[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1511":3,"related-1511":91},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":36,"classification":37,"search_phrases":54,"slug":57,"view_count":58,"doi":59,"paper":60,"created_at":90},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》",null,"Computers and Electronics in Agriculture","2026-09-02T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,7,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","小麦","育种","基因组预测","高通量表型",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,49],{"code":32,"score":44,"supported":45,"title":46,"abstract":47,"tags":48},15,true,[33],[27,33],[27],{"code":50,"score":21,"supported":14,"title":51,"abstract":52,"tags":53},"I1",[],[],[26],[55,56],"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":59,"openalex_id":61,"authors":62,"venue":10,"cited_by_count":82,"oa_url":9,"card":83,"direction":87,"ingested_from":89},"W7206161240",[63,65,67,69,71,73,75,77,80],{"name":64,"orcid":9},"Guoliang Wan",{"name":66,"orcid":9},"Lei Li",{"name":68,"orcid":9},"Luping Fu",{"name":70,"orcid":9},"R. Liu",{"name":72,"orcid":9},"Boyang Xue",{"name":74,"orcid":9},"Shaolin Yan",{"name":76,"orcid":9},"Kai Wang",{"name":78,"orcid":79},"Zhiqiang Tao","https:\u002F\u002Forcid.org\u002F0000-0002-5639-7540",{"name":81,"orcid":9},"Yonggui Xiao",0,{"tldr":84,"method":85,"finding":86,"direction":87,"opportunity":88},"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。","openalex","2026-09-03T23:30:02.218969Z",{"total":92,"page":21,"page_size":92,"items":93},6,[94,192,275,346,423,520],{"id":95,"title":96,"url":97,"summary":98,"summary_zh":99,"content":9,"source_name":100,"source_url":97,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":45,"score":102,"score_detail":103,"sources":108,"tags":110,"primary_topic":113,"secondary_topics":114,"classification":115,"search_phrases":131,"slug":134,"view_count":20,"doi":135,"paper":136,"created_at":191},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。","Remote Sensing of Environment","2026-10-02T00:00:00Z",84,{"impact":17,"substance":104,"depth":105,"authority":44,"freshness":106,"relevant":21,"comment":107},23,19,9,"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。",[109],{"name":100,"url":97},[26,27,111,30,112],"遥感监测","激光雷达",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":116},{"candidates":117,"agricultural_evidence":45},[118,122,126],{"code":32,"score":44,"supported":45,"title":119,"abstract":120,"tags":121},[33],[27,33],[27],{"code":50,"score":21,"supported":14,"title":123,"abstract":124,"tags":125},[],[],[26],{"code":127,"score":21,"supported":14,"title":128,"abstract":129,"tags":130},"I3",[],[],[111],[132,133],"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","10.1016\u002Fj.rse.2026.115711",{"doi":135,"openalex_id":137,"authors":138,"venue":100,"cited_by_count":82,"oa_url":97,"card":185,"direction":189,"ingested_from":89},"W7215681984",[139,142,144,147,149,151,154,156,159,162,165,167,169,171,173,175,177,179,182],{"name":140,"orcid":141},"Raúl López‐Lozano","https:\u002F\u002Forcid.org\u002F0000-0001-6499-4743",{"name":143,"orcid":9},"Tian Ma",{"name":145,"orcid":146},"Maxime Soma","https:\u002F\u002Forcid.org\u002F0000-0002-9242-3283",{"name":148,"orcid":9},"Marie Weiss",{"name":150,"orcid":9},"Aurélien Ausset",{"name":152,"orcid":153},"Philippe Burger","https:\u002F\u002Forcid.org\u002F0000-0003-3662-7924",{"name":155,"orcid":9},"Bruno Berthon",{"name":157,"orcid":158},"Romain Chapuis","https:\u002F\u002Forcid.org\u002F0000-0001-7196-4622",{"name":160,"orcid":161},"Marie-Pia