This research presents the development of an Artificial Intelligence (AI) - driven crop disease detection system designed to assist farmers in rural areas with limited resources. We aim to compare different deep learning models for a comparative analysis, focusing on their efficacy in transfer learning. By leveraging deep learning models, including EfficientNet, ResNet101, MobileNetV2, and our custom CNN, which achieved a validation accuracy of 95.76%, the system effectively classifies plant diseases. This research demonstrates the potential of transfer learning in reshaping agricultural practices, improving crop health management, and supporting sustainable farming in rural environments.
@article{arxiv.2506.20323,
title = {Comparative Analysis of Deep Learning Models for Crop Disease Detection: A Transfer Learning Approach},
author = {Saundarya Subramaniam and Shalini Majumdar and Shantanu Nadar and Kaustubh Kulkarni},
journal= {arXiv preprint arXiv:2506.20323},
year = {2025}
}