Deep Rice: A Comprehensive Deep Learning Approach for Quality Assessment in Rice Grains

Rice is a staple food and is one of the most extensively produced grain products worldwide. Ensuring that its quality is up-to-par is of utmost importance as it is heavily consumed all over the world. Rice quality is determined using morphological features, its texture, moisture content, and the nutritional profile of the grain. Finding the rice grain quality is extremely important as it plays an important role in identifying its consumer uptake, longevity, milling quality, and overall endorsement. This paper further delves into the classification of different grains and determining the grain quality. The types of rice grains used in this study are; Golden Ivy, Basmati, Parmal, Khandha Rice, and Sona Sella. The custom dataset created contains 3200 images for each variety of the regional rice of Uttarakhand,resulting in 16000 images in total. Various pre-processing techniques are applied to the images, through these images the morphological features are extracted, and then deep learning algorithms are applied to them to help classify the rice grains into their corresponding varieties. This results in an average validation accuracy of 93.96% over sixfold.