识别。随着全球对粮食安全和健康栽培需求的日益增长，HSI在农业诊断中的应用已显著扩展。本文对用于小麦病害区分的高光谱成像策略进行了全面且前沿的综述。文章首先介绍了HSI的理论基础，包括数据采集、预处理及光谱-空间特征提取。随后，综述详细探讨了从经典统计学习方法到深度学习的众多分析方法及其在HSI数据建模中的适用性。特别地，文中对这些方法进行了比较研究，并讨论了其在区分健康与患病小麦，以及在不同环境条件下识别不同类型病害方面的表现。本文还考虑了基准数据集、传感器及平台（田间\u002F实验室）在实际应用中存在的局限性。针对数据维度、大气条件引起的变异性以及开放田间场景下在线实施的需求等关键挑战进行了阐述。最后，提出了将HSI应用于其他领域的一系列建议，例如将HSI与额外遥感信息融合、将紧凑模型引入边缘设备，以及构建在商业农业中可扩展且经济可行的HSI解决方案。","Indian Journal of Agricultural Research","2026-09-02T00:00:00Z",77,{"impact":16,"substance":17,"depth":16,"authority":44,"freshness":138,"relevant":21,"comment":325},"高光谱成像用于小麦病害检测的综述，系统性强，方法比较全面，对智慧农业病害诊断有参考价值。",[327],{"name":321,"url":318},[26,27,28,92,329],"病害检测",{"code":32,"slug":33,"name":28,"path":34,"parent_code":35},[332],{"code":97,"slug":98,"name":99,"path":100,"parent_code":101},{"status":38,"locked":13,"version":39,"method":40,"evidence":334},{"candidates":335,"agricultural_evidence":45},[336,341,345,349,353],{"code":97,"score":16,"supported":45,"title":337,"abstract":339,"tags":340},[338],"hyperspectral",[92,117,338],[92],{"code":32,"score":122,"supported":45,"title":342,"abstract":343,"tags":344},[33],[28,33],[28],{"code":50,"score":138,"supported":45,"title":346,"abstract":347,"tags":348},[],[141,142],[26],{"code":55,"score":138,"supported":45,"title":350,"abstract":351,"tags":352},[],[207,203],[27],{"code":354,"score":75,"supported":45,"title":355,"abstract":356,"tags":359},"A8",[],[357,358],"粮食安全","food security",[],[361,146],"农业人工智能 智慧农业 病害检测 小麦","农业人工智能智慧农业病害检测小麦-1430","10.18805\u002Fijare.a-6533",{"doi":363,"openalex_id":365,"authors":366,"venue":321,"cited_by_count":65,"oa_url":318,"card":371,"direction":173,"ingested_from":175},"W7205005591",[367,369],{"name":368,"orcid":8},"M. Deepak",{"name":370,"orcid":8},"Gopal K. Shyam",{"tldr":372,"method":373,"finding":374,"direction":173,"opportunity":375},"综述高光谱成像在小麦病害检测中的应用，涵盖方法、挑战与未来方向。","综述数据采集、预处理、特征提取及统计学习与深度学习方法。","高光谱成像可早期、无损识别小麦病害，但面临维度高、环境变化和实时性挑战。","可探索高光谱与多源遥感融合、轻量化模型部署于边缘设备，以及低成本商业化方案。","2026-09-02T23:30:51.057495Z",{"id":378,"title":379,"url":380,"summary":381,"summary_zh":8,"content":8,"source_name":382,"source_url":380,"published_at":383,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":185,"score_detail":384,"sources":387,"tags":389,"primary_topic":390,"secondary_topics":391,"classification":393,"search_phrases":412,"slug":414,"view_count":148,"doi":415,"paper":416,"created_at":447},1203,"Transfer learning and multi-branch intermediate fusion deep neural network for wheat nitrogen concentration monitoring using multi-scale multi-modal remote sensing information","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112355","Transfer learning and multi-branch intermediate fusion deep neural network for wheat nitrogen concentration monitoring using multi-scale multi-modal remote sensing information。Computers and Electronics in Agriculture","Computers and Electronics in Agriculture","2026-08-27T00:00:00Z",{"impact":16,"substance":187,"depth":16,"authority":122,"freshness":385,"relevant":21,"comment":386},2,"提出多分支中间融合深度学习网络，结合多尺度多模态遥感监测小麦氮浓度，方法新颖，数据可靠，对精准农业有实质贡献。",[388],{"name":382,"url":380},[26,27,28,92],{"code":32,"slug":33,"name":28,"path":34,"parent_code":35},[392],{"code":97,"slug":98,"name":99,"path":100,"parent_code":101},{"status":38,"locked":13,"version":39,"method":40,"evidence":394},{"candidates":395,"agricultural_evidence":45},[396,400,404,408],{"code":32,"score":44,"supported":45,"title":397,"abstract":398,"tags":399},[33],[33],[28],{"code":97,"score":44,"supported":45,"title":401,"abstract":402,"tags":403},[117],[117],[92],{"code":50,"score":21,"supported":13,"title":405,"abstract":406,"tags":407},[],[],[26],{"code":55,"score":21,"supported":13,"title":409,"abstract":410,"tags":411},[],[],[27],[413,146],"农业人工智能 