Quality Testing of Rice Grains using SVM
Author(s):
P. Madhumitha, Sneha Samanta, A. Shireesha, G. Susmitha
Keywords:
Support Vector Machine, Machine Learning, Classification, Image Processing, Segmentation.
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.
Article Details
Unique Paper ID: 151424

Publication Volume & Issue: Volume 7, Issue 12

Page(s): 674 - 678
Article Preview & Download


Share This Article

Join our RMS



Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Latest Publication

Call For Paper

Volume 10 Issue 10

Last Date for paper submitting for March Issue is 25 June 2024

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies

Important Links

Browse Rsearch papers