[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1990":3,"related-1990":101},{"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":37,"classification":38,"search_phrases":62,"slug":65,"view_count":66,"doi":67,"paper":68,"created_at":100},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。",null,"The Plant Phenome Journal","2026-09-07T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,7,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","育种","大豆","遥感","表型鉴定",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,57],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},15,true,[48],"soybean",[28,48],[28],{"code":52,"score":20,"supported":46,"title":53,"abstract":54,"tags":56},"I3",[],[29,55],"remote sensing",[29],{"code":58,"score":21,"supported":14,"title":59,"abstract":60,"tags":61},"I1",[],[],[26],[63,64],"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":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":91,"oa_url":92,"card":93,"direction":97,"ingested_from":99},"W7211946346",[71,74,77,80,82,85,88],{"name":72,"orcid":73},"Anastasios Mazis","https:\u002F\u002Forcid.org\u002F0000-0002-5024-7234",{"name":75,"orcid":76},"Sarah N. Anderson","https:\u002F\u002Forcid.org\u002F0000-0002-1671-2286",{"name":78,"orcid":79},"Guilherme Ferreira Simiqueli","https:\u002F\u002Forcid.org\u002F0000-0002-2867-0255",{"name":81,"orcid":9},"Adam Barbeau",{"name":83,"orcid":84},"Sara B. Tirado","https:\u002F\u002Forcid.org\u002F0000-0003-0432-091X",{"name":86,"orcid":87},"Julien F. Linares","https:\u002F\u002Forcid.org\u002F0000-0002-0083-0974",{"name":89,"orcid":90},"Nathan D. Coles","https:\u002F\u002Forcid.org\u002F0000-0002-2008-3283",0,"https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fppj2.70100",{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"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。","农业遥感与作物表型","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。","openalex","2026-09-09T23:30:17.932682Z",{"total":102,"page":21,"page_size":102,"items":103},6,[104,168,235,303,379,447],{"id":105,"title":106,"url":107,"summary":108,"summary_zh":109,"content":9,"source_name":110,"source_url":107,"published_at":111,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":112,"score_detail":113,"sources":117,"tags":119,"primary_topic":122,"secondary_topics":123,"classification":129,"search_phrases":144,"slug":147,"view_count":66,"doi":148,"paper":149,"created_at":167},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","2026-09-24T00:00:00Z",81,{"impact":17,"substance":18,"depth":17,"authority":114,"freshness":115,"relevant":21,"comment":116},14,9,"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。",[118],{"name":110,"url":107},[26,28,120,29,121],"农情监测","作物表型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[124],{"code":52,"slug":125,"name":126,"path":127,"parent_code":128},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":130},{"candidates":131,"agricultural_evidence":46},[132,136,140],{"code":32,"score":45,"supported":46,"title":133,"abstract":134,"tags":135},[48],[28,48],[28],{"code":52,"score":115,"supported":46,"title":137,"abstract":138,"tags":139},[],[29,55],[121,120,29],{"code":58,"score":21,"supported":14,"title":141,"abstract":142,"tags":143},[],[],[26],[145,146],"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":148,"openalex_id":150,"authors":151,"venue":110,"cited_by_count":91,"oa_url":107,"card":162,"direction":97,"ingested_from":99},"W7214203102",[152,154,157,160],{"name":153,"orcid":9},"Qiuxiang Yi",{"name":155,"orcid":156},"Siting Chen","https:\u002F\u002Forcid.org\u002F0000-0003-3468-9320",{"name":158,"orcid":159},"Fumin Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5078-358X",{"name":161,"orcid":9},"Qinyan