D'argaignon","https:\u002F\u002Forcid.org\u002F0009-0004-6568-9213",{"name":163,"orcid":164},"Antonin Grau","https:\u002F\u002Forcid.org\u002F0000-0002-5366-9360",{"name":166,"orcid":9},"Florian Larue",{"name":168,"orcid":9},"Romane Le-Roy",{"name":170,"orcid":9},"Rémy Marandel",{"name":172,"orcid":9},"Vincent Mercier",{"name":174,"orcid":9},"Mathieu Roy",{"name":176,"orcid":9},"Gilles Tison",{"name":178,"orcid":9},"Frédéric Venault",{"name":180,"orcid":181},"Pierre Martre","https:\u002F\u002Forcid.org\u002F0000-0002-7419-6558",{"name":183,"orcid":184},"Frédéric Baret","https:\u002F\u002Forcid.org\u002F0000-0002-7655-8997",{"tldr":186,"method":187,"finding":188,"direction":189,"opportunity":190},"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。","2026-10-03T23:30:24.403056Z",{"id":193,"title":194,"url":195,"summary":196,"summary_zh":197,"content":9,"source_name":198,"source_url":195,"published_at":199,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":200,"score_detail":201,"sources":207,"tags":209,"primary_topic":212,"secondary_topics":213,"classification":220,"search_phrases":240,"slug":243,"view_count":244,"doi":245,"paper":246,"created_at":274},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":44,"substance":202,"depth":203,"authority":204,"freshness":205,"relevant":21,"comment":206},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。",[208],{"name":198,"url":195},[26,210,27,30,211],"农业人工智能","种子检测",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},[214],{"code":215,"slug":216,"name":217,"path":218,"parent_code":219},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":38,"locked":14,"version":39,"method":40,"evidence":221},{"candidates":222,"agricultural_evidence":45},[223,232,236],{"code":215,"score":224,"supported":45,"title":225,"abstract":228,"tags":231},26,[226,227],"deep learning","YOLO",[229,230,226,227],"目标检测","深度学习",[210],{"code":32,"score":44,"supported":45,"title":233,"abstract":234,"tags":235},[33],[27,33],[27],{"code":50,"score":21,"supported":14,"title":237,"abstract":238,"tags":239},[],[],[26],[241,242],"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":245,"openalex_id":247,"authors":248,"venue":198,"cited_by_count":82,"oa_url":195,"card":269,"direction":189,"ingested_from":89},"W7212562005",[249,252,254,257,259,261,263,265,267],{"name":250,"orcid":251},"Faisal Shahzad","https:\u002F\u002Forcid.org\u002F0009-0003-8413-2456",{"name":253,"orcid":9},"Hafiza Ayesha Arshad",{"name":255,"orcid":256},"Habib‐ur‐Rehman Athar","https:\u002F\u002Forcid.org\u002F0000-0002-8733-3865",{"name":258,"orcid":9},"Israr Hanif",{"name":260,"orcid":9},"Iqra Shokat",{"name":262,"orcid":9},"Ayesha Maryam",{"name":264,"orcid":9},"Laiba Urooj",{"name":266,"orcid":9},"Jaweria Maqbool",{"name":268,"orcid":9},"Aleena Akram",{"tldr":270,"method":271,"finding":272,"direction":189,"opportunity":273},"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":276,"title":277,"url":278,"summary":279,"summary_zh":280,"content":9,"source_name":10,"source_url":278,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":200,"score_detail":281,"sources":283,"tags":285,"primary_topic":287,"secondary_topics":288,"classification":293,"search_phrases":309,"slug":312,"view_count":21,"doi":313,"paper":314,"created_at":345},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":202,"depth":17,"authority":44,"freshness":58,"relevant":21,"comment":282},"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。",[284],{"name":10,"url":278},[26,27,286,30],"遥感",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},[289],{"code":127,"slug":290,"name":291,"path":292,"parent_code":219},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":38,"locked":14,"version":39,"method":40,"evidence":294},{"candidates":295,"agricultural_evidence":45},[296,300,305],{"code":32,"score":44,"supported":45,"title":297,"abstract":298,"tags":299},[33],[27,33],[27],{"code":127,"score":44,"supported":45,"title":301,"abstract":303,"tags":304},[302],"remote