The Detection and Analysis of Rice Kernel Parameters Conducted by the Application of Cluster Segmentation Techniques

Rice is the most frequently eaten food item on a global scale. Every research is essential for conducting a thorough study of the provided material. Nevertheless, conducting manual grain examinations incurs significant costs and requires a substantial amount of time. Moreover, the outcomes of such investigations are contingent upon the competence, disposition, and personal motivations of the quality inspector. Utilizing the aforementioned image processing technique, expeditious and uncomplicated outcomes can be obtained when investigating rice properties. This project demonstrates the utilization of image processing techniques to measure the dimensions (width and length) as well as the color characteristics of rice kernels. The present study elucidates the operational configuration of the proposed grain analyzer system, wherein effectively analyzes grain samples through many perspectives.