CNN-based Approach for Classification of Rice Varieties

Due to its importance as a worldwide staple crop, rice is crucial in guaranteeing the food security of billions of people. Due to a broad range of rice varieties, each of which has specific properties and characteristics, accurate categorization of rice variations is essential for agricultural practices, commerce, and culinary uses. Convolutional Neural Networks (CNNs) are an excellent option for tackling the difficulties of rice variety categorization since they have become a potent deep-learning technique. A CNN model for the precise and efficient categorization of rice types is presented in this study. An extensive set of 75,000 images from five different types of rice (Arborio, Basmati, Ipsala, Jasmine, and Karacadag) was assembled for model training and assessment. To facilitate unbiased performance measurement, the dataset was partitioned at random into a training batch of 60,000 images and a test batch of 15,000 images. CNNs can learn hierarchical representations of visual input, allowing them to recognize complex patterns and connections within images, making them ideally suited for computer vision applications, notably image classification. This skill is essential for classifying rice varieties since it allows for detecting minute variations across rice kinds. The research's findings are encouraging because the proposed CNN model had a remarkable accuracy on the test set of 97.31%. This high degree of precision supports the model's outstanding capacity to distinguish between the five annotated classes of rice varieties, demonstrating the promise of deep learning methods in solving complex agricultural problems. The efficacy of this CNN-based strategy will significantly impact the agriculture industry. Accurate rice variety identification gives farmers the flexibility to modify growing techniques per particular needs while maintaining the purity and legitimacy of the final product for the food business.