Image Classification for Rice varieties using Deep Learning Models

Rice is the most rapidly growing crop in India, and as the population grows, demand for rice also increases. The majority of Asian countries grow rice and export it worldwide. The various rice varieties have been cultivated depending on the people's food culture. At the same time, food quality is a top priority, so we use computer vision techniques to extract rice qualitative features. The products are analyzed using image processing techniques for physical attributes such as Visual Geometry Graph (VGG16) and Vanilla CNN (also known as vanilla neural networks) to identify traits and textual features of rice grain images. VGG16 consists o f CNN architecture with 16 layers. It can train millions of datasets and achieve the highest accuracy rate. In addition, another model, namely vanilla neural networks, is an extension of the linear regression model. However, vanilla CNN has an additional hidden layer between inputs and outputs that help with extra computations. Image processing techniques are combined with neural networks to provide more accuracy in the training model rather than the manual process. Jasmine, Basmati, Arborio, Ipsala, and Karacadag rice varieties are the five types of rice image datasetsused for classification. Each of these varieties has 15000 images, for a total of 75000 images used in the training and testing process. The best image classifier is chosen based on the best accuracy score. The proposed model's outcome deliberates the better performance in determining the rice varieties. Keywords: Convolutional Neural Network, Vanilla Neural Network, VGG16, Rice varieties