Thermal imaging of rice grains and flours to design convolutional systems to ensure quality and safety

Abstract

In this work, a thermographic camera and intelligent algorithms have been used to classify five different types of rice (Oryza sativa L.) in grain or flour format and to detect mixtures of different rice types which act as adulterated samples. For this purpose, more than 63,000 thermographic images of pure rice (Japonica and Indica) and their mixtures were used. These images have been used to train and validate a system based on deep learning (convolutional neural networks) to carry out their classification and detect adulterations. The combination of cognitive modelling and thermographic analysis can classify the different types of rice and detect potential adulterations with accuracies above 98%. These promising results make this tool viable for fraud detection, food quality control, and public safety.