智慧农业 小麦 遥感","农业人工智能智慧农业小麦遥感-1203","10.1016\u002Fj.compag.2026.112355",{"doi":415,"openalex_id":417,"authors":418,"venue":382,"cited_by_count":65,"oa_url":8,"card":442,"direction":173,"ingested_from":175},"W7204268071",[419,421,423,426,429,432,434,436,439],{"name":420,"orcid":8},"Xiangxiang Su",{"name":422,"orcid":8},"Huihan Wang",{"name":424,"orcid":425},"Mi Yang","https:\u002F\u002Forcid.org\u002F0009-0000-6984-1164",{"name":427,"orcid":428},"Dongmei Yu","https:\u002F\u002Forcid.org\u002F0000-0003-2150-2010",{"name":430,"orcid":431},"Jieyu Li","https:\u002F\u002Forcid.org\u002F0000-0003-3838-6618",{"name":433,"orcid":8},"Weiqiang Wang",{"name":435,"orcid":8},"Ying Nian",{"name":437,"orcid":438},"Xinwei Li","https:\u002F\u002Forcid.org\u002F0000-0002-7036-5422",{"name":440,"orcid":441},"Ze Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-2318-8865",{"tldr":443,"method":444,"finding":445,"direction":173,"opportunity":446},"提出迁移学习与多分支中间融合深度网络，利用多尺度多模态遥感监测小麦氮浓度。","多分支中间融合深度神经网络，结合迁移学习与多尺度多模态遥感数据。","融合多模态遥感信息可提高小麦氮浓度监测精度。","可探索不同作物、不同胁迫下的多模态融合策略，以及轻量化模型部署。","2026-09-01T04:03:02.125620Z",{"id":449,"title":450,"url":451,"summary":452,"summary_zh":8,"content":8,"source_name":453,"source_url":8,"published_at":454,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":258,"score_detail":455,"sources":457,"tags":459,"primary_topic":463,"secondary_topics":464,"classification":465,"search_phrases":485,"slug":488,"view_count":65,"doi":8,"paper":489,"created_at":496},5365,"基于经济型多光谱成像与知识蒸馏模型压缩的小麦线条花叶病毒早期检测","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-10\u002F20261017000601916.htm","研究人员利用配备六个光谱滤光片的消费级数码单反(DSLR)相机在自然环境条件下获取了一个新的小麦线条花叶病毒(WhSMV)多光谱数据集。评估十二个深度学习模型识别 WhSMV 感染不同阶段的性能,并首次将模型压缩技术应用于多光谱图像用于 WhSMV 感染的早期检测。比较了深度学习算法与压缩模型的性能,揭示压缩模型达到与大型深度学习模型相当的性能。WhSMV 是小麦土传媒介传播病毒,巴西与南非均有报道。","Smart Agricultural Technology 2026-10-17","2026-10-17T00:00:00Z",{"impact":16,"substance":17,"depth":18,"authority":188,"freshness":87,"relevant":21,"comment":456},"该研究构建了新的多光谱数据集，并首次将模型压缩用于小麦病毒早期检测，方法新颖、结论可靠，对智慧农业植保具有参考价值。",[458],{"name":453,"url":451},[26,27,460,461,462],"多光谱成像","小麦病害","模型压缩",{"code":32,"slug":33,"name":28,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":466},{"candidates":467,"agricultural_evidence":45},[468,473,477,481],{"code":32,"score":469,"supported":45,"title":470,"abstract":471,"tags":472},11,[28],[28],[],{"code":55,"score":298,"supported":45,"title":474,"abstract":475,"tags":476},[],[207],[27],{"code":50,"score":21,"supported":13,"title":478,"abstract":479,"tags":480},[],[],[26],{"code":97,"score":21,"supported":13,"title":482,"abstract":483,"tags":484},[],[],[460],[486,487],"小麦线条花叶病毒 多光谱 检测","知识蒸馏 模型压缩 小麦","小麦线条花叶病毒多光谱检测-5365",{"doi":8,"openalex_id":8,"authors":490,"venue":8,"cited_by_count":65,"oa_url":8,"card":491,"direction":173,"ingested_from":72},[],{"tldr":492,"method":493,"finding":494,"direction":173,"opportunity":495},"用经济型多光谱成像结合知识蒸馏压缩模型，实现小麦线条花叶病毒早期检测。","消费级单反加六波段滤光片采集多光谱数据，评估12个深度学习模型并做模型压缩。","压缩模型性能与大型深度学习模型相当，可用于WhSMV感染早期识别。","可探索低成本多光谱设备与轻量模型在田间病害早期预警中的部署与跨区域泛化。","2026-10-20T02:47:54.487590Z"]