IDENTIFICATION OF RICE PURITY LEVEL FROM MIXED RICE VARIETIES USING DEEP LEARNING

Abstract

The current study was conducted in Multan, Pakistan to investigate an automated appearance based system for purity level identification of seven common rice (Oryza sativa L.) varieties from mixed rice grain samples. Adulteration is a major hurdle that affects rice export in Pakistan that refers to the mixing of premium rice grain varieties with the low grade rice grains to be marketed at a high cost. This study was based on the dataset collected from Rice Research Institute, Kala Shah Kaku, Pakistan during 2018-2020. Three Pakistani premium rice varieties (Basmati Shaheen, Basmati Super, and Basmati Pak) were mixed with four low quality varieties (Basmati 198, Basmati 2000, Basmati 370 and Basmati 385) in weight ratios of 10%, 15%, 20%, 25% and 30%. Classification and recognition of purity level of basmati rice achieved average accuracy of 89.88% using convolutional neural network. The proposed system has the potential to be used at a commercial scale to test the purity level of exported rice.