[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4078":3,"related-4078":123},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":37,"classification":38,"search_phrases":59,"slug":62,"view_count":63,"doi":64,"paper":65,"created_at":122},4078,"Assessing plant area index and 3D plant area distribution in wheat canopies from a terrestrial LiDAR system for high-throughput plant phenotyping","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115711","This study investigates a new methodology to estimate plant area index ( PAI ) and its spatial distribution from 3D point clouds acquired by a system of 3 LiDARs (2 nadir-viewing and one at 45°) mounted on the Phenomobile Unmanned Ground Vehicle designed for high-throughput phenotyping of herbaceous crops. It relies on the computation of the gap fraction at the voxel level using the trajectories of the laser beams to numerically invert the Beer-Lambert law (BL), enabling the simultaneous estimation of PAI and AIA (Average Inclination Angle, in°). The method is evaluated on wheat crops both in silico (i.e. simulations based on the AdelWheat functional structural crop model coupled with a point cloud simulator) and in actual phenotyping trials of a panel of 10 bread wheat genotypes grown in two study sites under different treatments of plant density, water stress and sowing dates. The in silico validation indicates that the numeric inversion of BL with the LiDAR system of the Phenomobile leads to a relative error of 9.8% (RMSE = 0.32, R 2 = 0.99) and 8.4% (RMSE =5.4°, R 2 = 0.95) in the estimation of PAI and AIA , respectively. The validation in actual phenotyping experiments against destructive measurements produced satisfactory results for canopy PAI (RMSE = 1.44, R 2 = 0.82), while requiring prior knowledge on AIA , e.g. either by (i) introducing a regularization term in the cost function used to invert BL, to avoid unexpected high AIA estimations, (ii) using a fixed, reference value of 60°. The analysis of the results indicates that the inclusion of a regularization term mitigates the impact of actual differences in leaf inclination among cultivars on PAI accuracy, while providing a relative description of the actual variability in the vertical profile of leaf inclination across the different cultivars.","本研究探讨了一种新方法，用于从三维点云中估算植物面积指数（PAI）及其空间分布。该点云由安装在Phenomobile无人地面车辆上的3台激光雷达（LiDAR）系统（2台天底视角和1台45°视角）获取，该车辆专为草本作物的高通量表型设计。该方法依赖于利用激光束轨迹在体素水平上计算间隙率，以数值反演Beer-Lambert定律（BL），从而能够同时估算PAI和AIA（平均倾角，单位为°）。该方法在小麦作物上进行了评估，既包括计算机模拟（即基于AdelWheat功能结构作物模型结合点云模拟器的模拟），也包括在实际表型试验中对10个面包小麦基因型组成的 panel 在两个研究地点、不同植株密度、水分胁迫和播种日期处理下的种植。计算机模拟验证表明，利用Phenomobile的LiDAR系统对BL进行数值反演，在PAI和AIA估算中分别产生9.8%（RMSE = 0.32，R² = 0.99）和8.4%（RMSE = 5.4°，R² = 0.95）的相对误差。在实际表型试验中与破坏性测量进行验证，冠层PAI获得了令人满意的结果（RMSE = 1.44，R² = 0.82），但需要关于AIA的先验知识，例如通过以下方式之一：（i）在用于反演BL的代价函数中引入正则化项，以避免出现意外的高AIA估算值；（ii）使用固定的参考值60°。结果分析表明，引入正则化项减轻了品种间叶片倾角实际差异对PAI精度的影响，同时提供了不同品种间叶片倾角垂直剖面实际变异性的相对描述。",null,"Remote Sensing of Environment","2026-10-02T00:00:00Z","论文",10,true,84,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,23,19,15,9,1,"基于车载三激光雷达点云反演小麦植物面积指数与倾角分布，仿真与田间验证扎实，属高通量表型领域方法学新进展。