sensing",[286,302],[286],{"code":50,"score":21,"supported":14,"title":306,"abstract":307,"tags":308},[],[],[26],[310,311],"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":313,"openalex_id":315,"authors":316,"venue":10,"cited_by_count":82,"oa_url":339,"card":340,"direction":189,"ingested_from":89},"W7154593787",[317,320,322,324,327,329,331,334,336],{"name":318,"orcid":319},"Honghao Zhou","https:\u002F\u002Forcid.org\u002F0009-0001-9850-5749",{"name":321,"orcid":9},"Chunmei Guo",{"name":323,"orcid":9},"Bingxi Qin",{"name":325,"orcid":326},"Wenlong Su","https:\u002F\u002Forcid.org\u002F0009-0002-0110-1754",{"name":328,"orcid":9},"Qi Sun",{"name":330,"orcid":9},"Zijun Pei",{"name":332,"orcid":333},"Qing Li","https:\u002F\u002Forcid.org\u002F0000-0002-5332-1401",{"name":335,"orcid":9},"Jiang Dong",{"name":337,"orcid":338},"Jiawei Chen","https:\u002F\u002Forcid.org\u002F0000-0002-5035-7804","https:\u002F\u002Fdoi.org\u002F10.2139\u002Fssrn.6589943",{"tldr":341,"method":342,"finding":343,"direction":189,"opportunity":344},"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":347,"title":348,"url":349,"summary":350,"summary_zh":351,"content":9,"source_name":352,"source_url":349,"published_at":353,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":354,"score_detail":355,"sources":357,"tags":359,"primary_topic":363,"secondary_topics":364,"classification":365,"search_phrases":397,"slug":400,"view_count":82,"doi":401,"paper":402,"created_at":422},4820,"Integrated strategies for improving lodging resistance in wheat: physiological mechanisms, plant growth regulators and agronomic management","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1925460","Wheat lodging remains a major constraint to sustainable wheat production because it reduces grain yield and quality, increases harvesting losses and lowers production efficiency. The widespread cultivation of high-yielding cultivars, intensive nitrogen fertilization, higher planting densities, expansion of irrigated agriculture, and increasingly frequent extreme weather events have collectively increased lodging susceptibility, making effective management an important priority. Lodging is a complex phenomenon governed by interactions among plant architecture, stem mechanical properties, root anchorage, crop management practices and environmental conditions. Consequently, improving lodging resistance requires an integrated approach that combines physiological understanding with practical agronomic interventions. This review synthesizes recent advances in the structural, physiological, biochemical and agronomic mechanisms underlying lodging resistance in wheat. Particular emphasis is placed on stem biomechanics, basal internode characteristics, cell wall composition, lignin and cellulose deposition, root system architecture, and genetic factors that collectively determine plant stability. The review further examines the influence of major agronomic practices, including nitrogen management, plant population, sowing time, irrigation scheduling and cultivar