Zhu",{"tldr":163,"method":164,"finding":165,"direction":97,"opportunity":166},"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":169,"title":170,"url":171,"summary":172,"summary_zh":173,"content":9,"source_name":174,"source_url":171,"published_at":175,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":176,"sources":178,"tags":180,"primary_topic":183,"secondary_topics":184,"classification":185,"search_phrases":196,"slug":199,"view_count":200,"doi":201,"paper":202,"created_at":234},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。","BMC Plant Biology","2026-09-12T00:00:00Z",{"impact":45,"substance":18,"depth":17,"authority":114,"freshness":115,"relevant":21,"comment":177},"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。",[179],{"name":174,"url":171},[26,181,27,28,182],"设施农业","表型组学",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":186},{"candidates":187,"agricultural_evidence":46},[188,192],{"code":32,"score":45,"supported":46,"title":189,"abstract":190,"tags":191},[48],[28,48],[28],{"code":58,"score":21,"supported":14,"title":193,"abstract":194,"tags":195},[],[],[26],[197,198],"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":201,"openalex_id":203,"authors":204,"venue":174,"cited_by_count":91,"oa_url":171,"card":229,"direction":97,"ingested_from":99},"W7212354617",[205,208,210,213,215,217,219,221,223,225,227],{"name":206,"orcid":207},"Fan Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-8576-0273",{"name":209,"orcid":9},"Xiao Cui",{"name":211,"orcid":212},"Kanglin Liu","https:\u002F\u002Forcid.org\u002F0000-0001-8747-5022",{"name":214,"orcid":9},"Huilong Hong",{"name":216,"orcid":9},"Xin Su",{"name":218,"orcid":9},"Yajun Xiong",{"name":220,"orcid":9},"Sawaira Jadoon",{"name":222,"orcid":9},"Huan Yu",{"name":224,"orcid":9},"Yijie Chen",{"name":226,"orcid":9},"Qiu Lijuan",{"name":228,"orcid":9},"Jun Wang",{"tldr":230,"method":231,"finding":232,"direction":97,"opportunity":233},"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， ","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。","2026-09-13T23:30:19.792952Z",{"id":236,"title":237,"url":238,"summary":239,"summary_zh":240,"content":9,"source_name":241,"source_url":238,"published_at":242,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":243,"score_detail":244,"sources":248,"tags":250,"primary_topic":252,"secondary_topics":253,"classification":255,"search_phrases":275,"slug":278,"view_count":66,"doi":279,"paper":280,"created_at":302},1444,"An Improved Transformer-KAN Model for Soybean Mapping Based on Multi-Temporal Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16171895","Accurate and transferable soybean mapping is essential for agricultural monitoring and area verification, yet conventional Transformer models can be limited in modeling complex nonlinear phenological relationships and maintaining training stability. This study proposes an improved Transformer-KAN model for multi-temporal Sentinel-2 data. Temporal positional encoding, multi-head self-attention with relative positional bias, and a Pre-LayerNorm residual structure are introduced to strengthen phenological sequence modeling, while FastKAN replaces the conventional MLP-based feed-forward network to enhance nonlinear feature representation. The model was trained using 2023 samples from Hailun City and directly evaluated in Bozhou, McLean, and Cass without retraining or fine-tuning. Cross-year transferability was further evaluated by applying the Hailun-trained model to data from Bozhou and McLean from 2021 to 2025. The proposed model achieved overall accuracies of 0.975, 0.983, 0.958, and 0.966 in Hailun, Bozhou, McLean, and Cass, respectively, with corresponding Kappa coefficients of 0.895, 0.887, 0.883, and 0.916. Complexity analysis showed that Transformer-KAN required 0.8142 M parameters and 1.1967 M FLOPs, with an average inference time of 3.0191 ms per sample, compared with 0.6130 M parameters, 0.7971 M FLOPs, and 1.4009 ms per sample for the conventional Transformer, indicating that the improved feature representation was accompanied by increased computational