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","小麦","遥感监测","高通量表型","激光雷达",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[],{"status":39,"locked":40,"version":41,"method":42,"evidence":43},"classified",false,"2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":14},[45,49,54],{"code":33,"score":20,"supported":14,"title":46,"abstract":47,"tags":48},[34],[28,34],[28],{"code":50,"score":22,"supported":40,"title":51,"abstract":52,"tags":53},"I1",[],[],[27],{"code":55,"score":22,"supported":40,"title":56,"abstract":57,"tags":58},"I3",[],[],[29],[60,61],"Phenomobile 激光雷达 小麦","植物面积指数 三维点云","Phenomobile激光雷达小麦-4078",7,"10.1016\u002Fj.rse.2026.115711",{"doi":64,"openalex_id":66,"authors":67,"venue":10,"cited_by_count":114,"oa_url":6,"card":115,"direction":119,"ingested_from":121},"W7215681984",[68,71,73,76,78,80,83,85,88,91,94,96,98,100,102,104,106,108,111],{"name":69,"orcid":70},"Raúl López‐Lozano","https:\u002F\u002Forcid.org\u002F0000-0001-6499-4743",{"name":72,"orcid":9},"Tian Ma",{"name":74,"orcid":75},"Maxime Soma","https:\u002F\u002Forcid.org\u002F0000-0002-9242-3283",{"name":77,"orcid":9},"Marie Weiss",{"name":79,"orcid":9},"Aurélien Ausset",{"name":81,"orcid":82},"Philippe Burger","https:\u002F\u002Forcid.org\u002F0000-0003-3662-7924",{"name":84,"orcid":9},"Bruno Berthon",{"name":86,"orcid":87},"Romain Chapuis","https:\u002F\u002Forcid.org\u002F0000-0001-7196-4622",{"name":89,"orcid":90},"Marie-Pia D'argaignon","https:\u002F\u002Forcid.org\u002F0009-0004-6568-9213",{"name":92,"orcid":93},"Antonin Grau","https:\u002F\u002Forcid.org\u002F0000-0002-5366-9360",{"name":95,"orcid":9},"Florian Larue",{"name":97,"orcid":9},"Romane Le-Roy",{"name":99,"orcid":9},"Rémy Marandel",{"name":101,"orcid":9},"Vincent Mercier",{"name":103,"orcid":9},"Mathieu Roy",{"name":105,"orcid":9},"Gilles Tison",{"name":107,"orcid":9},"Frédéric Venault",{"name":109,"orcid":110},"Pierre Martre","https:\u002F\u002Forcid.org\u002F0000-0002-7419-6558",{"name":112,"orcid":113},"Frédéric Baret","https:\u002F\u002Forcid.org\u002F0000-0002-7655-8997",0,{"tldr":116,"method":117,"finding":118,"direction":119,"opportunity":120},"利用车载三LiDAR点云反演小麦冠层植物面积指数与三维分布，实现高通量表型分析。","体素间隙率结合Beer-Lambert数值反演，用AdelWheat模型仿真与1","PAI估算R²=0.82，加入正则项可减少叶倾角差异影响并描述垂直分布变异。","农业遥感与作物表型","可探索多角度LiDAR与正则化反演结合，提升不同基因型叶倾角垂直分布的鲁棒表型提取。","openalex","2026-10-03T23:30:24.403056Z",{"total":124,"page":22,"page_size":124,"items":125},6,[126,209,283,347,418,504],{"id":127,"title":128,"url":129,"summary":130,"summary_zh":131,"content":9,"source_name":132,"source_url":129,"published_at":133,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":134,"score_detail":135,"sources":141,"tags":143,"primary_topic":146,"secondary_topics":147,"classification":154,"search_phrases":174,"slug":177,"view_count":178,"doi":179,"paper":180,"created_at":208},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":20,"substance":136,"depth":137,"authority":138,"freshness":139,"relevant":22,"comment":140},20,17,13,8,"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。",[142],{"name":132,"url":129},[27,144,28,30,145],"农业人工智能","种子检测",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[148],{"code":149,"slug":150,"name":151,"path":152,"parent_code":153},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":40,"version":41,"method":42,"evidence":155},{"candidates":156,"agricultural_evidence":14},[157,166,170],{"code":149,"score":158,"supported":14,"title":159,"abstract":162,"tags":165},26,[160,161],"deep