Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.)

文献类型： 外文期刊

第一作者： Ji, Yishan

作者： Ji, Yishan;Liu, Rong;Li, Mengwei;Yan, Xin;Li, Guan;Wang, Dong;Fu, Li;Jin, Xiuliang;Zong, Xuxiao;Yang, Tao;Chen, Zhen;Cheng, Qian;Ma, Yu

作者机构：

关键词： Faba bean (Vicia faba L; ); Unmanned aerial vehicle (UAV); Plant height; Yield estimation; Machine learning

期刊名称：PLANT METHODS （ 影响因子：5.827； 五年影响因子：5.904 ）

ISSN：

年卷期： 2022 年 18 卷 1 期

页码：

收录情况： SCI

摘要： Background Faba bean is an important legume crop in the world. Plant height and yield are important traits for crop improvement. The traditional plant height and yield measurement are labor intensive and time consuming. Therefore, it is essential to estimate these two parameters rapidly and efficiently. The purpose of this study was to provide an alternative way to accurately identify and evaluate faba bean germplasm and breeding materials. Results The results showed that 80% of the maximum plant height extracted from two-dimensional red-green-blue (2D-RGB) images had the best fitting degree with the ground measured values, with the coefficient of determination (R-2), root-mean-square error (RMSE), and normalized root-mean-square error (NRMSE) were 0.9915, 1.4411 cm and 5.02%, respectively. In terms of yield estimation, support vector machines (SVM) showed the best performance (R-2 = 0.7238, RMSE = 823.54 kg ha(-1), NRMSE = 18.38%), followed by random forests (RF) and decision trees (DT). Conclusion The results of this study indicated that it is feasible to monitor the plant height of faba bean during the whole growth period based on UAV imagery. Furthermore, the machine learning algorithms can estimate the yield of faba bean reasonably with the multiple time points data of plant height.

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