complexity. Cross-year classification performance remained generally high from 2021 to 2025, although interannual variations were observed due to differences in crop growth conditions, phenological timing, and image acquisition quality. Discrepancies between remote-sensing-derived and officially reported soybean areas were mainly related to differences in statistical definitions and residual classification uncertainties, rather than model transferability. Overall, Transformer-KAN provides accurate and transferable soybean mapping and can serve as a spatially explicit complement to official agricultural statistics.","准确且可迁移的大豆制图对于农业监测和面积核查至关重要，然而传统Transformer模型在建模复杂非线性物候关系及维持训练稳定性方面可能存在局限。本研究提出一种改进的Transformer-KAN模型，用于多时相Sentinel-2数据。引入时间位置编码、带相对位置偏置的多头自注意力机制以及Pre-LayerNorm残差结构，以增强物候序列建模能力，同时采用FastKAN替代传统基于MLP的前馈网络，以提升非线性特征表达能力。该模型利用2023年海伦市样本进行训练，并在北州、麦克莱恩和卡斯直接评估，无需重新训练或微调。跨年度迁移能力进一步通过将海伦训练模型应用于2021至2025年北州和麦克莱恩的数据进行评估。所提模型在海伦、北州、麦克莱恩和卡斯的总体精度分别为0.975、0.983、0.958和0.966，对应的Kappa系数分别为0.895、0.887、0.883和0.916。复杂度分析表明，Transformer-KAN需要0.8142 M参数和1.1967 M FLOPs，平均每样本推理时间为3.0191 ms，而传统Transformer为0.6130 M参数、0.7971 M FLOPs和每样本1.4009 ms，说明改进的特征表示伴随着计算复杂度的增加。2021至2025年跨年度分类性能总体保持较高水平，但由于作物生长条件、物候时序和影像获取质量的差异，存在年际波动。遥感反演大豆面积与官方统计面积之间的差异主要与统计口径差异和残余分类不确定性有关，而非模型迁移能力所致。总体而言，Transformer-KAN能够提供准确且可迁移的大豆制图结果，可作为官方农业统计的空间显式补充。","Agriculture","2026-09-01T00:00:00Z",79,{"impact":17,"substance":18,"depth":245,"authority":246,"freshness":20,"relevant":21,"comment":247},20,12,"提出改进的Transformer-KAN模型用于大豆遥感制图，跨区域跨年份验证精度高，对农业监测有实质贡献。",[249],{"name":241,"url":238},[26,251,28,29],"农业人工智能",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[254],{"code":52,"slug":125,"name":126,"path":127,"parent_code":128},{"status":39,"locked":14,"version":40,"method":41,"evidence":256},{"candidates":257,"agricultural_evidence":46},[258,262,266,270],{"code":32,"score":45,"supported":46,"title":259,"abstract":260,"tags":261},[48],[28,48],[28],{"code":52,"score":246,"supported":46,"title":263,"abstract":264,"tags":265},[55],[29],[29],{"code":58,"score":21,"supported":14,"title":267,"abstract":268,"tags":269},[],[],[26],{"code":271,"score":21,"supported":14,"title":272,"abstract":273,"tags":274},"I2",[],[],[251],[276,277],"农业人工智能 智慧农业 大豆 遥感","农业人工智能 智慧农业","农业人工智能智慧农业大豆遥感-1444","10.3390\u002Fagriculture16171895",{"doi":279,"openalex_id":281,"authors":282,"venue":241,"cited_by_count":91,"oa_url":238,"card":297,"direction":97,"ingested_from":99},"W7204914742",[283,286,288,291,294],{"name":284,"orcid":285},"Chulin Pan","https:\u002F\u002Forcid.org\u002F0000-0003-3976-465X",{"name":287,"orcid":9},"Jiachen Ju",{"name":289,"orcid":290},"Hongpeng Guo","https:\u002F\u002Forcid.org\u002F0000-0003-0625-8929",{"name":292,"orcid":293},"Yufeng Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-8783-9786",{"name":295,"orcid":296},"Shuang Xu","https:\u002F\u002Forcid.org\u002F0000-0002-3470-0609",{"tldr":298,"method":299,"finding":300,"direction":97,"opportunity":301},"提出改进的Transformer-KAN模型，基于多时相遥感数据实现高精度、可迁移的大豆种植区制图。","结合时间位置编码、相对位置偏置、Pre-LayerNorm和FastKAN替换M","模型在四个区域精度达0.958-0.983，跨年迁移稳定，但计算复杂度高于传统Transformer","可探索降低模型复杂度或轻量化设计，以平衡精度与效率，并研究跨区域物候差异对迁移的影响。","2026-09-02T23:30:53.720188Z",{"id":304,"title":305,"url":306,"summary":307,"summary_zh":308,"content":9,"source_name":10,"source_url":306,"published_at":309,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":310,"score_detail":311,"sources":315,"tags":317,"primary_topic":321,"secondary_topics":322,"classification":324,"search_phrases":349,"slug":352,"view_count":353,"doi":354,"paper":355,"created_at":378},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":312,"substance":18,"depth":17,"authority":19,"freshness":313,"relevant":21,"comment":314},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。",[316],{"name":10,"url":306},[26,28,318,319,320],"无人机遥感","植被指数","除草剂药害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[323],{"code":52,"slug":125,"name":126,"path":127,"parent_code":128},{"status":39,"locked":14,"version":40,"method":41,"evidence":325},{"candidates":326,"agricultural_evidence":46},[327,331,338,345],{"code":32,"score":45,"supported":46,"title":328,"abstract":329,"tags":330},[48],[28,48],[28],{"code":52,"score":332,"supported":46,"title":333,"abstract":334,"tags":337},11,[],[29,335,55,336],"无人机多光谱","multispectral",[318,319],{"code":339,"score":102,"supported":46,"title":340,"abstract":341,"tags":344},"F4",[],[342,343],"除草","weed