learning","YOLO",[163,164,160,161],"目标检测","深度学习",[144],{"code":33,"score":20,"supported":14,"title":167,"abstract":168,"tags":169},[34],[28,34],[28],{"code":50,"score":22,"supported":40,"title":171,"abstract":172,"tags":173},[],[],[27],[175,176],"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",3,"10.56612\u002Fijaaeb.v6i1.249",{"doi":179,"openalex_id":181,"authors":182,"venue":132,"cited_by_count":114,"oa_url":129,"card":203,"direction":119,"ingested_from":121},"W7212562005",[183,186,188,191,193,195,197,199,201],{"name":184,"orcid":185},"Faisal Shahzad","https:\u002F\u002Forcid.org\u002F0009-0003-8413-2456",{"name":187,"orcid":9},"Hafiza Ayesha Arshad",{"name":189,"orcid":190},"Habib‐ur‐Rehman Athar","https:\u002F\u002Forcid.org\u002F0000-0002-8733-3865",{"name":192,"orcid":9},"Israr Hanif",{"name":194,"orcid":9},"Iqra Shokat",{"name":196,"orcid":9},"Ayesha Maryam",{"name":198,"orcid":9},"Laiba Urooj",{"name":200,"orcid":9},"Jaweria Maqbool",{"name":202,"orcid":9},"Aleena Akram",{"tldr":204,"method":205,"finding":206,"direction":119,"opportunity":207},"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":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":215,"source_url":212,"published_at":216,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":217,"score_detail":218,"sources":222,"tags":224,"primary_topic":227,"secondary_topics":228,"classification":229,"search_phrases":244,"slug":247,"view_count":178,"doi":248,"paper":249,"created_at":282},2114,"Estimating wheat lodging fraction from Sentinel-1\u002F2 imagery using UAV-derived reference labels","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112410","Estimating wheat lodging fraction from Sentinel-1\u002F2 imagery using UAV-derived reference labels。Computers and Electronics in Agriculture","利用无人机获取的参考标签，从Sentinel-1\u002F2影像估算小麦倒伏比例。《计算机与电子农业》","Computers and Electronics in Agriculture","2026-09-11T00:00:00Z",81,{"impact":17,"substance":219,"depth":17,"authority":220,"freshness":13,"relevant":22,"comment":221},21,14,"核心期刊论文，提出用Sentinel-1\u002F2结合无人机参考标签估算小麦倒伏比例，方法新颖、数据可靠，对农业遥感监测有实质参考价值。",[223],{"name":215,"url":212},[27,225,28,29,226],"无人机","作物倒伏",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[],{"status":39,"locked":40,"version":41,"method":42,"evidence":230},{"candidates":231,"agricultural_evidence":14},[232,236,240],{"code":33,"score":20,"supported":14,"title":233,"abstract":234,"tags":235},[34],[28,34],[28],{"code":50,"score":22,"supported":40,"title":237,"abstract":238,"tags":239},[],[],[27],{"code":55,"score":22,"supported":40,"title":241,"abstract":242,"tags":243},[],[],[29],[245,246],"作物倒伏 智慧农业 遥感监测 无人机","作物倒伏 智慧农业","作物倒伏智慧农业遥感监测无人机-2114","10.1016\u002Fj.compag.2026.112410",{"doi":248,"openalex_id":250,"authors":251,"venue":215,"cited_by_count":114,"oa_url":212,"card":277,"direction":119,"ingested_from":121},"W7212131491",[252,254,257,259,262,264,266,269,271,274],{"name":253,"orcid":9},"Baoyuan Zhang",{"name":255,"orcid":256},"Tianxiang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-0996-2586",{"name":258,"orcid":9},"Xingyu Liu",{"name":260,"orcid":261},"Chao