selection, on lodging risk under diverse production environments. In addition, the physiological responses, field performance, practical applications and limitations of commonly used plant growth regulators, including chlormequat chloride, trinexapac-ethyl, paclobutrazol, uniconazole and ethephon, are critically evaluated in relation to their roles in improving stem strength and reducing lodging. The available evidence indicates that no single strategy provides consistent lodging control across all production systems. Instead, durable improvement in lodging resistance depends on integrating genetic improvement, balanced crop nutrition, optimized agronomic management, and judicious use of plant growth regulators according to local environmental conditions. The review also highlights emerging opportunities offered by precision agriculture, high-throughput phenotyping, advanced spray technologies, and improved understanding of stem biomechanics and root anchorage. Together, these advances provide a framework for developing sustainable, climate-resilient lodging management strategies that enhance wheat productivity, crop stability and resource-use efficiency, while also highlighting key research priorities for future investigation.","小麦倒伏仍是可持续小麦生产的主要制约因素，因为它降低籽粒产量和品质，增加收获损失并降低生产效率。高产栽培品种的广泛种植、集约化氮肥施用、更高的种植密度、灌溉农业的扩张以及日益频繁的极端天气事件，共同增加了倒伏易感性，使有效管理成为重要优先事项。倒伏是一种复杂现象，受植株结构、茎秆力学特性、根系锚定、作物管理措施和环境条件之间相互作用的支配。因此，提高抗倒伏性需要将生理学认识与实际农艺干预相结合的综合方法。本综述综合了小麦抗倒伏性所依据的结构、生理、生化和农艺机制方面的最新进展。特别强调了茎秆生物力学、基部节间特征、细胞壁组成、木质素和纤维素沉积、根系构型以及共同决定植株稳定性的遗传因素。本综述进一步考察了主要农艺措施，包括氮素管理、植株群体、播期、灌溉制度和品种选择，在不同生产环境下对倒伏风险的影响。此外，对常用植物生长调节剂，包括矮壮素(chlormequat chloride)、抗倒酯(trinexapac-ethyl)、多效唑(paclobutrazol)、烯效唑(uniconazole)和乙烯利(ethephon)，在其提高茎秆强度和减少倒伏的作用方面，对其生理响应、田间表现、实际应用和局限性进行了批判性评价。现有证据表明，没有任何单一策略能够在所有生产系统中提供一致的倒伏控制。相反，抗倒伏性的持久改善取决于将遗传改良、平衡作物营养、优化农艺管理以及根据当地环境条件合理使用植物生长调节剂相结合。本综述还强调了精准农业、高通量表型分析、先进喷雾技术以及对茎秆生物力学和根系锚定认识的提高所提供的新兴机遇。总之，这些进展为开发可持续、气候适应型的倒伏管理策略提供了框架，从而提高小麦生产","Frontiers in Plant Science","2026-10-12T00:00:00Z",81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":106,"relevant":21,"comment":356},"系统综述小麦抗倒伏的生理机制、生长调节剂与农艺管理，信息增量与专业深度较高，对小麦稳产与智慧化管理有参考价值。",[358],{"name":352,"url":349},[26,27,360,361,362],"抗倒伏","植物生长调节剂","农艺管理",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":366},{"candidates":367,"agricultural_evidence":45},[368,372,378,385,391],{"code":32,"score":44,"supported":45,"title":369,"abstract":370,"tags":371},[33],[27,33],[27],{"code":50,"score":20,"supported":45,"title":373,"abstract":374,"tags":377},[],[375,376],"精准农业","precision agriculture",[26],{"code":379,"score":92,"supported":45,"title":380,"abstract":381,"tags":384},"H3",[],[382,383],"灌溉","irrigation",[],{"code":386,"score":244,"supported":45,"title":387,"abstract":388,"tags":390},"F3",[],[389],"fertilization",[],{"code":392,"score":244,"supported":45,"title":393,"abstract":394,"tags":396},"H7",[],[395],"气候适应",[],[398,399],"小麦 抗倒伏 栽培管理","小麦 植物生长调节剂 矮壮素","小麦抗倒伏栽培管理-4820","10.3389\u002Ffpls.2026.1925460",{"doi":401,"openalex_id":403,"authors":404,"venue":352,"cited_by_count":82,"oa_url":416,"card":417,"direction":189,"ingested_from":89},"W7221517218",[405,407,409,411,414],{"name":406,"orcid":9},"Kavita Devi",{"name":408,"orcid":9},"Jasdev Singh Deol",{"name":410,"orcid":9},"Amit Kaul",{"name":412,"orcid":413},"Arun Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-0106-2735",{"name":415,"orcid":9},"Rahul