control",[],{"code":58,"score":21,"supported":14,"title":346,"abstract":347,"tags":348},[],[],[26],[350,351],"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":354,"openalex_id":356,"authors":357,"venue":10,"cited_by_count":91,"oa_url":306,"card":373,"direction":97,"ingested_from":99},"W7221759072",[358,361,364,367,370],{"name":359,"orcid":360},"Seda Ozer","https:\u002F\u002Forcid.org\u002F0000-0002-1644-2394",{"name":362,"orcid":363},"Brian W. Diers","https:\u002F\u002Forcid.org\u002F0000-0003-3584-5495",{"name":365,"orcid":366},"Dean E. Riechers","https:\u002F\u002Forcid.org\u002F0000-0002-6081-5629",{"name":368,"orcid":369},"Caio Canella Vieira","https:\u002F\u002Forcid.org\u002F0000-0001-5428-3753",{"name":371,"orcid":372},"Alper Adak","https:\u002F\u002Forcid.org\u002F0000-0002-2737-8041",{"tldr":374,"method":375,"finding":376,"direction":97,"opportunity":377},"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":380,"title":381,"url":382,"summary":383,"summary_zh":384,"content":9,"source_name":385,"source_url":382,"published_at":386,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":243,"score_detail":387,"sources":389,"tags":391,"primary_topic":395,"secondary_topics":396,"classification":397,"search_phrases":419,"slug":422,"view_count":91,"doi":423,"paper":424,"created_at":446},4325,"Precision agriculture reveals crop-yield footprints associated with distance to natural-seminatural habitats","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104997","Context Modern agricultural expansion has simplified landscapes and reduced natural and semi-natural habitats (NSNH), potentially disrupting ecological processes supporting crop production. Many of these processes are distance-dependent, potentially leaving spatial footprints in crop yield that vary with landscape context. Yet, their influence on crop yields at sub-field scales remains poorly understood. Precision agriculture offers opportunities to address this gap. Objective To evaluate how sunflower distance-to-NSNH - yield relationships vary across landscape contexts within contrasting landscape settings using precision agriculture data. Methods High-resolution yield-monitor data (∼700,000 observations) from 54 yield maps (Pampas, Argentina) were combined with landscape metrics derived from land-cover products, and distance-yield relationships were analysed using mixed-effects models. These relationships were evaluated across gradients of NSNH cover (landscape context) and between zones representing contrasting landscape settings. Results and conclusions Distance-yield relationships were non-linear, varied with landscape context, and differed between landscape settings. In simplified landscapes, yields declined beyond a few hundred meters from NSNH, with steeper declines under higher NSNH cover. In the more complex landscape setting, where NSNH were more abundant and distances to habitat were shorter, yield responses were weak across the observed NSNH gradient. These patterns may reflect distance-mediated ecological processes associated with NSNH, although their observational nature precludes attribution to specific mechanisms. Expanding the dataset across broader landscape gradients would strengthen the mechanistic interpretation of these patterns. Significance Precision agriculture can help identify crop-yield footprints consistent with distance-mediated ecological processes at sub-field scales, improving understanding of landscape-crop interactions and supporting ecologically informed landscape management.","背景 现代农业扩张使景观趋于简化，并减少了自然与半自然生境（NSNH），可能破坏了支撑作物生产的生态过程。