[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2315":3,"related-2315":98},{"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":38,"classification":39,"search_phrases":56,"slug":59,"view_count":60,"doi":61,"paper":62,"created_at":97},2315,"A practical workflow combining Kaplan-Meier and Bayesian accelerated failure time analyses for censored reproductive phenology data in soybean","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12870-026-09851-6","Abstract Developmental progression in plants is inherently a time-to-event process, yet plant phenology data are often analyzed as ordinary endpoint traits even when some individuals fail to reach the target stage within the observation window. Such observations are right-censored rather than missing and should be retained in inference. Here, we evaluated 21 soybean cultivars across 10 controlled-environment treatment combinations varying in CO 2 concentration, photoperiod, and LED light quality, and analyzed days from sowing to the R6 stage using a practical workflow that combined Kaplan-Meier survival analysis with a Bayesian Weibull accelerated failure time (AFT) model. Based on 210 cultivar × treatment observations derived from 1,050 plants, treatments resolved into favorable, intermediate, and strongly inhibitory classes. Elevated CO 2 showed the strongest association with reproductive timing: increasing CO 2 from ambient to 1000–1400 ppm reduced median survival time, while the additional gain from 1000 ppm to 1400 ppm was minimal, indicating a saturating response. Blue - Red spectral treatments and 6–8 h photoperiods were associated with rapid and synchronized development within the tested treatment combinations. The Bayesian AFT model included treatment as a random effect to account for the chamber-level experimental structure; the between-treatment standard deviation was estimated at 0.02 (95% credible interval: 0.00–0.06), indicating negligible chamber-to-chamber variation. The highest-ranked predicted combination (1400 ppm, blue: red = 2:1, 6 h) gave a median time to R6 of 66.90 d (mean 72.18 d). This study provides a practical and transferable workflow for censored plant phenology datasets in controlled-environment research, phenotyping, and breeding. The workflow integrates established survival analysis methods and explicitly accounts for chamber-level design, offering a framework for structured plant phenotyping experiments.","摘要 植物的发育进程本质上是一个时间到事件的过程，然而植物物候数据常常被当作普通的终点性状来分析，即使部分个体在观测窗口内未能达到目标阶段也是如此。此类观测属于右删失而非缺失，应在推断中予以保留。本研究评估了21个大豆品种在10种控制环境处理组合下的表现，这些组合在CO₂浓度、光周期和LED光质上有所不同，并采用将Kaplan-Meier生存分析与贝叶斯Weibull加速失效时间（AFT）模型相结合的实际工作流程，分析了从播种到R6阶段的天数。基于来自1，050株植物的210个品种×处理观测值，各处理可归纳为有利、中等和强抑制三类。升高CO₂与生殖时序的关联最强：将CO₂从环境浓度提高至1000–1400 ppm可缩短中位生存时间，而从1000 ppm进一步提高至1400 ppm的额外增益极小，表明存在饱和响应。在测试的处理组合中，蓝红光光谱处理和6–8 h光周期与快速且同步的发育相关。贝叶斯AFT模型将处理作为随机效应纳入，以考虑 chamber 层面的实验结构；处理间标准差估计为0.02（95%可信区间：0.00–0.06），表明 chamber 间变异可忽略不计。排名最高的预测组合（1400 ppm，蓝：红=2：1，6 h）给出的R6中位时间为66.90 d（均值72.18 d）。本研究为控制环境研究、表型分析和育种中的删失植物物候数据集提供了一个实用且可迁移的工作流程。该工作流程整合了已有的生存分析方法，并明确考虑了 chamber 层面的设计，为结构化植物表型实验提供了一个框架。",null,"BMC Plant Biology","2026-09-12T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,22,18,14,9,1,"提出结合Kaplan-Meier与贝叶斯AFT模型的删失表型数据分析流程，方法可迁移，对大豆生殖物候与受控环境育种表型研究有实用价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","设施农业","育种","大豆","表型组学",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":46},[45,51],{"code":33,"score":17,"supported":46,"title":47,"abstract":49,"tags":50},true,[48],"soybean",[30,48],[30],{"code":52,"score":22,"supported":14,"title":53,"abstract":54,"tags":55},"I1",[],[],[27],[57,58],"智慧农业 表型组学 设施农业 大豆","智慧农业 表型组学","智慧农业表型组学设施农业大豆-2315",2,"10.1186\u002Fs12870-026-09851-6",{"doi":61,"openalex_id":63,"authors":64,"venue":10,"cited_by_count":89,"oa_url":6,"card":90,"direction":94,"ingested_from":96},"W7212354617",[65,68,70,73,75,77,79,81,83,85,87],{"name":66,"orcid":67},"Fan Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-8576-0273",{"name":69,"orcid":9},"Xiao Cui",{"name":71,"orcid":72},"Kanglin