Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{151424,
        author = {P. Madhumitha and Sneha Samanta and A. Shireesha and G. Susmitha},
        title = {Quality Testing of Rice Grains using SVM},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {12},
        pages = {674-678},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151424},
        abstract = {Classification of rice grains is important for us humans because it straight away affects our health. Approximately 90% of the Asian countries prefer rice as their major food, whose demand and economical aspects are increasing day by day which is to be considered. The only purpose of putting forward this method is, to offer a substitute for quality inspection which reduces the required labor, cost, and time. The exact recognition of rice seeds is essential for classifying rice diversity. The detection of the degree of purity of rice grains makes the piece of work hard and complex. Marketing price, its characteristics, and quality of grains depends on the type of rice. The grade and value of rice are decided by these aspects. Machine Learning Techniques were used to obtain constant standard quality and accuracy. Physical and chemical characteristics together helps in analyzing the quality of rice. Size, shape, and color of grain are some physical characteristics. Using Support Vector Machine all physical features and classification of the rice grains are obtained. By implementing these two and comparing both Support Vector Machine outputs and identifying which technique will perform the classification efficiently.},
        keywords = {Support Vector Machine, Machine Learning, Classification, Image Processing, Segmentation.},
        month = {},
        }