Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)

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Rice Leaf Disease Detection using YOLOv8, YOLOv9, and Detectron2 for Precision Agriculture

Authors
Mervis Mascarenhas1, *, Nadar Maheshwaran Ganeshan1, *, Anujeet Kunturkar1, *, Nilesh Mishra1, *, Teena Varma1, *
1Xavier Institute of Engineering, Mahim, Mumbai, India, 400016
*Corresponding author. Email: mervismascarenhas12@gmail.com
*Corresponding author.
*Corresponding author. Email: anujeetkunturkar12@gmail.com
*Corresponding author. Email: nileshmishra080@gmail.com
*Corresponding author. Email: teena.v@xavier.ac.in
Corresponding Authors
Mervis Mascarenhas, Nadar Maheshwaran Ganeshan, Anujeet Kunturkar, Nilesh Mishra, Teena Varma
Available Online 7 October 2025.
DOI
10.2991/978-94-6463-852-3_15How to use a DOI?
Keywords
Rice leaf disease detection; object detection; YOLOv8; YOLOv9; Detectron2; deep learning; crop disease identification; disease classification; early disease detection
Abstract

The necessity of combining computer vision and artificial intelligence (AI) technologies to improve crop health monitoring and productivity has been brought to light by the quick development of smart agricultural systems. Manual inspection is a labor-intensive and time-consuming process that is frequently used in traditional crop disease detection methods. This results in delayed diagnoses and ineffective treatment. The application of deep learning-based object detection models, such as YOLOv8, YOLOv9, and Detectron2, for real-time rice leaf disease detection is investigated in this study. The suggested system’s ability to correctly identify and categorize diseases from photos taken by inexpensive sensors is demonstrated by contrasting the performance of these models with that of conventional techniques. The results highlight how AI-powered disease detection tools can enhance decision-making, lower crop losses, and promote environmentally friendly rice farming methods.

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 MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
Series
Advances in Intelligent Systems Research
Publication Date
7 October 2025
ISBN
978-94-6463-852-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-852-3_15How 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  - Mervis Mascarenhas
AU  - Nadar Maheshwaran Ganeshan
AU  - Anujeet Kunturkar
AU  - Nilesh Mishra
AU  - Teena Varma
PY  - 2025
DA  - 2025/10/07
TI  - Rice Leaf Disease Detection using YOLOv8, YOLOv9, and Detectron2 for Precision Agriculture
BT  - Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
PB  - Atlantis Press
SP  - 230
EP  - 245
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-852-3_15
DO  - 10.2991/978-94-6463-852-3_15
ID  - Mascarenhas2025
ER  -