A newly developed method to extract the optimal hyperspectral feature for monitoring leaf biomass in wheat

Abstract

Abstract Hyperspectral image provides a plethora of information, some of which may be redundant. Therefore, reducing information’s dimensionality using feature selection methods becomes essential. Here, we proposed a new technique, named synergy interval partial least squares (SIPLS) with successive projections algorithm (SPA) (SIPLS-SPA), combining SIPLS and SPA, to efficiently extract optimal spectral features of wheat biomass from hyperspectral image data. In this study, hyperspectral images and leaf biomass were acquired from two-year wheat field experiments with varied nitrogen rates, planting densities, and cultivars. The results showed that eight wavelengths (706, 724, 734, 806, 808, 810, 812, and 816\u202fnm) were selected as the sensitive input variables to establish partial least squares regression (PLSR) model for wheat leaf biomass. The SIPLS-SPA biomass model performed better with higher Rc2 (0.79) in calibration, lower RMSEv (0.059\u202fkg/m2) and RRMSEv (38.55%) in validation. Compared with other state-of-the-art feature selection techniques, the SIPLS-SPA method provided significantly fewer unrelated, collinear spectral variables, and showed a promising application in terms of lower overall complexity, reduced computational complexity, and shorter running time. Furthermore, this work demonstrates the potential of SIPLS-SPA method for extracting other plant traits from hyperspectral data in future agricultural applications.