Comparative Analysis of Deep Learning Models for Crop Disease Detection: A Transfer Learning Approach

Saundarya Subramaniam, Shalini Majumdar, Shantanu Nadar, Kaustubh Kulkarni
6/25/2025
cs.LGcs.AI

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Abstract

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.

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Cite this paper

@article{subramaniam2025comparative,
  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},
  year   = {2025},
  eprint = {2506.20323},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2506.20323v1}
}

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