Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)

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A Comprehensive Review on Various Deep Learning Techniques for Identification and Classification of Millets

Authors
M. Ravichandran1, *, K. Jagan Mohan2, S. Sivasankaran3
1Reasearch Scholar, Department of IT, Annamalai University, Chidambaram, India
2Associate Professor, Department of IT, Annamalai University, Chidambaram, India
3Assisstant Professor, Department of CSE, IFET College of Engineering, Villupuram, India
*Corresponding author. Email: mkjkravi@gmail.com
Corresponding Author
M. Ravichandran
Available Online 31 March 2026.
DOI
10.2991/978-94-6239-616-6_38How to use a DOI?
Keywords
Millet classification; Deep learning; Convolutional Neural Networks (CNN); Transfer learning; Hyperspectral imaging; Crop identification; Agricultural AI; Food security
Abstract

Millets, including pearl, finger, foxtail, proso, kodo, and little millet, are small-grained cereals known for their climate resilience. These crops require enhanced digital tools for species and cultivar recognition, disease detection, and origin and quality assessment. ML techniques based on neural networks, such as CNNs or combined models, have helped in millet classification from diverse data sources, including leaf and seed images, hyperspectral scans, and field-level imagery. This review elaborates on findings made between 2020 and 2025 while contrasting model families, datasets, and reported metrics across the modalities, pointing out key challenges such as diversity in datasets, environmental variability, explainability, and edge deployment.

Copyright
© 2026 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 the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
31 March 2026
ISBN
978-94-6239-616-6
ISSN
1951-6851
DOI
10.2991/978-94-6239-616-6_38How to use a DOI?
Copyright
© 2026 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. Ravichandran
AU  - K. Jagan Mohan
AU  - S. Sivasankaran
PY  - 2026
DA  - 2026/03/31
TI  - A Comprehensive Review on Various Deep Learning Techniques for Identification and Classification of Millets
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 507
EP  - 517
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_38
DO  - 10.2991/978-94-6239-616-6_38
ID  - Ravichandran2026
ER  -