Proceedings of the International Conference on Materials, Energy, Environment & Manufacturing Sciences & Computational Intelligence and Smart Communication (MEEMS-CISC-2024)

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Rice Leaf Disease Detection using YOLOv9

Authors
B. Sai Sudharshan1, S. V. Deepan Avinaas1, U. Sujith1, G. Anitha1, *
1Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Vengal, Chennai, 601103, Tamil Nadu, India
*Corresponding author. Email: anitha@ch.amrita.edu
Corresponding Author
G. Anitha
Available Online 16 June 2025.
DOI
10.2991/978-94-6463-762-5_6How to use a DOI?
Keywords
Rice leaf disease detection; YOLOv9; Deep learning; Bacterial Blight; Brown Spot; Leaf blast; Agricultural technology; Plant health monitoring; Precision agriculture; Image classification
Abstract

A deep learning solution for detecting rice leaf diseases based on the YOLOv9 architecture is presented in this research. We utilize a dataset comprising ten distinct classes, representing critical rice diseases: Bacterial Leaf Blight, Brown Spot, Healthy, Leaf Blast, Leaf Scald, Narrow Brown Spot, Neck Blast, Rice Hispa, Sheath Blight and Tungro. We propose a solution to the drawbacks of traditional plant disease detection tools by improving accuracy, speed and resiliency across changing conditions. Evaluations on YOLOv9 vs. YOLOv8 and other detection methods demonstrate that YOLOv9 outperforms in precision, recall and detection speed. Based on these experiments, the proposed system is shown to effectively classify both diseased and healthy rice leaves, which significantly improve early disease detection and plant health management. The findings of this research help to advance agricultural technology with improved disease monitoring and intervention strategies to protect rice crops from widespread damage.

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 the International Conference on Materials, Energy, Environment & Manufacturing Sciences & Computational Intelligence and Smart Communication (MEEMS-CISC-2024)
Series
Advances in Engineering Research
Publication Date
16 June 2025
ISBN
978-94-6463-762-5
ISSN
2352-5401
DOI
10.2991/978-94-6463-762-5_6How 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  - B. Sai Sudharshan
AU  - S. V. Deepan Avinaas
AU  - U. Sujith
AU  - G. Anitha
PY  - 2025
DA  - 2025/06/16
TI  - Rice Leaf Disease Detection using YOLOv9
BT  - Proceedings of the International Conference on Materials, Energy, Environment & Manufacturing Sciences & Computational Intelligence and Smart Communication (MEEMS-CISC-2024)
PB  - Atlantis Press
SP  - 51
EP  - 63
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-762-5_6
DO  - 10.2991/978-94-6463-762-5_6
ID  - Sudharshan2025
ER  -