Liu","https:\u002F\u002Forcid.org\u002F0000-0001-8747-5022",{"name":74,"orcid":9},"Huilong Hong",{"name":76,"orcid":9},"Xin Su",{"name":78,"orcid":9},"Yajun Xiong",{"name":80,"orcid":9},"Sawaira Jadoon",{"name":82,"orcid":9},"Huan Yu",{"name":84,"orcid":9},"Yijie Chen",{"name":86,"orcid":9},"Qiu Lijuan",{"name":88,"orcid":9},"Jun Wang",0,{"tldr":91,"method":92,"finding":93,"direction":94,"opportunity":95},"用Kaplan-Meier与贝叶斯AFT分析大豆R6期右删失数据，评估21个品种在10种环境组合下的","21个大豆品种、1050株、10种CO2\u002F光周期\u002FLED光质组合，Kaplan-","CO2升高缩短R6中位时间但1000-1400ppm增益极小，蓝红2:1与6-8h光周期发育最快， ","农业遥感与作物表型","可将该删失表型分析流程推广至田间多环境育种数据，并耦合高通量表型图像自动判定发育阶段。","openalex","2026-09-13T23:30:19.792952Z",{"total":99,"page":22,"page_size":99,"items":100},6,[101,173,253,331,384,441],{"id":102,"title":103,"url":104,"summary":105,"summary_zh":106,"content":9,"source_name":107,"source_url":104,"published_at":108,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":109,"sources":113,"tags":115,"primary_topic":118,"secondary_topics":119,"classification":120,"search_phrases":137,"slug":140,"view_count":141,"doi":142,"paper":143,"created_at":172},1990,"Scaling up soybean breeding: Satellite imagery delivers accurate maturity estimation across plot sizes","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fppj2.70100","Abstract Accurate and scalable phenotyping is essential for accelerating genetic gain in soybean ( Glycine max (L.) Merr.) breeding programs. Traditional methods for estimating physiological maturity are labor‐intensive and prone to subjectivity, limiting throughput and consistency. This study evaluates the potential of high‐resolution satellite imagery as an alternative to unmanned aerial vehicle (UAV)‐based imaging for estimating soybean maturity across diverse environments and plot configurations. Using vegetation indices derived from both platforms, we applied logistic regression models to predict maturity dates and compared them to established maturity date assessments. Our results demonstrate strong correlations and concordance between satellite‐ and UAV‐derived maturity estimates ( R 2 up to 0.94), with high broad‐sense heritability values ( H 2 up to 0.98), indicating robust genetic control of the trait. Satellite imagery proved effective even in small plot settings (four‐ and eight‐row designs), though performance was higher in larger plots. These findings highlight the feasibility of satellite‐based phenotyping for soybean maturity, offering a cost‐effective, scalable, and reliable alternative to UAVs. The approach has broad implications for enhancing breeding efficiency and expanding remote sensing applications in crop improvement.","摘要：准确且可扩展的表型鉴定对于加速大豆（Glycine max (L.) Merr.）育种项目中的遗传增益至关重要。传统的生理成熟度估算方法劳动密集且易受主观性影响，限制了通量和一致性。本研究评估了高分辨率卫星影像作为无人机（UAV）影像替代方案，在不同环境和小区布局下估算大豆成熟度的潜力。利用两种平台获取的植被指数，我们应用逻辑回归模型预测成熟日期，并将其与既定的成熟度评估进行比较。结果表明，卫星与无人机估算的成熟度之间具有强相关性和一致性（R²最高达0.94），且具有较高的广义遗传力值（H²最高达0.98），表明该性状受稳健的遗传控制。即使在较小的小区设置（四行和八行设计）中，卫星影像也表现出有效性，但在较大小区中性能更优。这些发现凸显了基于卫星的大豆成熟度表型鉴定的可行性，为无人机提供了一种经济高效、可扩展且可靠的替代方案。该方法对提升育种效率及扩展遥感在作物改良中的应用具有广泛意义。","The Plant Phenome Journal","2026-09-07T00:00:00Z",{"impact":19,"substance":18,"depth":19,"authority":110,"freshness":111,"relevant":22,"comment":112},13,7,"卫星遥感替代无人机用于大豆成熟期估算，方法新颖且数据可靠，对规模化育种表型鉴定有实质推动作用。",[114],{"name":107,"url":104},[27,29,30,116,117],"遥感","表型鉴定",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":121},{"candidates":122,"agricultural_evidence":46},[123,127,133],{"code":33,"score":17,"supported":46,"title":124,"abstract":125,"tags":126},[48],[30,48],[30],{"code":128,"score":111,"supported":46,"title":129,"abstract":130,"tags":132},"I3",[],[116,131],"remote sensing",[116],{"code":52,"score":22,"supported":14,"title":134,"abstract":135,"tags":136},[],[],[27],[138,139],"智慧农业 表型鉴定 大豆 育种","智慧农业 表型鉴定","智慧农业表型鉴定大豆育种-1990",3,"10.1002\u002Fppj2.70100",{"doi":142,"openalex_id":144,"authors":145,"venue":107,"cited_by_count":89,"oa_url":166,"card":167,"direction":94,"ingested_from":96},"W7211946346",[146,149,152,155,157,160,163],{"name":147,"orcid":148},"Anastasios Mazis","https:\u002F\u002Forcid.org\u002F0000-0002-5024-7234",{"name":150,"orcid":151},"Sarah