A Channel Attention-Driven Hybrid CNN Framework for Paddy Leaf Disease Detection

TL;DR

This paper introduces a novel hybrid deep learning model with channel attention and Swish ReLU activation for early rice leaf disease detection, achieving high accuracy and F1-score, thus enhancing agricultural disease management.

Contribution

The paper presents a new hybrid CNN framework that extends Squeeze-and-Excitation networks with channel attention and Swish ReLU, improving disease classification in rice leaves.

Findings

Achieved 99.76% F1-score in disease detection

Surpassed existing models in accuracy and performance

Demonstrated effectiveness of channel attention and Swish ReLU in agriculture

Abstract

Farmers face various challenges when it comes to identifying diseases in rice leaves during their early stages of growth, which is a major reason for poor produce. Therefore, early and accurate disease identification is important in agriculture to avoid crop loss and improve cultivation. In this research, we propose a novel hybrid deep learning (DL) classifier designed by extending the Squeeze-and-Excitation network architecture with a channel attention mechanism and the Swish ReLU activation function. The channel attention mechanism in our proposed model identifies the most important feature channels required for classification during feature extraction and selection. The dying ReLU problem is mitigated by utilizing the Swish ReLU activation function, and the Squeeze-andExcitation blocks improve information propagation and cross-channel interaction. Upon evaluation, our model achieved a high F1-score of 99.76% and an accuracy of 99.74%, surpassing the performance of existing models. These outcomes demonstrate the potential of state-of-the-art DL techniques in agriculture, contributing to the advancement of more efficient and reliable disease detection systems.