Identification of multiple grain image samples from tray

Abstract

Summary This paper presents a methodology to identify the different grain types from image samples of tray containing multiple grains using colour and textural features. The multiple grain images are segmented into individual grain images. From these images, eighteen colour and twenty‐four textural features are obtained. A neural network model is implemented for identification of bulk food grains. Five different types of grains namely, alasandi, green gram, metagi, red gram and wheat commonly used in Indian food preparations are considered in this work. The maximum and minimum food grain identification accuracies observed in this work are 94% and 80% for wheat and alasandi, respectively. The work finds application in development of machine vision system for grain identification, classification and grading.