Empowering Precision Agriculture: A Deep Learning Comparison for Rice Disease Detection

DOI

10.2991/978-94-6463-716-8_20How to use a DOI?

Keywords

CNN Deep Learning; Fine-tuning; Rice leaf diseases; Transfer learning

Abstract

Rice crops in India face significant threats from diseases caused by bacteria, fungi, and other pathogens, which lead to considerable crop losses. Traditional methods for detecting these diseases rely on manual inspections, which are time-consuming, prone to errors, and require expert knowledge. Given the vast agricultural areas and a shortage of specialists, these conventional approaches are inadequate for effective disease management. This study proposes an automated detection system using the ResNet-50 convolutional neural network (CNN) model with a transfer learning approach to classify rice leaf diseases accurately. Utilizing a real-time dataset from the Raipur region with six disease classes, ResNet-50 achieved high performance, with a training accuracy of 99.71% and a testing accuracy of 97.57%. In contrast, a VGG16 model achieved a lower testing accuracy of 91.63%. Our results demonstrate that ResNet-50’s deep learning capabilities make it a highly effective tool for accurate disease identification, potentially transforming rice disease management practices.

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.

Download article (PDF)

TY  - CONF
AU  - Poreddy Jayaraju
AU  - M. I. Thariq Hussan
AU  - Gajam Shekar
AU  - T. Rama Krishna
AU  - A. Kanagaraj
PY  - 2025
DA  - 2025/05/26
TI  - Empowering Precision Agriculture: A Deep Learning Comparison for Rice Disease Detection
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 236
EP  - 249
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_20
DO  - 10.2991/978-94-6463-716-8_20
ID  - Jayaraju2025
ER  -