Identification of soybean varieties by terahertz spectroscopy and integrated learning method

Abstract

Terahertz (THz) spectroscopy and integrated learning method were utilized to identify ten types of soybean seeds in this work. Before soybean seeds were identified, seed sample of nine thicknesses were tested to verify THz spectra of soybeans prepared by grinding and polishing is feasible, and a statistical method of single factor variance was calculated to prove THz spectra have significant influence on soybean varieties. Then, the integrated learning method (DT_A) based on adaboost algorithm and decision tree (DT) was studied to identify soybean varieties by five pretreatment methods, thirty basic integrated classifiers and three compared methods. DT_A method combined with Savitzky Golay smoothing (SGS) and kernel principal component analysis (KPCA) obtained the best result of 99.24%. The best average accuracy of the proposed method was 89.29% for THz time-domain spectrum. The improved accuracy shows THz spectroscopy combined with integrated learning method may be an effective candidate technology for soybean detection.