N. Anderson","https:\u002F\u002Forcid.org\u002F0000-0002-1671-2286",{"name":153,"orcid":154},"Guilherme Ferreira Simiqueli","https:\u002F\u002Forcid.org\u002F0000-0002-2867-0255",{"name":156,"orcid":9},"Adam Barbeau",{"name":158,"orcid":159},"Sara B. Tirado","https:\u002F\u002Forcid.org\u002F0000-0003-0432-091X",{"name":161,"orcid":162},"Julien F. Linares","https:\u002F\u002Forcid.org\u002F0000-0002-0083-0974",{"name":164,"orcid":165},"Nathan D. Coles","https:\u002F\u002Forcid.org\u002F0000-0002-2008-3283","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fppj2.70100",{"tldr":168,"method":169,"finding":170,"direction":94,"opportunity":171},"用卫星影像替代无人机估算大豆成熟期，实现规模化育种表型分析。","利用高分辨率卫星影像与无人机影像的植被指数，构建逻辑回归模型预测成熟期。","卫星与无人机估算成熟期相关性高（R²达0.94），遗传力高（H²达0.98），小小区也有效。","卫星影像在小区育种中的精度受限于小区大小，可探索优化算法或结合多源数据提升小尺度精度。","2026-09-09T23:30:17.932682Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":9,"source_name":107,"source_url":176,"published_at":179,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":180,"score_detail":181,"sources":185,"tags":187,"primary_topic":191,"secondary_topics":192,"classification":198,"search_phrases":223,"slug":226,"view_count":227,"doi":228,"paper":229,"created_at":252},4998,"Unmanned aerial vehicle time‐series phenotyping illuminates genotype‐specific responses to off‐target dicamba exposure in soybean","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fppj2.70118","Abstract Dicamba, a synthetic auxin herbicide, is widely used for postemergence weed control. However, the high potential for volatility and spray drift raises concerns about damage to nontarget dicot crops lacking the dicamba monooxygenase transgene, which provides bioengineered resistance. Our objective was to evaluate the practical application of unmanned aerial vehicle (UAV)‐based multispectral imaging for quantifying off‐target dicamba injury, using non‐genetically modified (GM) soybean ( Glycine max (L.) Merr.) genotypes with known tolerant (T) and sensitive (S) responses to provide a reference framework. We conducted field experiments in 2021 and 2022 using varying dicamba rates. Temporal visual injury estimates (0–5 scale) were recorded approximately every 5 days for 6–8 weeks. Significant differences were detected between T and S lines, especially with 1\u002F500 single and weekly repeated treatments, which provided the largest separation. To augment the precision of visual injury assessment, UAV‐based multispectral imagery from each of multiple flights was used to calculate four vegetation indices (VIs): green leaf index (GLI), normalized excess green index (ExG2), normalized green red difference index (NGRDI), and red edge normalized difference vegetation index (RENDVI). These VIs captured differences between T and S lines, indicating dicamba injury can be quantified through remote sensing across contrasting soybean genotypes. Strong negative correlations were observed between visual injury scores and VI scores across treatments at each flight date, with overall correlations for GLI, ExG2, NGRDI, and RENDVI being −0.43, −0.43, −0.44, and −0.39 in 2021 and −0.54, −0.54, −0.64, and −0.57 in 2022. These results support the utility of UAV‐derived VIs for detecting and quantifying off‐target herbicide injury in soybean, facilitating scientific research efforts to develop non‐GM‐tolerant genotypes.","摘要 麦草畏（dicamba）是一种合成生长素类除草剂，广泛用于苗后杂草防除。然而，其高挥发性和喷雾飘移潜力引发了人们对非靶标双子叶作物的担忧，这些作物缺乏提供生物工程抗性的麦草畏单加氧酶（dicamba monooxygenase）转基因。本研究旨在评估基于无人机（UAV）的多光谱成像在量化非靶标麦草畏药害中的实际应用，利用对麦草畏具有已知耐受（T）和敏感（S）响应的非转基因（GM）大豆（Glycine max (L.) Merr.）基因型作为参考框架。我们于2021年和2022年开展了田间试验，使用不同的麦草畏剂量。在约6–8周内，每约5天记录一次时序视觉药害评估（0–5级）。在T和S品系之间检测到显著差异，尤其是在1\u002F500单次和每周重复处理下，差异最为明显。为提高视觉药害评估的精度，利用多次飞行获取的无人机多光谱影像计算了4种植被指数（VIs）：绿叶指数（GLI）、归一化超绿指数（ExG2）、归一化绿红差异指数（NGRDI）和红边归一化差异植被指数（RENDVI）。这些植被指数捕捉到了T和S品系之间的差异，表明麦草畏药害可通过遥感在不同大豆基因型间进行量化。在各飞行日期的所有处理中，视觉药害评分与植被指数评分之间均观察到强负相关，2021年GLI、ExG2、NGRDI和RENDVI的总体相关系数分别为−0.43、−0.43、−0.44和−0.39，2022年分别为−0.54、−0.54、−0.64和−0.57。