Generic prediction model of moisture content for maize kernels by combing spectral and color data through hyperspectral imaging

Abstract

Moisture content (MC) is an important index to measure the quality of maize kernels. This study aims to construct a generic prediction model and optimize the characteristic variables for efficiently predicting the MC of maize kernels across various varieties. Visible/near-infrared hyperspectral imaging (HSI) within the wavelength range of 374.98 - 1038.79 nm is employed. The 270 samples containing 18 varieties at five periods over two years are collected. Spectral and color feature data based on the H, S, and V color channels of maize are extracted. Partial least squares regression (PLSR) and principal component regression (PCR) are used to establish MC prediction models. Backward interval least squares regression (Bi-PLS), uninformative variables elimination (UVE), and successive projections algorithm (SPA) are jointly (Bi-PLS, UVE, Bi-PLS-SPA, UVE-SPA, and SPA) used to select characteristic wavelengths. The MC prediction models are developed using whole wavelengths, characteristic wavelengths, color features, and fused data. The results suggest that the optimal PLSR model is based on fused data of color features and characteristic wavelengths selected by UVE-SPA from S-G smoothing spectral data (HSV+S-G-UVE-SPA-PLSR). The Rc and Rp are 0.9804 and 0.9835, respectively. The RMCEc and RMCEp are 1.6889% and 1.6523%, respectively. The RPD is 5.22. The optimal prediction model can quickly measure MC for different maize kernels, guiding diverse application scenarios to enhance the quality of maize kernels and processing efficiency.