Revolutionizing Rice Quality Control: MobileNetV2 for Grain Classification

The classification of rice grains is a crucial aspect of the agricultural sector, particularly in light of challenges such as grain variability, limited computing resources, and the need for real-time processing. Maintaining quality control and increasing yield often rely on the efficient and accurate classification of rice grains. In this study, we explore the application of MobileNetV2, a deep learning model, for rice grain classification. Utilizing the advanced features of MobileNetV2, including linear bottlenecks and inverted residuals, we achieve an impressive accuracy rate of 99%. Despite the model’s effectiveness, we identify several challenges, including variability in grain appearance, constraints on computational resources, and the necessity for real-time processing. We demonstrate that the model remains robust and efficient under real-world conditions by employing strategies such as data augmentation, model tuning, and hardware acceleration to overcome these issues. Specifically, we enhance the model’s resilience to grain variability through data augmentation techniques, optimize the MobileNetV2 architecture for performance in resource-limited environments, and implement hardware acceleration to meet real-time processing requirements. Collectively, these strategies enable us to maintain a high accuracy rate of 99% in classifying rice grains.