这些结果支持无人机衍生植被指数在大豆非靶标除草剂药害检测和量化中的实用性，有助于推动开发非转基因耐受基因型的科学研究工作。","2026-10-14T00:00:00Z",77,{"impact":182,"substance":18,"depth":19,"authority":110,"freshness":183,"relevant":22,"comment":184},16,8,"该研究利用无人机多光谱时序表型定量评估大豆非靶标除草剂药害，方法新颖、数据扎实，对智慧农业遥感监测具有参考价值。",[186],{"name":107,"url":176},[27,30,188,189,190],"无人机遥感","植被指数","除草剂药害",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[193],{"code":128,"slug":194,"name":195,"path":196,"parent_code":197},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":199},{"candidates":200,"agricultural_evidence":46},[201,205,212,219],{"code":33,"score":17,"supported":46,"title":202,"abstract":203,"tags":204},[48],[30,48],[30],{"code":128,"score":206,"supported":46,"title":207,"abstract":208,"tags":211},11,[],[116,209,131,210],"无人机多光谱","multispectral",[188,189],{"code":213,"score":99,"supported":46,"title":214,"abstract":215,"tags":218},"F4",[],[216,217],"除草","weed control",[],{"code":52,"score":22,"supported":14,"title":220,"abstract":221,"tags":222},[],[],[27],[224,225],"无人机 多光谱 大豆 药害","麦草畏 飘移 大豆 遥感","无人机多光谱大豆药害-4998",4,"10.1002\u002Fppj2.70118",{"doi":228,"openalex_id":230,"authors":231,"venue":107,"cited_by_count":89,"oa_url":176,"card":247,"direction":94,"ingested_from":96},"W7221759072",[232,235,238,241,244],{"name":233,"orcid":234},"Seda Ozer","https:\u002F\u002Forcid.org\u002F0000-0002-1644-2394",{"name":236,"orcid":237},"Brian W. Diers","https:\u002F\u002Forcid.org\u002F0000-0003-3584-5495",{"name":239,"orcid":240},"Dean E. Riechers","https:\u002F\u002Forcid.org\u002F0000-0002-6081-5629",{"name":242,"orcid":243},"Caio Canella Vieira","https:\u002F\u002Forcid.org\u002F0000-0001-5428-3753",{"name":245,"orcid":246},"Alper Adak","https:\u002F\u002Forcid.org\u002F0000-0002-2737-8041",{"tldr":248,"method":249,"finding":250,"direction":94,"opportunity":251},"用无人机多光谱时序表型量化非转基因大豆受除草剂飘移药害的基因型差异。","2021-2022田间试验，无人机多光谱影像计算GLI、ExG2、NGRDI、R","植被指数与目测药害评分显著负相关，能区分耐性与敏感基因型，2022年相关性更强。","可拓展到多作物、多除草剂飘移的无人机早期诊断，并融合机器学习构建药害分级预警模型。","2026-10-15T23:30:20.537887Z",{"id":254,"title":255,"url":256,"summary":257,"summary_zh":258,"content":9,"source_name":259,"source_url":256,"published_at":260,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":261,"sources":263,"tags":265,"primary_topic":269,"secondary_topics":270,"classification":271,"search_phrases":298,"slug":301,"view_count":227,"doi":302,"paper":303,"created_at":330},3786,"A cost-effective approach of building plant digital twin combining plant phenotyping and growth models: A case study on rapeseed plants","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128341","High-throughput phenotyping platforms enable dense temporal monitoring of plant growth, yet field-based image collection remains labor-intensive with sparse sampling intervals, leading to missing information on intermediate developmental stages. Functional–structural plant models (FSPMs) offer dynamic growth simulation but often lack realistic 3D architecture derived from actual plants. This study proposes a lightweight framework that bridges sparse phenotyping time points by integrating 3D reconstruction with the GreenLab plant growth model, enabling the construction of plant digital twins for rapeseed ( Brassica napus L.). Multi-angle smartphone images of four rapeseed cultivars at two key growth stages were used to reconstruct 3D plant architecture via 3D Gaussian Splatting (3DGS). Leaves were segmented from the point cloud to calculate area. GreenLab source and sink parameters were estimated from organ-level fresh weights (leaves, internodes, and roots) measured at two growth stages. The goodness–of–fit was high, with weighted sum of squares (WSS) between 5.63 and 9.33 and Pearson correlation coefficients ( r ) ranging from 0.93 to 0.95 across the four cultivars. The calibrated model reproduced leaf areas consistent with 3D reconstructed static structures. Reconstructed organ geometries were used for visualization and functional analysis of light interception. The integrated model thus recovered the complete dynamic development and growth trajectory between sparse sampling points. Temporal hold-out validation showed