数字农科院2.0

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

文献类型： 外文期刊

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

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

ISSN： 1746-4811

年卷期： 2022 年

页码：

收录情况： JCR（2021版）

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 efciently. 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 ftting degree with the ground measured values, with the coefcient of determination (R2), 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 (R2=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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