AI-Driven Paddy Leaf Disease Classification and Prediction using DenseNet-121

V. Sahasranamam; T. Ramesh; R. Rajeswari; A. Karthikkumar; D. Muthumanickam

doi:10.2991/978-94-6463-740-3_24

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AI-Driven Paddy Leaf Disease Classification and Prediction using DenseNet-121

Authors

V. Sahasranamam¹^{, *}, T. Ramesh², R. Rajeswari³, A. Karthikkumar⁴, D. Muthumanickam⁵

¹Department of Information Technology, Bharathiyar University, Coimbatore, 641 046, India

²Department of Information Technology, Bharathiyar University, Coimbatore, 641 046, India

³Department of Computer Applications, Bharathiyar University, Coimbatore, 641 046, India

⁴Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore, 641 003, India

⁵Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore, 641 003, India

^*Corresponding author. Email: sahasra14@yahoo.com

Corresponding Author

V. Sahasranamam

Available Online 25 June 2025.

DOI: 10.2991/978-94-6463-740-3_24 How to use a DOI?
Keywords: Deep Learning; Paddy Cultivation; Paddy Leaf Disease Prediction; Precision Agriculture; DenseNet-121
Abstract: Paddy cultivation is a cornerstone of agricultural production worldwide in India and many other rice-growing regions. Efficient management of paddy fields is essential for maximizing yield and assuring food security. Paddy leaf health is a critical indicator of the overall condition of rice crops, as it reflects the presence of diseases, nutrient deficiencies, and pest infestations. Accurate and timely prediction of paddy leaf health can significantly enhance crop management methods by permitting farmers to take proactive measures. The first objective of this research is to propose the DenseNet-121 model for predicting disease-affected paddy leaves. The proposed DenseNet-121 model is trained using labeled datasets of healthy and diseased leaf images. The proposed Densenet121 model optimizes resource use, reduces crop losses, and enhances overall productivity compared with ResNet50. Densenet121 model is efficient in the classification and identification of paddy leaf disease with a test accuracy of 97.16% compared to ResNet50 and VGG-16 Deep Learning Models.
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 6th International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR 2024)
Series: Advances in Intelligent Systems Research
Publication Date: 25 June 2025
ISBN: 978-94-6463-740-3
ISSN: 1951-6851
DOI: 10.2991/978-94-6463-740-3_24 How to use a DOI?
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

ris enw bib

TY  - CONF
AU  - V. Sahasranamam
AU  - T. Ramesh
AU  - R. Rajeswari
AU  - A. Karthikkumar
AU  - D. Muthumanickam
PY  - 2025
DA  - 2025/06/25
TI  - AI-Driven Paddy Leaf Disease Classification and Prediction using DenseNet-121
BT  - Proceedings of the 6th International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR 2024)
PB  - Atlantis Press
SP  - 272
EP  - 292
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-740-3_24
DO  - 10.2991/978-94-6463-740-3_24
ID  - Sahasranamam2025
ER  -

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