Sharma","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1925460\u002Fpdf",{"tldr":418,"method":419,"finding":420,"direction":87,"opportunity":421},"综述小麦抗倒伏的生理机制、植物生长调节剂与农艺管理，提出综合防控策略。","文献综述，整合茎秆生物力学、细胞壁、根系及农艺与调节剂证据。","单一措施难以稳定控倒，需结合遗传、营养、农艺与调节剂并因地制宜。","可结合高通量表型与精准农业构建抗倒伏智能决策模型，实现因地制宜动态调控。","2026-10-13T23:30:19.035054Z",{"id":424,"title":425,"url":426,"summary":427,"summary_zh":428,"content":9,"source_name":429,"source_url":426,"published_at":430,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":431,"score_detail":432,"sources":435,"tags":437,"primary_topic":441,"secondary_topics":442,"classification":447,"search_phrases":494,"slug":497,"view_count":82,"doi":498,"paper":499,"created_at":519},4242,"Integrated Irrigation and Disease Management Strategies for Sustainable Wheat Production and Improved Plant Health and Grain Yield","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fkjard.2.10.2026.774","Wheat production was expected to provide reliable grain quality and farmer income while using less water and facing climatic variability, soil degradation, disease development, and rising energy costs. This literature-based paper critically synthesized verified research on irrigation, plant disease management, irrigation–disease interactions, sustainability, digital agriculture, and the Pakistan\u002Fdeveloping-economy context. It considered irrigation and plant health as related management areas rather than completely separate input decisions. The available evidence did not identify a universal irrigation technology or deficit percentage. Instead, it supported a context-specific approach that considered stored soil water and rainfall, protected important growth stages, managed canopy humidity and nitrogen, used resistant cultivars and clean seed, and applied surveillance, forecasting, and fungicides when justified. Global and China-focused studies reported that moderate deficit or optimized water-saving irrigation could improve water productivity, whereas severe water deficit commonly reduced yield. Jointing\u002Fstem elongation was repeatedly identified as an important stage for avoiding water stress, while anthesis and early grain filling were also important when water availability and disease risk required attention. Disease-management evidence emphasized genetic resistance, sanitation, crop rotation, scouting, and forecasting before chemical control. Fusarium head blight required particular attention to weather conditions around anthesis, residue inoculum, cultivar resistance, fungicide timing, and deoxynivalenol risk. Irrigation increased yield without increasing disease in some environments, although dense nitrogen-rich canopies, overhead wetting, and saturated soils could favor foliar diseases and waterlogging. Digital tools were considered decision-support infrastructure that linked observations, analysis, and management actions. Wheat-specific evidence was relatively stronger for soil-moisture scheduling and weather-based stripe-rust forecasting, whereas many claims involving artificial intelligence, UAVs, and variable-rate irrigation remained predictive or transferable rather than directly supported by causal field evidence. Evidence from Pakistan indicated dependence on groundwater, salinity and waterlogging problems, uneven disease surveillance, and barriers to technology adoption. Differences among official statistics also indicated the need for careful reconciliation rather than simple averaging. The paper proposed an integrated framework involving diagnosis, planning, stage-aware irrigation, plant-health protection, evaluation, and seasonal feedback. It also identified the need for multi-location and multi-season studies that evaluated water use, disease, grain quality, energy use, greenhouse-gas emissions, profitability, and equity together.","小麦生产被期望在提供可靠籽粒品质和农民收入的同时，减少用水并应对气候变异、土壤退化、病害发展和能源成本上升。