reasonable agreement between reconstructed leaf area and length–width–derived reference estimates, with a single-leaf mean absolute percentage error (MAPE) of 18.42%. Light interception analysis revealed cultivar-specific differences in canopy light use efficiency attributable to contrasting morphological architectures. Simulated and reconstructed canopy light interception showed strong agreement ( r = 0.98 ). This framework provides a cost-effective approach to reconstructing plant growth dynamics and evaluating light capture characteristics from minimal field data. It offers practical utility for breeders in cultivar evaluation and for optimizing environmental control strategies in precision agriculture.","高通量表型平台能够实现对植物生长的高密度时间监测，但田间图像采集仍然劳动密集且采样间隔稀疏，导致中间发育阶段的信息缺失。功能–结构植物模型（FSPMs）能够进行动态生长模拟，但通常缺乏源自实际植物的真实三维结构。本研究提出了一种轻量级框架，通过整合三维重建与GreenLab植物生长模型来桥接稀疏表型时间点，从而为甘蓝型油菜（Brassica napus L.）构建植物数字孪生。利用四个油菜品种在两个关键生长阶段的多角度智能手机图像，通过三维高斯泼溅（3DGS）重建植物三维结构。从点云中分割叶片以计算叶面积。基于两个生长阶段测定的器官水平鲜重（叶片、节间和根系）估计GreenLab源和库参数。拟合优度较高，四个品种的加权平方和（WSS）在5.63至9.33之间，皮尔逊相关系数（r）在0.93至0.95之间。校准后的模型再现的叶面积与三维重建的静态结构一致。重建的器官几何结构用于可视化和光截获功能分析。该集成模型由此恢复了稀疏采样点之间完整的动态发育和生长轨迹。时间留出验证表明，重建叶面积与基于长宽推导的参考估计之间具有合理的一致性，单叶平均绝对百分比误差（MAPE）为18.42%。光截获分析揭示了由于形态结构差异导致的品种特异性冠层光利用效率差异。模拟与重建的冠层光截获表现出高度一致性（r = 0.98）。该框架提供了一种经济有效的方法，可从最少的田间数据重建植物生长动态并评估光捕获特征。它为育种者进行品种评估以及优化精准农业中的环境调控策略提供了实用价值。","European Journal of Agronomy","2026-09-28T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":262},"该研究提出结合3D高斯泼溅与GreenLab模型的低成本植物数字孪生框架，在油菜上验证可靠，对智慧育种与精准农业有实用价值，值得精选。",[264],{"name":259,"url":256},[27,266,31,267,268],"油菜","作物生长模型","植物数字孪生",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":272},{"candidates":273,"agricultural_evidence":46},[274,282,287,293],{"code":52,"score":19,"supported":46,"title":275,"abstract":277,"tags":281},[276],"digital twin",[278,279,280],"精准农业","数字孪生","precision agriculture",[27],{"code":33,"score":17,"supported":46,"title":283,"abstract":285,"tags":286},[284],"rapeseed",[266,284],[266],{"code":288,"score":141,"supported":46,"title":289,"abstract":290,"tags":292},"B1",[],[291],"甘蓝",[],{"code":294,"score":22,"supported":14,"title":295,"abstract":296,"tags":297},"I2",[],[],[267],[299,300],"油菜 植物数字孪生 表型","GreenLab 生长模型 油菜","油菜植物数字孪生表型-3786","10.1016\u002Fj.eja.2026.128341",{"doi":302,"openalex_id":304,"authors":305,"venue":259,"cited_by_count":89,"oa_url":256,"card":325,"direction":94,"ingested_from":96},"W7214617633",[306,309,312,314,316,318,320,322],{"name":307,"orcid":308},"Xiujuan Wang","https:\u002F\u002Forcid.org\u002F0000-0001-8327-1735",{"name":310,"orcid":311},"Bowen Zheng","https:\u002F\u002Forcid.org\u002F0009-0008-1141-7158",{"name":313,"orcid":9},"Zhiqing Liu",{"name":315,"orcid":9},"Cuiping Bu",{"name":317,"orcid":9},"Jing Hua",{"name":319,"orcid":9},"Haoyu Wang",{"name":321,"orcid":9},"Qirun Huo",{"name":323,"orcid":324},"Mengzhen Kang","https:\u002F\u002Forcid.org\u002F0000-0002-5016-1772",{"tldr":326,"method":327,"finding":328,"direction":94,"opportunity":329},"结合手机3D重建与GreenLab生长模型，低成本构建油菜植株数字孪生。","多角度手机图像3D高斯泼溅重建，GreenLab源库参数标定与光截获分析。","模型拟合优度高（r=0.93-0.95），可恢复稀疏采样间生长动态并评估光截获。","可探索将轻量级数字孪生框架扩展至更多作物及田间高通量场景，降低表型监测成本。","2026-09-29T23:30:23.763490Z",{"id":332,"title":333,"url":334,"summary":335,"summary_zh":9,"content":9,"source_name":336,"source_url":9,"published_at":337,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":338,"score_detail":339,"sources":341,"tags":343,"primary_topic":346,"secondary_topics":347,"classification":348,"search_phrases":370,"slug":373,"view_count":60,"doi":9,"paper":374,"created_at":383},3600,"Development of an Adaptive Sprayer Control System for UAV-Based Spot Spraying in Soybean（基于植被覆盖率的自适应无人机大豆点喷控制系统研发）","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260924002257098.htm","本研究开发了一套集成无人机的自适应喷雾控制系统，将冠层感知与基于阈值的点喷技术相连接。研究人员利用大豆单独覆盖度作为局部杂草斑块检测的基准基线，室内与田间估算结果的R²值分别为0.91和0.69。在播种后24天，采用25%的阈值可将潜在误喷激活率限制在4.8%。室内测试中，自动喷雾执行准确率达到97.3%。研究指出杂草可导致大豆超过30%的产量损失，全球年经济损失达330亿美元。