Deep Learning for Precise Rice Multi-Class Classification:Unveiling the Potential of CNNs

This research delves into the difficult realm of classifying rice. It concentrates on five common kinds cultivated in Turkey: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The research presents a massive database of 75,000 high-resolution grain images, meticulously matched with 15,000 shots of each species of rice. The imagegraphs were carefully picked by Koklu et al. he significance of classifying these varieties lies in its paramount importance for agricultural practices and seed quality assessment. Using Convolutional Neural Network (CNN) techniques, the research illustrates how complicated and rich the dataset is. It also highlights how crucial it is to have complicated models to locate and exploit the data's complex patterns. The findings reveal an incredible 97.45% accuracy, which illustrates how effectively the CNN algorithms functioned at categorizing the various varieties of rice. Precision, recall, and F-score measurements were also assessed to provide a holistic image of how well the categorization functioned. Researchers and rice enthusiasts who wish to learn more about how to categorize rice and what it entails for cultivation and quality evaluation will find this research extremely informative.