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

TL;DR

This paper compares various deep learning models, including transfer learning techniques, for crop disease detection, demonstrating high accuracy and potential to improve agricultural practices in resource-limited rural areas.

Contribution

It provides a comparative analysis of multiple deep learning models for crop disease detection, highlighting the effectiveness of transfer learning in this domain.

Findings

Custom CNN achieved 95.76% validation accuracy.

Transfer learning models outperform traditional methods.

The system supports sustainable farming in resource-limited settings.

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.