无人机喷洒避免了与土壤的直接接触，减少了土壤压实和作物损伤的风险，提高了关键除草窗口期的操作灵活性。","Smart Agricultural Technology 7.1 2026-09-24","2026-09-24T00:00:00Z",79,{"impact":19,"substance":18,"depth":19,"authority":110,"freshness":183,"relevant":22,"comment":340},"该研究提出基于冠层覆盖度的无人机自适应点喷控制方法，数据详实、结论可靠，对精准植保技术研发具有参考价值，值得进入每日精选。",[342],{"name":336,"url":334},[27,278,30,344,345],"无人机植保","杂草管理",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":349},{"candidates":350,"agricultural_evidence":46},[351,356,360,366],{"code":33,"score":352,"supported":46,"title":353,"abstract":354,"tags":355},20,[30,48],[30],[30],{"code":213,"score":227,"supported":46,"title":357,"abstract":358,"tags":359},[],[216],[344],{"code":361,"score":141,"supported":46,"title":362,"abstract":363,"tags":365},"H2",[],[364],"土壤",[],{"code":52,"score":60,"supported":14,"title":367,"abstract":368,"tags":369},[],[],[27,278],[371,372],"无人机 大豆 点喷","自适应喷雾 大豆 杂草","无人机大豆点喷-3600",{"doi":9,"openalex_id":9,"authors":375,"venue":9,"cited_by_count":89,"oa_url":9,"card":376,"direction":380,"ingested_from":382},[],{"tldr":377,"method":378,"finding":379,"direction":380,"opportunity":381},"研发基于冠层覆盖率的无人机自适应点喷系统，实现大豆田间精准除草。","用无人机冠层感知与阈值点喷控制，室内外估算覆盖度R²为0.91和0.69。","25%阈值下误喷激活率仅4.8%，室内自动喷雾执行准确率达97.3%。","智慧农业 \u002F 农业物联网","可探索多作物、多生育期自适应阈值与杂草识别模型融合，提升田间鲁棒性。","agent","2026-09-27T00:05:18.158668Z",{"id":385,"title":386,"url":387,"summary":388,"summary_zh":389,"content":9,"source_name":390,"source_url":387,"published_at":337,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":391,"score_detail":392,"sources":394,"tags":396,"primary_topic":399,"secondary_topics":400,"classification":402,"search_phrases":417,"slug":420,"view_count":141,"doi":421,"paper":422,"created_at":440},3501,"A Dual-Phenological-Characteristic Weighting Method to Reconcile Time Discrepancies in Soybean Phenology Estimation from MODIS NDVI Time Series","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193300","Accurate large-scale monitoring of crop phenology is essential for optimizing agricultural management. Remote sensing has been widely used for estimating crop phenological stages, yet time discrepancies often exist between remotely sensed phenological metrics and ground-observed growth stages. Moreover, phenological parameters derived from different characterization models exhibit varying degrees of deviation from field observations. The primary goal of this study was to develop a novel method that fully exploits the deviation patterns of diverse phenological parameters to enhance the accuracy of soybean phenology retrieval. To this end, we extracted 11 phenological parameters for six key growth stages—emerged, blooming, pod-setting, turning yellow, dropping leaf, and harvest—of soybean across 16 U.S. states using MODIS NDVI (normalized difference vegetation index) time-series data from 2000 to 2020, employing GU-, curvature-, and derivative-based phenological modeling methods. The study design centered on proposing a dual-phenological-characteristic weighting (DPCW) method that leverages the deviation features of different phenological parameters relative to ground-observed growth stages, generating composite phenological characteristics by pairing two distinct parameters. The key innovation of this paper is the use of dual-feature weighting to improve the correspondence between satellite-derived phenometrics and field observations, offering an alternative to conventional phenological estimation. The results demonstrated that the optimal DPCW-based combinations for the six growth stages were SOS (start of season) and GREEN, SOS and POS (peak of season), MATURITY and POS, EOS (end of season) and SENES (senescence), RD (recession date) and DD (downturn date), and EOS and DORM (dormancy), respectively. The coefficient of determination (R2) between the retrieved transition dates and ground observations exceeded 0.65 for most stages, with the emerged stage improving to 0.47 from 0.052 and 0.357 of the unadjusted and offset-adjusted benchmarks. The average root mean square error (RMSE) was less than 5 days in most cases, representing a reduction of over 40%, with the most substantial improvement at the turning yellow stage, where RMSE dropped from 12.8 days to 2.8 days. A strength of this study lies in its multi-state, multi-decade validation, demonstrating the robustness and temporal consistency of the DPCW method within the major U.S. soybean-growing region. However, a limitation is that the method’s performance may vary with different satellite sensors or crop types, warranting further investigation. The proposed approach is expected to enhance the accuracy of remote sensing-based crop phenology monitoring and offers an effective alternative for calibrating remotely sensed phenological parameters.","准确的大尺度作物物候监测对于优化农业管理至关重要。