  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.
