Faba bean above-ground biomass and bean yield estimation based on consumer-grade unmanned aerial vehicle RGB images and ensemble learning

文献类型： 外文期刊

第一作者： Ji, Yishan

作者： Ji, Yishan;Liu, Rong;Xiao, Yonggui;Cui, Yuxing;Zong, Xuxiao;Yang, Tao;Chen, Zhen

作者机构：

关键词： Legumes; Plant height; Machine learning; Stacking regression; Regression coefficient

期刊名称：PRECISION AGRICULTURE （ 影响因子：6.2； 五年影响因子：6.2 ）

ISSN： 1385-2256

年卷期： 2023 年 24 卷 4 期

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收录情况： SCI

摘要： Accurately and economically estimated crop above-ground biomass (AGB) and bean yield (BY) are critical for cultivation management in precision agriculture. Unmanned aerial vehicle (UAV) platforms have shown great potential in crop AGB and BY estimation due to their ability to rapidly acquire remote sensing data with high temporal-spatial resolution. In this study, a low-cost and consumer-grade camera mounted on a UAV was adopted to acquire red-green-blue (RGB) images, which were then combined with ensemble learning to estimate faba bean AGB and BY. The following results were obtained: (1) The faba bean plant height derived from UAV RGB images presented a strong correlation with the ground measurement (R-2 = 0.84, RMSE = 63.6 mm). (2) The accuracy of BY estimation (R-2 = 0.784, RMSE = 0.460 t ha(-1), NRMSE = 14.973%) based on RGB images was higher than the accuracy of AGB estimation (R-2 = 0.618, RMSE = 0.606 t ha(-1), NRMSE = 16.746%). (3) The combination of three variables (vegetation index, structural information, textural information) improved the AGB and BY estimation accuracy. (4) The AGB and BY estimation performance were best for the mid bean-filling stage. (5) The ensemble learning model provided higher AGB and BY estimation accuracy than the five base learners (k-nearest neighbor, support vector machine, ridge regression, random forest and elastic net models). These results indicate that UAV RGB images combined with machine learning algorithms, particularly ensemble learning models, can provide relatively accurate faba bean AGB (R-2 = 0.683, RMSE = 0.568 t ha(-1), NRMSE = 15.684%) and BY (R-2 = 0.854, RMSE = 0.390 t ha(-1), NRMSE = 12.693%) estimation and considerably contribute to the high-throughput phenotyping study of food legumes.

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