Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds.

ABSTRACT: Common maize seeds and silage maize seeds are similar in appearance and are difficult to identify with the naked eye. Four varieties of common maize seeds and four varieties of silage maize seeds were identified by near-infrared hyperspectral imaging (NIR-HSI) combined with chemometrics. The pixel-wise principal component analysis was used to distinguish the differences among different varieties of maize seeds. The object-wise spectra of each single seed sample were extracted to build classification models. Support vector machine (SVM) and radial basis function neural network (RBFNN) classification models were established using two different classification strategies. First, the maize seeds were directly classified into eight varieties with the prediction accuracy of the SVM model and RBFN

SUBMITTER: Bai X 

PROVIDER: S-EPMC9050551 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature