Non-destructive discrimination of the variety of sweet maize seeds based on hyperspectral image coupled with wavelength selection algorithm

Abstract

A novel method for discriminating the varieties of sweet maize seeds was developed on the basis of hyperspectral imaging technology in the visible and near-infrared (Vis–NIR) region (326.7–1098.1 nm). First, the Vis–NIR hyperspectral images of nine varieties of sweet maize seeds were obtained with the orientations of germ up and down. Second, Savitzky–Golay (SG) smoothing and first derivative (FD) methods were used to highlight the differences of different maize seeds. Finally, a variety discrimination model was established by support vector machine (SVM) based on the effective wavelengths extracted by competitive adaptive reweighted sampling (CARS) algorithm. Additionally, the performance of other six comparative algorithms including successive projections algorithm (SPA), principal component analysis (PCA), factor analysis (FA), random projection (RP), independent component analysis (ICA), and t-distributed stochastic neighbor embedding (t-SNE) were compared with CARS. The classification models of SVM was also compared with Naive Bayes (NB), K-nearest neighbors (KNN), artificial neural networks (ANN), decision tree (DT), linear discriminant analysis (LDA) and logistic regression (LR) algorithms. Results showed that the SG + FD + CARS + SVM model achieved the best performance for discrimination of nine varieties of sweet maize seeds with classification accuracies of 94.07% and 94.86% for germ up and germ down orientations respectively, which is promising to be a new approach for discrimination the variety of sweet maize seeds.