Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

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Automated Grain Quality Testing Using CNN, Densenet, Mobilenet

Authors
M. S. B. Kasyapa1, *, B. Sumalya1, J. Mahesh Chandra1, P. Bhanu Prasad1, S. Adhitya1
1Department of IT, Vignan Institute of Technology and Science, Deshmuki, Hyderabad, TS, India
*Corresponding author. Email: msbkasyapa@gmail.com
Corresponding Author
M. S. B. Kasyapa
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_95How to use a DOI?
Keywords
Quality; CNN; Densenet; Mobilenet
Abstract

Millions of people around the world depend on rice as their main crop. And the quality of the grain has a great influence on its market value and social acceptance. Traditional methods of counting and classifying rice grains are time-consuming and manual. This results in inconsistent and inaccurate final products. Deep learning (DL) techniques have shown a lot of promise in recent years in the automatic assessment of grain quality. By collecting many images of rice grains We created a DL-based method to analyze the quality of rice grains doing this job in the 1990s. Size, shape, color, texture, and flawlessness are just some of the quality criteria used to train DL models to classify rice grains. The results showed that the DL model had a high accuracy rate in classifying rice grains. Moreover, the DL study revealed information about the basic chemical and physical properties of rice grains. This can be used to improve processing and processing characteristics.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_95How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - M. S. B. Kasyapa
AU  - B. Sumalya
AU  - J. Mahesh Chandra
AU  - P. Bhanu Prasad
AU  - S. Adhitya
PY  - 2025
DA  - 2025/11/04
TI  - Automated Grain Quality Testing Using CNN, Densenet, Mobilenet
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1144
EP  - 1151
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_95
DO  - 10.2991/978-94-6463-858-5_95
ID  - Kasyapa2025
ER  -