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 (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.
Keywords: Faba bean (Vicia faba L.); Machine learning; Plant height; Unmanned aerial vehicle (UAV); Yield estimation.
© 2022. The Author(s).