[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2117":3},{"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,"view_count":31,"doi":32,"paper":33,"created_at":64},2117,"A source-side trait-mediated framework for dynamic maize yield prediction using UAV multispectral imagery","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112375","Dynamic in-season maize yield prediction is essential for cultivation management and precision decision-making. However, traditional yield prediction models based on vegetation indices often lack physiological interpretability, and integrating multitemporal dynamic information within a unified framework remains challenging. This study proposes a source-side trait-mediated approach for dynamic maize yield prediction using UAV multispectral imagery. Leaf area index (LAI), leaf chlorophyll content (SPAD), and aboveground biomass (AGB) were used as intermediate variables. A cascaded framework linking canopy spectra, source-side agronomic traits, and yield was constructed to improve agronomic interpretability. In addition, the temporal dynamic factor (TDF) was introduced to model stage-dependent trait-yield relationships, enabling dynamic yield prediction. The results showed that increasing planting density significantly enhanced LAI and AGB, whereas yield first increased with planting density and then decreased slightly. For agronomic trait prediction, XGBoost generally outperformed RF. In the independent multi-cultivar test, the XGBoost models achieved coefficients of determination (R 2 ) of 0.8185, 0.8758, and 0.8113 for LAI, SPAD, and AGB, respectively. The TDF fitting results for the 2025 dataset showed that the linear relationship between yield and the Comprehensive Yield Index (CYI) had R 2 values ranging from 0.67 to 0.86 and RMSE values from 0.428 to 0.648 t\u002Fha across growth stages. In the 2024 interannual independent test, the model maintained an R 2 of 0.7803 and an RMSE of 0.6076 t\u002Fha. Furthermore, in a multi-cultivar scenario, yield prediction achieved an R 2 of 0.6824 and an RMSE of 0.3746 t\u002Fha. The proposed framework improved the agronomic interpretability and interannual and cross-cultivar applicability of maize yield prediction using UAV-based multitemporal imagery.","动态的玉米生长季产量预测对栽培管理和精准决策至关重要。然而，传统的基于植被指数的产量预测模型往往缺乏生理可解释性，且在统一框架内整合多时相动态信息仍具挑战性。本研究提出了一种基于源端性状介导的方法，利用无人机多光谱影像进行玉米动态产量预测。以叶面积指数（LAI）、叶片叶绿素含量（SPAD）和地上生物量（AGB）作为中间变量，构建了连接冠层光谱、源端农艺性状与产量的级联框架，以提高农艺可解释性。此外，引入时间动态因子（TDF）来建模阶段依赖的性状-产量关系，从而实现动态产量预测。结果表明，增加种植密度显著提高了LAI和AGB，而产量随种植密度先增加后略有下降。在农艺性状预测方面，XGBoost通常优于RF。在独立多品种测试中，XGBoost模型对LAI、SPAD和AGB的决定系数（R²）分别为0.8185、0.8758和0.8113。2025年数据集的TDF拟合结果显示，在各生育阶段，产量与综合产量指数（CYI）之间的线性关系R²范围为0.67至0.86，RMSE范围为0.428至0.648 t\u002Fha。在2024年际独立测试中，模型保持了0.7803的R²和0.6076 t\u002Fha的RMSE。此外，在多品种场景下，产量预测的R²为0.6824，RMSE为0.3746 t\u002Fha。所提出的框架提高了基于无人机多时相影像的玉米产量预测的农艺可解释性以及年际和跨品种适用性。",null,"Computers and Electronics in Agriculture","2026-09-10T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"提出源端性状中介的无人机多光谱玉米动态产量预测框架，方法新颖、跨年跨品种验证扎实，对精准农业决策有实质参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","无人机","产量预测","玉米","遥感",0,"10.1016\u002Fj.compag.2026.112375",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":6,"card":57,"direction":61,"ingested_from":63},"W7212127922",[36,38,40,42,45,48,50,52,54],{"name":37,"orcid":9},"Chengxin Bai",{"name":39,"orcid":9},"Xiaoyuan Bao",{"name":41,"orcid":9},"Baoyuan Zhang",{"name":43,"orcid":44},"Congcong Guo","https:\u002F\u002Forcid.org\u002F0000-0002-8231-7655",{"name":46,"orcid":47},"Xinying Li","https:\u002F\u002Forcid.org\u002F0000-0002-4256-589X",{"name":49,"orcid":9},"Fuyang Cui",{"name":51,"orcid":9},"Hong Fan",{"name":53,"orcid":9},"Cai Zhao",{"name":55,"orcid":56},"Xiaohe Gu","https:\u002F\u002Forcid.org\u002F0000-0002-7102-1939",{"tldr":58,"method":59,"finding":60,"direction":61,"opportunity":62},"提出基于无人机多光谱的源端性状中介框架，实现玉米动态产量预测。","用LAI、SPAD、AGB作中间变量，构建冠层光谱-性状-产量级联框架，引入时间","框架提升农学可解释性，跨年独立测试R²=0.78，多品种场景R²=0.68。","农业遥感与作物表型","可探索将源端性状框架扩展至其他作物或结合多源遥感数据，提升跨区域泛化能力。","openalex","2026-09-11T23:30:01.789186Z"]