Key points are not available for this paper at this time.
Rice is a staple food for a significant portion of the global population, making accurate classification of rice varieties essential for farming and consumer protection. This review provides a focused analysis of the current advancements and challenges in applying computer vision (CV) techniques to rice variety classification. The study examines key steps in the automation process, including image acquisition, pre-processing, feature extraction, and classification algorithms, with particular emphasis on machine learning and deep learning methods such as Convolutional Neural Networks (CNNs), which have demonstrated exceptional performance in recent research. However, practical implementation faces challenges, including the availability of high-quality datasets, the impact of environmental variations on image quality, and the computational demands of complex models. Our study discusses these obstacles and highlights the importance of developing resilient and scalable systems for real-world applications. By synthesizing findings from various studies, this review proposes future directions for advancing rice variety classification, focusing on improved feature extraction techniques, enhanced dataset management, and integrating innovative machine learning paradigms. This work is a valuable resource for researchers and practitioners aiming to advance rice classification technologies and contribute to food security and agricultural sustainability.
Islam et al. (Sat,) studied this question.