Quantifying effect of maize tassels on LAI estimation based on multispectral imagery and machine learning methods

Abstract

A reliable method to estimate the leaf area index (LAI) in a field environment is crucial for precise monitoring of crop-growth status. Currently, the crop canopy information has been widely used to estimate LAI using remote sensing methods. Many studies regard canopy tassels and leaves as integrated objects, no systematic study has yet investigated how tassels affect the accuracy of LAI estimates. Moreover, the estimation accuracy and generalization ability of the number of selected vegetation indices was seldom evaluated. Therefore, this study used deep learning segmentation methods to quantify how maize tassels affect LAI estimates and to evaluate how the number of variables affects LAI estimates. The results showed that the multispectral dataset segmentation tassels had the highest accuracy when using the VGG-encoded U-Net model (class pixel accuracy, CPA = 89.53 %; mean intersection over union, MIoU = 85.97 %). The segmentation accuracy first was increased and then decreased with tassel growth. By quantifying the contribution of tassels to the vegetation index, tassels most strongly affect the modified nonlinear vegetation index (MNLI) constructed from the canopy spectral information. Moreover, removing the tassels in images could significantly improve the accuracy of LAI estimates using the gradient-boosting decision tree method (GBDT). The estimation method obtained the highest accuracy when using nine vegetation indices to estimate the LAI (R2 = 0.816, RMSE = 0.399, rRMSE = 7.4 %). Overall, the proposed method improves the accuracy of LAI estimates, which provides crucial technical support for monitoring the LAI of maize.