Quality Testing of Food Grains Using Digital Image Processing Techniques

Abstract

Grain production is the principal agricultural crop for our country. Farmers pay close attention to yield while the crop is still in the ground, but once the grain has been processed and sold, quality takes over as the primary determinant of its viability. These grains contain a variety of impurities, such as stones, weed seeds, chaff, damaged seeds, etc. Today, scientific methods are used to identify grain seed variations and quality. We suggested a grain classification system based on machine learning and image processing algorithms to distinguish between distinct types of grains and assess the purity of grains using image processing techniques based on several factors like particle size and form.