融合无人机光谱、纹理特征与LAI的冬小麦单产估测方法

Winter Wheat Yield Estimation Method Based on Integration of UAV Spectral Features, Texture Features and LAI

摘要:

田块尺度冬小麦单产精准估测对大田农业生产管理具有重要现实意义。针对单一植被指数难以全面表征作物生长状况,引入多参数、多变量的无人机遥感估产方法已成为发展趋势。为此,本文采用“空地一体”决策思路,以2023—2024年度冬小麦为研究对象,选取抽穗期和灌浆期的无人机多光谱影像,融合地面少量实测叶面积指数(Leaf area index,LAI)作为作物形态参数,构建植被指数单变量、植被指数+纹理特征双变量及植被指数+纹理特征+LAI三变量的单产估测模型,并采用随机森林(Random forest,RF)、极端梯度提升(Extreme gradient boosting,XGBoost)和支持向量机(Support vector machine,SVM)机器学习算法进行建模,对比分析不同模型和特征组合的估测效果。结果表明,基于“植被指数+纹理特征+LAI”三变量组合的RF算法在两生育期数据中建模精度最高(抽穗期:R2=0.729,RMSE为524.475kg/hm2,NRMSE为11.181%,RE为3.481%;灌浆期:R2=0.779,RMSE为479.265kg/hm2,NRMSE为9.736%,RE为3.205%),显著优于XGBoost和SVM算法。基于最优模型最优组合获取2023—2024年度与2024—2025年度抽穗期和灌浆期单产空间分布图,结果显示,灌浆期估测精度整体高于抽穗期,两年度灌浆期估测单产与实测值的平均差异分别为334.035、284.235kg/hm2。SHAP分析表明,LAI在不同生育期均对单产估测精度提升具有重要贡献。研究结果表明,本文提出的“空地一体”多变量逐步融合决策思路可实现田块尺度冬小麦单产的精准估测,为智慧农业管理提供技术支撑。

Abstract:

Precise estimation of winter wheat yield at the field plot scale was considered to be of significant practical importance for agricultural yield management under large-scale farming conditions. Given that single vegetation indices were insufficient to comprehensively characterize crop growth status, the development of multi-parameter and multi-variable UAV-based remote sensing yield estimation methods was regarded as an emerging trend. An air-ground integrated decision-making framework was adopted, and winter wheat from the 2023—2024 growing season was selected as the research object. UAV multispectral imagery acquired during the heading and grain filling stages was utilized and combined with a limited amount of ground-measured leaf area index (LAI) as a crop morphological parameter to construct yield estimation models, including single-variable models ( vegetation index only), dual- variable models (vegetation index + texture features), and triple-variable models (vegetation index + texture features + LAI). Yield estimation modelling was conducted by using random forest ( RF), extreme gradient boosting ( XGBoost ), and support vector machine ( SVM ) algorithms, and the estimation performance of different models and feature combinations was comparatively analyzed. The results indicated that the RF algorithm based on the three-variable combination of vegetation index, texture features, and LAI achieved the highest modelling accuracy at both growth stages (heading stage: R2 = 0. 729, RMSE = 524. 475 kg / hm2, NRMSE = 11. 181% , RE = 3. 481% ; grain filling stage: R2 = 0. 779, RMSE = 479. 265 kg / hm2, NRMSE = 9. 736% , RE = 3. 205% ), and significantly outperformed the XGBoost and SVM algorithms. Based on the optimal model-feature combination, spatial distribution maps of winter wheat yield for the 2023—2024 and 2024—2025 seasons during the heading and grain filling stages were generated. The results revealed that yield estimation accuracy during the grain filling stage was generally higher than that during the heading stage. The average differences between estimated and measured yields during the grain filling stage for the two seasons were 334. 035 kg / hm2 and 284. 235 kg / hm2, respectively. SHAP analysis further indicated that LAI contributed substantially to the improvement of yield estimation accuracy across all growth stages. Overall, the findings demonstrated that the proposed air-ground integrated multivariate stepwise fusion decision-making approach enabled rapid and accurate estimation of winter wheat yield at the field plot scale, thereby providing effective technical support for smart agricultural management.