其中许多过程具有距离依赖性，可能在作物产量中留下随景观背景而变化的时空印记。然而，这些过程在田块尺度以下对作物产量的影响仍知之甚少。精准农业为解决这一空白提供了机遇。目的 利用精准农业数据，评估在不同景观情境下，向日葵距自然与半自然生境的距离—产量关系如何随景观背景而变化。方法 将来自54幅产量图的高分辨率产量监测数据（约70万条观测值，阿根廷潘帕斯地区）与基于土地覆盖产品衍生的景观指标相结合，并采用混合效应模型分析距离—产量关系。这些关系在自然与半自然生境覆盖度梯度（景观背景）以及代表不同景观情境的区域之间进行了评估。结果与结论 距离—产量关系呈非线性，随景观背景而变化，并在不同景观情境之间存在差异。在简化景观中，产量在距自然与半自然生境数百米之外下降，且在自然与半自然生境覆盖度较高时下降更陡。在更为复杂的景观情境中，自然与半自然生境更为丰富且距生境距离更短，产量响应在所观测的自然与半自然生境梯度上较弱。这些格局可能反映了与自然与半自然生境相关的距离介导生态过程，尽管其观测性质使其无法归因于具体机制。将数据集扩展至更广泛的景观梯度将增强对这些格局的机制性解释。意义 精准农业有助于识别与田块尺度以下距离介导生态过程相一致的作物产量印记，增进对景观—作物相互作用的理解，并支持基于生态学信息的景观管理。","Agricultural Systems","2026-10-06T00:00:00Z",{"impact":312,"substance":18,"depth":17,"authority":114,"freshness":115,"relevant":21,"comment":388},"基于70万条产量监测数据揭示田块尺度产量与自然栖息地距离的非线性关系，方法新颖、数据规模大，对生态化农田管理有参考价值。",[390],{"name":385,"url":382},[26,392,29,393,394],"精准农业","作物产量","农业生态",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":398},{"candidates":399,"agricultural_evidence":46},[400,405,410,414],{"code":58,"score":312,"supported":46,"title":401,"abstract":403,"tags":404},[402],"precision agriculture",[392,402],[26,392],{"code":32,"score":66,"supported":46,"title":406,"abstract":407,"tags":409},[],[408],"向日葵",[],{"code":52,"score":200,"supported":14,"title":411,"abstract":412,"tags":413},[],[],[393,29],{"code":415,"score":21,"supported":14,"title":416,"abstract":417,"tags":418},"H6",[],[],[394],[420,421],"潘帕斯 向日葵 产量 自然栖息地","精准农业 产量监测 景观","潘帕斯向日葵产量自然栖息地-4325","10.1016\u002Fj.agsy.2026.104997",{"doi":423,"openalex_id":425,"authors":426,"venue":385,"cited_by_count":91,"oa_url":382,"card":441,"direction":97,"ingested_from":99},"W7220681058",[427,430,433,436,438],{"name":428,"orcid":429},"Facundo José Oddi","https:\u002F\u002Forcid.org\u002F0000-0003-4478-978X",{"name":431,"orcid":432},"Matías G. Goldenberg","https:\u002F\u002Forcid.org\u002F0000-0002-9498-2951",{"name":434,"orcid":435},"Paula Zermoglio","https:\u002F\u002Forcid.org\u002F0000-0002-6056-5084",{"name":437,"orcid":9},"Tomás González",{"name":439,"orcid":440},"Lucas A. Garibaldi","https:\u002F\u002Forcid.org\u002F0000-0003-0725-4049",{"tldr":442,"method":443,"finding":444,"direction":97,"opportunity":445},"利用精准农业产量数据揭示向日葵产量随距自然半自然生境距离变化的非线性关系。","54幅产量图约70万条监测数据，结合土地覆盖景观指标与混合效应模型。","简化景观中产量随距生境距离增加而下降，复杂景观中响应微弱。","可扩展至多作物多区域，结合机制模型量化生境距离对产量的因果贡献。","2026-10-07T23:30:03.865642Z",{"id":448,"title":449,"url":450,"summary":451,"summary_zh":9,"content":9,"source_name":452,"source_url":9,"published_at":111,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":243,"score_detail":453,"sources":455,"tags":457,"primary_topic":460,"secondary_topics":461,"classification":462,"search_phrases":483,"slug":486,"view_count":200,"doi":9,"paper":487,"created_at":496},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",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":313,"relevant":21,"comment":454},"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。",[456],{"name":452,"url":450},[26,392,28,458,459],"无人机植保","杂草管理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":463},{"candidates":464,"agricultural_evidence":46},[465,469,473,479],{"code":32,"score":245,"supported":46,"title":466,"abstract":467,"tags":468},[28,48],[28],[28],{"code":339,"score":353,"supported":46,"title":470,"abstract":471,"tags":472},[],[342],[458],{"code":474,"score":66,"supported":46,"title":475,"abstract":476,"tags":478},"H2",[],[477],"土壤",[],{"code":58,"score":200,"supported":14,"title":480,"abstract":481,"tags":482},[],[],[26,392],[484,485],"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":488,"venue":9,"cited_by_count":91,"oa_url":9,"card":489,"direction":493,"ingested_from":495},[],{"tldr":490,"method":491,"finding":492,"direction":493,"opportunity":494},"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"]