Biomass prediction based on hyperspectral images of the Arabidopsis canopy

Hyperspectral images provide detailed crop canopy images and spectral information to evaluate crop biomass. However, a large amount of irrelevant information also exists in hyperspectral images, making it difficult to accurately predict crop biomass. Therefore, this study aimed to eliminate irrelevant information from hyperspectral data to accurately predict shoot and root biomass of Arabidopsis. First, hyperspectral images of Arabidopsis were acquired in the spectral range of 400–1000 nm, and the background was removed using different segmentation techniques. Comparing the results of image processing based on image and spectral information methods, the average reflectance spectrum obtained by the spectral information based normalized difference vegetation Index (NDVI) segmentation resulted better shoot and root biomass prediction than the excess green index (ExG), CIELAB (Lab), and soil-adjusted vegetation index (SAVI) methods. Three wavelength optimization methods such as competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), and non-dominated sorting genetic algorithm-II (NSGA) were used to extract useful information from hyperspectral images. BOSS selected the least number of wavelengths and the partial least squares regression (PLSR) models developed using BOSS produced better results for both shoot and root biomass. Only 19 informative wavelengths were selected and PLSR models accurately predicted shoot biomass with RC2 of 0.91, RV2 of 0.85, RMSEC, RMSEV of 0.013 g and 0.017 g, respectively, and root biomass with RC2 of 0.94, RV2 of 0.88, RMSEC and RMSEV of 0.03 g and 0.04 g, respectively. The results indicated that hyperspectral imaging is very effective for accurately predicting shoot and root biomass of Arabidopsis.