遥感已被广泛用于估算作物物候阶段，但遥感物候指标与地面观测生育阶段之间常存在时间差异。此外，不同特征化模型衍生的物候参数与田间观测之间存在不同程度的偏差。本研究的主要目标是开发一种新方法，充分利用多种物候参数的偏差模式，以提高大豆物候反演精度。为此，我们利用2000—2020年MODIS NDVI（归一化差异植被指数）时间序列数据，采用基于GU、曲率和导数的方法提取了美国16个州大豆六个关键生育阶段——出苗、开花、结荚、黄化、落叶和收获——的11个物候参数。研究设计的核心是提出一种双物候特征加权（dual-phenological-characteristic weighting，DPCW）方法，该方法利用不同物候参数相对于地面观测生育阶段的偏差特征，通过配对两个不同参数生成复合物候特征。本文的关键创新在于利用双特征加权提高卫星衍生物候指标与田间观测之间的对应关系，为传统物候估算提供了一种替代方案。结果表明，六个生育阶段基于DPCW的最优组合分别为SOS（生长季开始）与GREEN、SOS与POS（生长季峰值）、MATURITY与POS、EOS（生长季结束）与SENES（衰老）、RD（衰退日期）与DD（下降日期）以及EOS与DORM（休眠）。反演得到的转换日期与地面观测之间的决定系数（R²）在大多数阶段超过0.65，其中出苗阶段从基准的0.052和偏移调整后的0.357提高至0.47。大多数情况下平均均方根误差（RMSE）小于5天，降幅超过40%，其中黄化阶段改善最为显著，RMSE从12.8天降至2.8天。本研究的一个优势在于其多州、多年代际验证，证明了DPCW方法在美国主要大豆种植区内的稳健性和时间一致性。","Remote Sensing",81,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":393},"提出双物候特征加权方法，用MODIS NDVI长时序数据校正大豆物候遥感估算偏差，方法新颖、验证扎实，对农情遥感监测有参考价值。",[395],{"name":390,"url":387},[27,30,397,116,398],"农情监测","作物表型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[401],{"code":128,"slug":194,"name":195,"path":196,"parent_code":197},{"status":40,"locked":14,"version":41,"method":42,"evidence":403},{"candidates":404,"agricultural_evidence":46},[405,409,413],{"code":33,"score":17,"supported":46,"title":406,"abstract":407,"tags":408},[48],[30,48],[30],{"code":128,"score":21,"supported":46,"title":410,"abstract":411,"tags":412},[],[116,131],[398,397,116],{"code":52,"score":22,"supported":14,"title":414,"abstract":415,"tags":416},[],[],[27],[418,419],"MODIS NDVI 大豆 物候","美国大豆 遥感 物候监测","MODISNDVI大豆物候-3501","10.3390\u002Frs18193300",{"doi":421,"openalex_id":423,"authors":424,"venue":390,"cited_by_count":89,"oa_url":387,"card":435,"direction":94,"ingested_from":96},"W7214203102",[425,427,430,433],{"name":426,"orcid":9},"Qiuxiang Yi",{"name":428,"orcid":429},"Siting Chen","https:\u002F\u002Forcid.org\u002F0000-0003-3468-9320",{"name":431,"orcid":432},"Fumin Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5078-358X",{"name":434,"orcid":9},"Qinyan Zhu",{"tldr":436,"method":437,"finding":438,"direction":94,"opportunity":439},"提出双物候特征加权法，校正MODIS NDVI大豆物候估计与地面观测的时间偏差。","用MODIS NDVI 2000-2020数据，结合GU、曲率、导数三类物候模型","多数生育期R²超0.65，RMSE多小于5天，降幅超40%，转黄期RMSE从12.8天降至2.8天。","可将该加权校正思路迁移到其他作物与多源遥感数据，并探索自适应权重与深度学习融合的物候反演。","2026-09-25T23:30:31.075499Z",{"id":442,"title":443,"url":444,"summary":445,"summary_zh":446,"content":9,"source_name":447,"source_url":444,"published_at":448,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":449,"score_detail":450,"sources":453,"tags":455,"primary_topic":459,"secondary_topics":460,"classification":465,"search_phrases":485,"slug":488,"view_count":141,"doi":489,"paper":490,"created_at":518},3485,"Detection of Red Crown Rot of Soybean in Illinois Fields Using High-Resolution Satellite Imagery and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.09.22.753594","Red crown rot (RCR), caused by Calonectria ilicicola, is an emerging soybean disease in the U.S. Midwest for which scalable approaches to characterize within-field disease distribution are lacking. This study evaluated high-resolution PlanetScope satellite imagery for mapping RCR-affected soybean canopies across 15 commercial fields in Illinois surveyed during the 2024 and 2025 growing seasons. A total of 2,921 georeferenced canopy plots were classified as asymptomatic or RCR-affected and paired with six multispectral bands and seven vegetation indices. Spectral differences between classes were evaluated using linear mixed-effects models, and seven