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

Abstract

Abstract 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.

Plant phenotyping relevance

UAV画像からソラマメの草丈と収量を推定する手法を開発・評価しており、形質取得と推定精度の検証が研究の中心です。

Code and data availability

The paper's UAV-derived plant height data, ground measurements, and yield estimation datasets are not publicly deposited; the authors state they are available only from the corresponding author on reasonable request. No author analysis code, models, or public data URLs are provided in the supplied blocks.

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