Cognitive spectroscopy for the classification of rice varieties: A comparison of machine learning and deep learning approaches in analysing long-wave near-infrared hyperspectral images of brown and milled samples

Rapid and non-destructive detection of genuine Thai Jasmine rice (Khao Dawk Mali 105 (KDML105)) from Pathum Thani1 (PTT1) and Phitsanulok2 (PSL2) under either milled or brown conditions is required to disrupt fraudulent. This study aimed to resolve this real issue using long-wave near infrared hyperspectral imaging (NIRHSI) coupled with machine learning and deep learning approaches. The best classification accuracy for the milled rice was achieved using the spectral imaging-based analysis on the NIR-HSI data with selected wavelength, approximately 95% for the test set either by convolutional neural network or support vector machine (SVM), whereas for the brown rice, the SVM model based on the averaged NIR spectra could achieve the best classification accuracy of 95.4%. It suggests the chemical component difference and its spatial distribution in the milled rice could contribute higher classification accuracy. Additionally, the surface bran effects of brown rice could be reduced by using averaged spectral data coupled with the SVM method.