Song","https:\u002F\u002Forcid.org\u002F0000-0002-0328-9239",{"name":263,"orcid":9},"Xiaoyuan Bao",{"name":265,"orcid":9},"Qian Sun",{"name":267,"orcid":268},"Yuchun Pan","https:\u002F\u002Forcid.org\u002F0000-0002-1163-5963",{"name":270,"orcid":9},"Xia Yao",{"name":272,"orcid":273},"Meiyan Shu","https:\u002F\u002Forcid.org\u002F0000-0002-1519-5520",{"name":275,"orcid":276},"Xiaohe Gu","https:\u002F\u002Forcid.org\u002F0000-0002-7102-1939",{"tldr":278,"method":279,"finding":280,"direction":119,"opportunity":281},"利用无人机标签结合Sentinel-1\u002F2影像估算小麦倒伏比例。","Sentinel-1\u002F2卫星影像与无人机参考标签，机器学习回归。","无人机标签可有效训练卫星影像模型，实现区域小麦倒伏比例估算。","可探索多时相卫星与无人机协同的倒伏早期预警及不同品种泛化能力。","2026-09-11T23:30:01.591874Z",{"id":284,"title":285,"url":286,"summary":287,"summary_zh":288,"content":9,"source_name":215,"source_url":286,"published_at":289,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":290,"score_detail":291,"sources":294,"tags":296,"primary_topic":299,"secondary_topics":300,"classification":301,"search_phrases":312,"slug":315,"view_count":316,"doi":317,"paper":318,"created_at":346},1511,"Accurate prediction of wheat grain protein content via integration of genomic prediction and high-throughput phenotyping in breeding fields","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112388","Accurate prediction of wheat grain protein content via integration of genomic prediction and high-throughput phenotyping in breeding fields。Computers and Electronics in Agriculture","通过整合基因组预测与育种田间高通量表型分析实现小麦籽粒蛋白质含量的精准预测。《Computers and Electronics in Agriculture》","2026-09-02T00:00:00Z",79,{"impact":17,"substance":292,"depth":17,"authority":220,"freshness":63,"relevant":22,"comment":293},22,"融合基因组选择与高通量表型技术精准预测小麦籽粒蛋白含量，对育种产业有实质推动作用。",[295],{"name":215,"url":286},[27,28,297,298,30],"育种","基因组预测",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[],{"status":39,"locked":40,"version":41,"method":42,"evidence":302},{"candidates":303,"agricultural_evidence":14},[304,308],{"code":33,"score":20,"supported":14,"title":305,"abstract":306,"tags":307},[34],[28,34],[28],{"code":50,"score":22,"supported":40,"title":309,"abstract":310,"tags":311},[],[],[27],[313,314],"基因组预测 高通量表型 智慧农业 小麦","基因组预测 高通量表型","基因组预测高通量表型智慧农业小麦-1511",2,"10.1016\u002Fj.compag.2026.112388",{"doi":317,"openalex_id":319,"authors":320,"venue":215,"cited_by_count":114,"oa_url":9,"card":340,"direction":344,"ingested_from":121},"W7206161240",[321,323,325,327,329,331,333,335,338],{"name":322,"orcid":9},"Guoliang Wan",{"name":324,"orcid":9},"Lei Li",{"name":326,"orcid":9},"Luping Fu",{"name":328,"orcid":9},"R. Liu",{"name":330,"orcid":9},"Boyang Xue",{"name":332,"orcid":9},"Shaolin Yan",{"name":334,"orcid":9},"Kai Wang",{"name":336,"orcid":337},"Zhiqiang Tao","https:\u002F\u002Forcid.org\u002F0000-0002-5639-7540",{"name":339,"orcid":9},"Yonggui Xiao",{"tldr":341,"method":342,"finding":343,"direction":344,"opportunity":345},"结合基因组预测和高通量表型，提高小麦籽粒蛋白含量预测精度。","整合基因组预测与高通量表型数据，在育种田中进行建模。","集成方法显著提升蛋白含量预测准确性。","农业人工智能与决策模型","可探索多环境、多组学数据融合，或开发低成本表型采集方案以优化育种决策。","2026-09-03T23:30:02.218969Z",{"id":348,"title":349,"url":350,"summary":351,"summary_zh":352,"content":9,"source_name":215,"source_url":350,"published_at":289,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":134,"score_detail":353,"sources":355,"tags":357,"primary_topic":359,"secondary_topics":360,"classification":365,"search_phrases":381,"slug":384,"view_count":22,"doi":385,"paper":386,"created_at":417},1509,"Full-season