数字农科院2.0

Estimating Stratified Biomass in Cotton Fields Using UAV Multispectral Remote Sensing and Machine Learning

文献类型： 外文期刊

作者： Zhengdong Hu;Shiyu Fan;Yabin Li;Qiuxiang Tang;Longlong Bao;Shuyuan Zhang;Guldana Sarsen;Rensong Guo;Liang Wang;Na Zhang;Jianping Cui;Xiuliang Jin;Tao Lin

作者机构：

关键词： aboveground biomass;machine learning;precision agriculture;stratified estimation;UAV multispectral sensing;vegetation index

期刊名称： Drones

ISSN： 2504-446X

年卷期： 2025 年 9 卷 3 期

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： The accurate estimation of aboveground biomass (AGB) is essential for monitoring crop growth and supporting precision agriculture. Traditional AGB estimation methods relying on single spectral indices (SIs) or statistical models often fail to address the complexity of vertical canopy stratification and growth dynamics due to spectral saturation effects and oversimplified structural representations. In this study, a unmanned aerial vehicle (UAV) equipped with a 10-channel multispectral sensor was used to collect spectral reflectance data at different growth stages of cotton. By integrating multiple vegetation indices (VIs) with three algorithms, including random forest (RF), linear regression (LR), and support vector machine (SVM), we developed a novel stratified biomass estimation model. The results revealed distinct spectral reflectance characteristics across the upper, middle, and lower canopy layers, with upper-layer biomass models exhibiting superior accuracy, particularly during the middle and late growth stages. The coefficient of determination of the UAV-based hierarchical model (R2 = 0.53–0.70, RMSE = 1.50–2.96) was better than that of the whole plant model (R2 = 0.24–0.34, RMSE = 3.91–13.85), with a significantly higher R2 and a significantly lower root mean squared error (RMSE). This study provides a cost-effective and reliable approach for UAV-based AGB estimation, addressing limitations in traditional methods and offering practical significance for improving crop management in precision agriculture.

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