这篇基于文献的论文批判性地综合了关于灌溉、植物病害管理、灌溉–病害相互作用、可持续性、数字农业以及巴基斯坦\u002F发展中经济体背景的已验证研究。它将灌溉和植物健康视为相关的管理领域，而非完全独立的投入决策。现有证据并未确定一种通用的灌溉技术或亏缺百分比。相反，它支持一种因地制宜的方法，即考虑土壤储水和降雨、保护重要生育阶段、管理冠层湿度和氮素、使用抗性品种和清洁种子，并在有依据时应用监测、预报和杀菌剂。全球和中国的研究报告称，适度亏缺或优化节水灌溉可提高水分生产力，而严重水分亏缺通常降低产量。拔节\u002F茎伸长阶段被反复确定为避免水分胁迫的重要阶段，而在水分可用性和病害风险需要关注时，开花期和灌浆早期也很重要。病害管理证据强调在化学防治之前采用遗传抗性、卫生措施、轮作、田间巡查和预报。赤霉病需要特别关注开花期前后的天气条件、残体接种体、品种抗性、杀菌剂施用时机和脱氧雪腐镰刀菌烯醇风险。在某些环境中，灌溉提高了产量而未增加病害，尽管密集的富氮冠层、 overhead wetting 和饱和土壤可能有利于叶部病害和渍害。数字工具被视为连接观测、分析和管理行动的决策支持基础设施。小麦-specific 证据在土壤水分调度和基于天气的条锈病预报方面相对更强，而许多涉及人工智能、无人机和变量灌溉的说法仍属于预测性或可迁移性，而非直接得到因果田间证据的支持。来自巴基斯坦的证据表明对地下水的依赖、盐渍化和渍害问题、病害监测不均衡以及技术采用的障碍。官方统计数据之间的差异也表明需要谨慎。","Kashmir Journal of Academic Research and Development","2026-10-04T00:00:00Z",66,{"impact":433,"substance":202,"depth":203,"authority":205,"freshness":106,"relevant":21,"comment":434},12,"综述性论文，系统整合灌溉与病害协同管理及数字农业证据，对小麦节水抗病生产有参考价值，但非原创数据、层级偏学术。",[436],{"name":429,"url":426},[438,26,27,439,440],"数字农业","病害防控","灌溉管理",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},[443],{"code":379,"slug":383,"name":444,"path":445,"parent_code":446},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":38,"locked":14,"version":39,"method":40,"evidence":448},{"candidates":449,"agricultural_evidence":45},[450,454,458,463,470,477,483,489],{"code":32,"score":44,"supported":45,"title":451,"abstract":452,"tags":453},[33],[27,33],[27],{"code":379,"score":44,"supported":45,"title":455,"abstract":456,"tags":457},[383],[382,383],[440],{"code":50,"score":205,"supported":45,"title":459,"abstract":460,"tags":462},[],[438,461],"digital agriculture",[438,26],{"code":464,"score":92,"supported":45,"title":465,"abstract":466,"tags":469},"A7",[],[467,468],"轮作","crop rotation",[],{"code":471,"score":92,"supported":45,"title":472,"abstract":473,"tags":476},"H2",[],[474,475],"土壤","soil",[],{"code":392,"score":92,"supported":45,"title":478,"abstract":479,"tags":482},[],[480,481],"渍害","waterlogging",[],{"code":484,"score":244,"supported":45,"title":485,"abstract":486,"tags":488},"B7",[],[487],"greenhouse",[],{"code":215,"score":244,"supported":45,"title":490,"abstract":491,"tags":493},[],[492],"artificial intelligence",[],[495,496],"小麦 灌溉 病害 综合管理","小麦 节水灌溉 赤霉病","小麦灌溉病害综合管理-4242","10.71317\u002Fkjard.2.10.2026.774",{"doi":498,"openalex_id":500,"authors":501,"venue":429,"cited_by_count":82,"oa_url":426,"card":512,"direction":518,"ingested_from":89},"W7219840451",[502,504,506,508,510],{"name":503,"orcid":9},"Amir Khan Jamali",{"name":505,"orcid":9},"Zahir Muhammad Larik",{"name":507,"orcid":9},"Videsh Kumar",{"name":509,"orcid":9},"Um E Habiba",{"name":511,"orcid":9},"Amanullah Maree",{"tldr":513,"method":514,"finding":515,"direction":516,"opportunity":517},"综述小麦灌溉与病害管理整合策略，强调因地制宜而非通用方案。","文献综述，综合灌溉、病害、数字农业及巴基斯坦等发展经济体证据。","适度亏缺灌溉可提高水分生产力，但严重亏缺减产；病害管理需结合抗性、预测与适时用药。","智慧农业 \u002F 农业物联网","AI、无人机和变量灌溉在小麦中的因果田间证据不足，可开展整合灌溉-病害的验证研究。","数字乡村与农业信息化","2026-10-05T23:30:30.962770Z",{"id":521,"title":522,"url":523,"summary":524,"summary_zh":525,"content":9,"source_name":526,"source_url":523,"published_at":527,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":528,"score_detail":529,"sources":532,"tags":534,"primary_topic":537,"secondary_topics":538,"classification":539,"search_phrases":554,"slug":557,"view_count":244,"doi":558,"paper":559,"created_at":575},4020,"Robust QR Code Detection in