wheat phenotyping using UAV-based multi-source remote sensing: dynamic time-series modeling and high-throughput trait extraction","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112383","Full-season wheat phenotyping using UAV-based multi-source remote sensing: dynamic time-series modeling and high-throughput trait extraction。Computers and Electronics in Agriculture","基于无人机多源遥感的全生育期小麦表型分析：动态时间序列建模与高通量性状提取。《Computers and Electronics in Agriculture》",{"impact":17,"substance":136,"depth":17,"authority":20,"freshness":316,"relevant":22,"comment":354},"基于无人机多源遥感的全生育期小麦表型研究，方法新颖，数据详实，对精准农业有参考价值。",[356],{"name":215,"url":350},[27,28,358,30],"遥感",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[361],{"code":55,"slug":362,"name":363,"path":364,"parent_code":153},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":40,"version":41,"method":42,"evidence":366},{"candidates":367,"agricultural_evidence":14},[368,372,377],{"code":33,"score":20,"supported":14,"title":369,"abstract":370,"tags":371},[34],[28,34],[28],{"code":55,"score":20,"supported":14,"title":373,"abstract":375,"tags":376},[374],"remote sensing",[358,374],[358],{"code":50,"score":22,"supported":40,"title":378,"abstract":379,"tags":380},[],[],[27],[382,383],"高通量表型 智慧农业 小麦 遥感","高通量表型 智慧农业","高通量表型智慧农业小麦遥感-1509","10.1016\u002Fj.compag.2026.112383",{"doi":385,"openalex_id":387,"authors":388,"venue":215,"cited_by_count":114,"oa_url":411,"card":412,"direction":119,"ingested_from":121},"W7154593787",[389,392,394,396,399,401,403,406,408],{"name":390,"orcid":391},"Honghao Zhou","https:\u002F\u002Forcid.org\u002F0009-0001-9850-5749",{"name":393,"orcid":9},"Chunmei Guo",{"name":395,"orcid":9},"Bingxi Qin",{"name":397,"orcid":398},"Wenlong Su","https:\u002F\u002Forcid.org\u002F0009-0002-0110-1754",{"name":400,"orcid":9},"Qi Sun",{"name":402,"orcid":9},"Zijun Pei",{"name":404,"orcid":405},"Qing Li","https:\u002F\u002Forcid.org\u002F0000-0002-5332-1401",{"name":407,"orcid":9},"Jiang Dong",{"name":409,"orcid":410},"Jiawei Chen","https:\u002F\u002Forcid.org\u002F0000-0002-5035-7804","https:\u002F\u002Fdoi.org\u002F10.2139\u002Fssrn.6589943",{"tldr":413,"method":414,"finding":415,"direction":119,"opportunity":416},"利用无人机多源遥感进行全季节小麦表型分析，通过动态时间序列建模提取高通量性状。","无人机多源遥感数据，动态时间序列建模，高通量性状提取。","实现了全季节小麦表型动态监测，提取了关键性状，支持育种评估。","可探索多源遥感数据融合与深度学习结合，提高复杂环境下作物性状预测精度和泛化能力。","2026-09-03T23:30:02.117716Z",{"id":419,"title":420,"url":421,"summary":422,"summary_zh":423,"content":9,"source_name":424,"source_url":421,"published_at":425,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":426,"score_detail":427,"sources":430,"tags":432,"primary_topic":436,"secondary_topics":437,"classification":445,"search_phrases":480,"slug":483,"view_count":114,"doi":484,"paper":485,"created_at":503},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":428,"substance":219,"depth":137,"authority":138,"freshness":13,"relevant":22,"comment":429},16,"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。",[431],{"name":424,"url":421},[27,433,29,434,435],"机器学习","土壤墒情","冬小麦",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[438,444],{"code":439,"slug":440,"name":441,"path":442,"parent_code":443},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":149,"slug":150,"name":151,"path":152,"parent_code":153},{"status":39,"locked":40,"version":41,"method":42,"evidence":446},{"candidates":447,"agricultural_evidence":14},[448,453,458,463,468,475],{"code":33,"score":158,"supported":14,"title":449,"abstract":451,"tags":452},[450,34],"winter