Comparative Analysis of Deep Learning Models for Crop Disease Detection: A Transfer Learning Approach
It addresses crop health management for farmers in rural areas, but is incremental as it applies existing methods to a specific domain.
This research tackled crop disease detection by comparing deep learning models using transfer learning, achieving a validation accuracy of 95.76% with a custom CNN.
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.