Potato LAI estimation by fusing UAV multi-spectral and texture features

Objective  Develop a method to improve the potato (Solanum tuberosum) leaf area index (LAI) estimation accuracy using the UAV multiple spectral wavebands and texture information.

Method  The DJI P4M drone was used to collect multispectral images of the southern winter potato at seedling period, budding period and tuber swelling period from February to April 2021. LAI data were measured by LAI-2000 canopy analyzer. The spectral and texture characteristics of images were extracted. The correlations between vegetation index, texture characteristics and LAI were analyzed. The selected characteristic variables were analyzed based on subset of adjusted R²_adj. The principal component analysis was used to fuse spectrum and texture features, and the principal component analysis-multiple linear regression (PCA-MLR) model was used to estimate potato LAI.

Result  From the seedling period to the tuber swelling period, the PCA-MLR estimation model was better than texture multiple linear regression (T-MLR) and vegetation index multiple linear regression (VI-MLR) model, with R² of 0.73, 0.59 and 0.66 respectively.

Conclusion  This study proposed a method of PCA-MLR to estimate the potato LAI and improve the levels of the potato growth monitoring and field management.