wheat",[28,450,34],[435],{"code":439,"score":20,"supported":14,"title":454,"abstract":455,"tags":457},[440],[456,440],"土壤",[434],{"code":149,"score":20,"supported":14,"title":459,"abstract":461,"tags":462},[460],"machine learning",[433,460],[433],{"code":55,"score":13,"supported":14,"title":464,"abstract":465,"tags":467},[],[358,374,466],"multispectral",[29],{"code":469,"score":124,"supported":14,"title":470,"abstract":471,"tags":474},"H3",[],[472,473],"灌溉","irrigation",[],{"code":50,"score":476,"supported":14,"title":477,"abstract":478,"tags":479},4,[],[27],[27],[481,482],"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":484,"openalex_id":486,"authors":487,"venue":424,"cited_by_count":114,"oa_url":421,"card":497,"direction":502,"ingested_from":121},"W7223156204",[488,491,493,495],{"name":489,"orcid":490},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":492,"orcid":9},"Yi Zhang",{"name":494,"orcid":9},"WEI Lei",{"name":496,"orcid":9},"Chengcai Zhang",{"tldr":498,"method":499,"finding":500,"direction":119,"opportunity":501},"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":505,"title":506,"url":507,"summary":508,"summary_zh":509,"content":9,"source_name":510,"source_url":507,"published_at":511,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":426,"score_detail":512,"sources":514,"tags":516,"primary_topic":518,"secondary_topics":519,"classification":521,"search_phrases":540,"slug":543,"view_count":114,"doi":544,"paper":545,"created_at":568},5367,"Integration of multi-source UAV remote sensing features and derived features for winter wheat aboveground biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102656","Accurate estimation of winter wheat ( Triticum aestivum L.) aboveground biomass (AGB) is important for crop growth monitoring and field management. However, unmanned aerial vehicle (UAV)-based AGB estimation remains challenging due to variations in canopy conditions across growth stages, and different remote sensing features can capture complementary characteristics related to AGB estimation. This study integrated multi-source UAV-derived features, including vegetation indices (VIs), texture features (TFs), light detection and ranging (LiDAR)-derived features, and derived features (DFs) generated from the original feature sets, with four machine learning algorithms for winter wheat AGB estimation. Results showed that TFs provided better prediction performance than other single-source feature sets. Compared with single-source feature sets, the integration of VIs, TFs, LiDAR-derived features, and DFs further improved AGB estimation, and the random forest (RF) model achieved the best performance among the evaluated models (R² = 0.923, RMSE = 1.731 Mg ha⁻¹). Incremental R² analysis based on a stage-only baseline model showed that the RF model explained additional