Classification of Rice Varieties using Convolution Neural Networks

The classification of rice varieties is a crucial task in the agricultural industry, as it helps farmers to identify and manage crops effectively. In recent years, deep learning algorithms have shown promising results in image recognition tasks, and have been applied to agricultural applications to classify crop varieties. This study proposes a deep learning-based CNN approach to classify five variants of rice varieties based on their images. The proposed method utilizes convolutional neural networks (CNNs) to learn the features from rice images and classify them into their respective categories. The dataset used in this study consists of 300 images of five different rice varieties, which are collected from various sources and angles. The experimental results demonstrate that the proposed method achieves high accuracy in classifying the five rice varieties. The accuracy of the classification algorithm is evaluated using different metrics as mentioned in the literature.