Oblique Aerial Images for Agricultural Field Experiments","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193361","Field experiments in precision agriculture require reliable linkage of plant variants to experimental plots, which is difficult in oblique aerial imagery because the drone position does not identify the imaged ground location. We present and evaluate a reproducible workflow for detecting QR reference markers in high-resolution UAV images acquired over a wheat field before crop growth at a 45° viewing angle. The workflow combines manually defined regions of interest, documented preprocessing, a rotation sweep, and independent evaluation of OpenCV and pyzbar decoding backends. QR codes were selected for practical field deployment because their payload can directly encode a plot identifier and can be checked with an ordinary smartphone; the study does not benchmark QR codes against other fiducial-marker families. In 500 field-collected QR-code crops from 20 images, 491 codes (98.2%) were decoded; all nine failures had incomplete code visibility. The observed result is therefore a system-level outcome of both image acquisition and decoding under the tested configuration, rather than a growing-season operational accuracy. The mean processing time was approximately 0.41 s per region of interest on a standard PC. The work demonstrates QR-marker detection under the stated pre-growth conditions and provides a basis for future plot-label reference, calibration, and downstream Fusarium Head Blight phenotyping.","精准农业中的田间试验需要将植物变异可靠地关联到试验小区，而这在倾斜航空影像中较为困难，因为无人机位置并不能标识所成像的地面位置。我们提出并评估了一套可重复的工作流程，用于在作物生长前以45°视角获取的小麦田高分辨率无人机影像中检测QR参考标记。该工作流程结合了人工定义的感兴趣区域、有记录的前处理步骤、旋转扫描，以及对OpenCV和pyzbar解码后端的独立评估。选择QR码用于实际田间部署，是因为其有效载荷可直接编码小区标识符，并可用普通智能手机进行核验；本研究并未将QR码与其他基准标记类型进行基准比较。在来自20幅影像的500个田间采集QR码裁剪区域中，491个码（98.2%）被成功解码；全部9个失败案例均存在码可见性不完整的问题。因此，所观察到的结果是测试配置下图像采集与解码共同作用的系统级结果，而非整个生长季的运行精度。在标准PC上，每个感兴趣区域的平均处理时间约为0.41 s。该工作证明了在上述生长前条件下QR标记检测的可行性，并为未来的小区标签参考、校准以及下游赤霉病（Fusarium Head Blight）表型分析提供了基础。","Remote Sensing","2026-10-01T00:00:00Z",65,{"impact":205,"substance":17,"depth":530,"authority":19,"freshness":106,"relevant":21,"comment":531},16,"该论文提出斜视无人机影像中QR码检测的可复现流程，在500个样本中解码成功率达98.2%，为精准农业田间试验的地块标识提供了实用方案，方法新颖且数据可靠，但属细分技术进展，影响范围有限。",[533],{"name":526,"url":523},[26,535,536,27,375],"无人机","农业遥感",{"code":32,"slug":33,"name":27,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":540},{"candidates":541,"agricultural_evidence":45},[542,546,550],{"code":50,"score":205,"supported":45,"title":543,"abstract":544,"tags":545},[],[375,376],[26,375],{"code":32,"score":20,"supported":45,"title":547,"abstract":548,"tags":549},[],[27,33],[27],{"code":127,"score":21,"supported":14,"title":551,"abstract":552,"tags":553},[],[],[536],[555,556],"无人机 斜视影像 QR码 检测","小麦 田间试验 地块标识","无人机斜视影像QR码检测-4020","10.3390\u002Frs18193361",{"doi":558,"openalex_id":560,"authors":561,"venue":526,"cited_by_count":82,"oa_url":523,"card":570,"direction":189,"ingested_from":89},"W7215065180",[562,564,567],{"name":563,"orcid":9},"Gudrun Kinz",{"name":565,"orcid":566},"Hermann Buerstmayr","https:\u002F\u002Forcid.org\u002F0000-0002-0748-2351",{"name":568,"orcid":569},"Peter M. Roth","https:\u002F\u002Forcid.org\u002F0000-0001-9566-1298",{"tldr":571,"method":572,"finding":573,"direction":189,"opportunity":574},"提出并评估了在45°斜视无人机图像中检测田间二维码标记的可复现流程，解码成功率达98.2%。","结合手动ROI、预处理、旋转扫描及OpenCV与pyzbar双后端解码，使用50","491\u002F500个二维码成功解码，9个失败均因码可见性不完整，平均处理时间约0.41秒\u002FROI。","可探索斜视图像下多类基准标记的鲁棒检测与自动ROI定位，并接入生长季表型分析流程。","2026-10-02T23:30:19.815764Z"]