variation in AGB beyond growth-stage information, with an incremental R² of 0.479. Stage-specific evaluation indicated that model performance varied among growth stages, with the highest accuracy observed at the booting stage (R² = 0.632, RMSE = 1.425 Mg ha⁻¹). Feature contribution analysis based on the full-feature fusion model using all growth-stage data identified canopy coverage and several DFs as important predictors, while stage-specific analyses showed differences in dominant predictors across growth stages. The integration of multi-source UAV-derived features and DFs with machine learning provides an effective approach for winter wheat AGB estimation, while model performance remains strongly dependent on growth stage.","准确估算冬小麦（Triticum aestivum L.）地上生物量（aboveground biomass, AGB）对作物生长监测和田间管理具有重要意义。然而，由于不同生育期冠层条件存在差异，且不同遥感特征能够捕捉与AGB估算相关的互补信息，基于无人机（unmanned aerial vehicle, UAV）的AGB估算仍面临挑战。本研究将多源无人机衍生特征，包括植被指数（vegetation indices, VIs）、纹理特征（texture features, TFs）、激光雷达（light detection and ranging, LiDAR）衍生特征以及由原始特征集生成的衍生特征（derived features, DFs），与四种机器学习算法相结合，用于冬小麦AGB估算。结果表明，TFs的预测性能优于其他单一来源特征集。与单一来源特征集相比，融合VIs、TFs、LiDAR衍生特征和DFs进一步提高了AGB估算精度，其中随机森林（random forest, RF）模型在所评估的模型中表现最佳（R² = 0.923，RMSE = 1.731 Mg ha⁻¹）。基于仅含生育期信息的基线模型进行的增量R²分析表明，RF模型在生育期信息之外解释了AGB的额外变异，增量R²为0.479。分生育期评估表明，模型性能在不同生育期之间存在差异，其中在孕穗期观察到最高精度（R² = 0.632，RMSE = 1.425 Mg ha⁻¹）。基于使用全部生育期数据的全特征融合模型进行的特征贡献分析识别出冠层覆盖度和若干DFs为重要预测因子，而分生育期分析则表明不同生育期的主导预测因子存在差异。将多源无人机衍生特征和DFs与机器学习相结合，为冬小麦AGB估算提供了一种有效方法，但模型性能仍强烈依赖于生育期。","Smart Agricultural Technology","2026-10-17T00:00:00Z",{"impact":428,"substance":292,"depth":17,"authority":138,"freshness":139,"relevant":22,"comment":513},"该研究融合无人机多源遥感特征与衍生特征，用随机森林估算冬小麦地上生物量，方法新颖、结论可靠，对作物长势监测有参考价值。",[515],{"name":510,"url":507},[27,225,29,435,517],"生物量估算",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[520],{"code":55,"slug":362,"name":363,"path":364,"parent_code":153},{"status":39,"locked":40,"version":41,"method":42,"evidence":522},{"candidates":523,"agricultural_evidence":14},[524,528,532,536],{"code":33,"score":158,"supported":14,"title":525,"abstract":526,"tags":527},[450,34],[28,450,34],[435],{"code":55,"score":428,"supported":14,"title":529,"abstract":530,"tags":531},[374],[358,374],[517,29],{"code":149,"score":124,"supported":14,"title":533,"abstract":534,"tags":535},[],[433,460],[],{"code":50,"score":22,"supported":40,"title":537,"abstract":538,"tags":539},[],[],[27],[541,542],"无人机 多源遥感 冬小麦 生物量","冬小麦 地上生物量 随机森林","无人机多源遥感冬小麦生物量-5367","10.1016\u002Fj.atech.2026.102656",{"doi":544,"openalex_id":546,"authors":547,"venue":510,"cited_by_count":114,"oa_url":507,"card":563,"direction":119,"ingested_from":121},"W7222806066",[548,550,552,554,557,559,561],{"name":549,"orcid":9},"Guangtao Wang",{"name":551,"orcid":9},"Sumei Yao",{"name":553,"orcid":9},"Yanzheng Xie",{"name":555,"orcid":556},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":558,"orcid":9},"Xiangdong Chen",{"name":560,"orcid":9},"Yuhao Zhang",{"name":562,"orcid":9},"Lina Jiang",{"tldr":564,"method":565,"finding":566,"direction":119,"opportunity":567},"融合无人机多源遥感特征与衍生特征，用机器学习估算冬小麦地上生物量。","无人机植被指数、纹理、LiDAR特征及衍生特征结合四种机器学习算法建模。","多源特征融合优于单一特征，随机森林最优（R²=0.923），精度随生育期变化。","可探索生育期自适应建模与特征选择，提升跨期生物量估算的稳健性与可迁移性。","2026-10-20T23:30:03.106267Z"]