Classification of traditional Joha rice using deep neural network

Abstract

Rice is the major food for the most of the people in the world. It is grown comfortably in a rain fed area. Rice is called a kharif crop in India. According to the International Rice Research Institute (IRRI) classification rice is classified as Ahu, Sali, Boro and Hill rice. In north eastern India the Joha rice is mainly cultivated in the state of Assam. There are many varieties of Joha rice like Kunkuni, Rampal, Manipuri, Tulshi, and Keteki. But it is quite complex for the farmers as well as the common people to differentiate these type of Joha rice due to their morphological structure. Initially this type of classification done by naked eyes or used some laboratory experiments. But due to the tiredness and some external factors it is not fruitful. Some of the image classification technologies are used in classification as well identification of rice in different research work using MATLAB. At present deep learning plays a tremendous role in image analysis in agricultural domain. Here in this paper we take two verities of Joha rice which are closer in morphological structure and cannot be separated by our naked eyes. Using deep neural network, binary classification is done in these Joha rice.