A Survey on Rice Grain Classification from Traditional Methods to Deep Learning Approaches

M. Niranjana, F. Kurus Malai Selvi

2025

Abstract

Challenges persist in developing a suitable method to distinguish cultivated quality rice seeds, which can be estimated based on their characteristics. To avoid rice grain varieties from getting incorrectly labelled, the quality and types of rice grains must be identified. In this paper, classification rice grains are analysed and study is done on different types of algorithms for every stage. Generally, visual observations are made with specialists using specific devices measuring various properties. The resultant data are fed into different stages using various algorithms which are discussed in detail. This study reviews machine learning techniques to differentiate between rice seeds using different types of algorithms. Every stage is analysed under different objectives and important conclusions that gives extensions to the next stage of the research.

Paper Citation

in Harvard Style

Niranjana M. and Selvi F. (2025). A Survey on Rice Grain Classification from Traditional Methods to Deep Learning Approaches. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 311-320. DOI: 10.5220/0013912200004919

in Bibtex Style

@conference{icrdicct`2525,
author={M. Niranjana and F. Selvi},
title={A Survey on Rice Grain Classification from Traditional Methods to Deep Learning Approaches},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={311-320},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013912200004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - A Survey on Rice Grain Classification from Traditional Methods to Deep Learning Approaches
SN - 978-989-758-777-1
AU - Niranjana M.
AU - Selvi F.
PY - 2025
SP - 311
EP - 320
DO - 10.5220/0013912200004919
PB - SciTePress