machine-learning classifiers representing linear, tree-based, neural-network, kernel, and probabilistic approaches were compared using spatially independent leave-one-field-out cross-validation. RCR-affected canopies exhibited increased reflectance in the visible and red-edge regions, reduced near-infrared reflectance, and lower vegetation-index values relative to asymptomatic canopies. All classifiers showed strong discrimination, with ROC-AUC values ranging from 0.963 to 0.982. Regularized logistic regression achieved the highest overall performance, with an accuracy of 0.945, balanced accuracy of 0.945, F1-score of 0.948, and ROC-AUC of 0.982 at the optimized decision threshold. Permutation analysis identified EVI, NDVI, and red reflectance as the most influential predictors across representative model architectures. Satellite-derived probability and classification maps generally corresponded with symptomatic canopy patterns observed in high-resolution UAV imagery, although mixed pixels reduced precision near disease-patch boundaries. These results demonstrate the potential of high-resolution satellite imagery for within-field mapping of RCR-associated canopy symptoms across independent commercial soybean fields.","由冬青丽赤壳菌（Calonectria ilicicola）引起的红冠腐病（RCR）是美国中西部一种新发大豆病害，目前尚缺乏可规模化表征田块内病害分布的方法。本研究评估了高分辨率PlanetScope卫星影像在2024年和2025年生长季对伊利诺伊州15块商业田块中受RCR影响的大豆冠层的制图能力。共有2,921个地理参考冠层样区被分类为无症状或受RCR影响，并与6个多光谱波段和7个植被指数配对。采用线性混合效应模型评估类别间的光谱差异，并通过空间独立的留一田块交叉验证比较了7种机器学习分类器，涵盖线性、基于树、神经网络、核函数和概率方法。与无症状冠层相比，受RCR影响的冠层在可见光和红边区域反射率增加，近红外反射率降低，植被指数值较低。所有分类器均表现出较强的判别能力，ROC-AUC值范围为0.963至0.982。正则化逻辑回归在优化决策阈值下取得了最高的整体性能，准确率为0.945，平衡准确率为0.945，F1分数为0.948，ROC-AUC为0.982。置换分析表明，在代表性模型架构中，EVI、NDVI和红光反射率是最具影响力的预测变量。卫星衍生的概率图和分类图总体上与高分辨率无人机影像中观察到的症状冠层模式一致，尽管混合像元降低了病害斑块边界附近的精度。这些结果证明了高分辨率卫星影像在独立商业大豆田块中对RCR相关冠层症状进行田块内制图的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-09-23T00:00:00Z",76,{"impact":182,"substance":18,"depth":19,"authority":451,"freshness":183,"relevant":22,"comment":452},12,"基于高分辨率卫星影像与机器学习实现大豆红冠腐病田间制图，方法扎实、数据规模可观，对作物病害遥感监测有参考价值。",[454],{"name":447,"url":444},[27,456,30,457,458],"机器学习","遥感监测","病害识别",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[461],{"code":294,"slug":462,"name":463,"path":464,"parent_code":197},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":40,"locked":14,"version":41,"method":42,"evidence":466},{"candidates":467,"agricultural_evidence":46},[468,472,477,481],{"code":33,"score":17,"supported":46,"title":469,"abstract":470,"tags":471},[48],[30,48],[30],{"code":294,"score":110,"supported":46,"title":473,"abstract":475,"tags":476},[474],"machine learning",[456],[456,458],{"code":128,"score":227,"supported":46,"title":478,"abstract":479,"tags":480},[],[210],[457],{"code":52,"score":22,"supported":14,"title":482,"abstract":483,"tags":484},[],[],[27],[486,487],"伊利诺伊 大豆 红冠腐病 卫星遥感","PlanetScope 大豆 病害 机器学习","伊利诺伊大豆红冠腐病卫星遥感-3485","10.64898\u002F2026.09.22.753594",{"doi":489,"openalex_id":491,"authors":492,"venue":447,"cited_by_count":89,"oa_url":512,"card":513,"direction":94,"ingested_from":96},"W7214126630",[493,496,499,501,503,506,509],{"name":494,"orcid":495},"Bruno Daniel Pugliese","https:\u002F\u002Forcid.org\u002F0009-0000-4795-4248",{"name":497,"orcid":498},"Juan Andrés Paredes","https:\u002F\u002Forcid.org\u002F0000-0002-3967-0197",{"name":500,"orcid":9},"Andres Fabian Ruiz",{"name":502,"orcid":9},"Norman Denis Bowman",{"name":504,"orcid":505},"Elhan S. Ersoz","https:\u002F\u002Forcid.org\u002F0000-0001-6930-6946",{"name":507,"orcid":508},"Nicolas Federico Martin","https:\u002F\u002Forcid.org\u002F0000-0002-1587-665X",{"name":510,"orcid":511},"Boris X. Camiletti","https:\u002F\u002Forcid.org\u002F0000-0002-1492-7988","https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F09\u002F23\u002F2026.09.22.753594.full.pdf",{"tldr":514,"method":515,"finding":516,"direction":94,"opportunity":517},"利用高分辨率卫星影像和机器学习在大豆田中检测红冠腐病。","PlanetScope多光谱影像、植被指数与7种机器学习分类器，留一田块交叉验证","各分类器ROC-AUC达0.963-0.982，正则化逻辑回归最优，EVI、NDVI和红波段最重要。","可探索多时相卫星影像与无人机融合，提升病害斑块边界混合像元识别精度。","2026-09-25T23:30:22.839600Z"]