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International Journal of Computer & Organization Trends (IJCOT) |  |
| © 2023 by IJCOT Journal | ||
| Volume - 13 Issue - 1 | ||
| Year of Publication : 2023 | ||
| Authors : K. Vasumathi, S. Selvakani, P. Rajesh | ||
| DOI : 10.14445/22492593/IJCOT-V13I1P304 |
How to Cite?
"Deep Learning for Analysing Rice Quality" International Journal of Computer and Organization Trends vol. 13, no. 1, pp. 16-22, 2023. Crossref, https://doi.org/10.14445/22492593/IJCOT-V13I1P304
Abstract
The demand for high-quality rice is a top priority in the rice manufacturing industry, as rice is the most consumed food worldwide. The physical dimensions of rice, including length, width, and thickness, are important factors in assessing rice quality. However, traditional methods of measuring these factors are time-consuming and imprecise due to manual evaluation. To meet market demands, rice quality evaluation is critical in the rice production industry, with factors such as whiteness, shape, milling degree, chalkiness, cracks, and polish being key indicators of rice quality. Ensuring rice quality is crucial to protect consumers from substandard products, particularly given that more than half the world's population relies on rice as a primary dietary staple. While rice is a rich source of energy, protein, essential vitamins and minerals, fibre, grain, beneficial antioxidants, and carbohydrates, manual evaluation of rice kernels for quality analysis is complex, time-consuming, and prone to human bias. To address these challenges and achieve high-quality rice, image processing techniques have a significant role to play. This project reviews various techniques for assessing rice quality using image processing, which is essential for seed identification and classification, grading, and quality determination in seed science and food processing sectors.
Keywords
Milling degree, Chalkiness, Whiteness, Grain, Antioxidants, and Carbohydrates.
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