  Rice disease classification is a critical task in agricultural research, and
in this study, we rigorously evaluate the impact of integrating feature
extraction methodologies within pre-trained convolutional neural networks
(CNNs). Initial investigations into baseline models, devoid of feature
extraction, revealed commendable performance with ResNet-50 and ResNet-101
achieving accuracies of 91% and 92%, respectively. Subsequent integration of
Histogram of Oriented Gradients (HOG) yielded substantial improvements across
architectures, notably propelling the accuracy of EfficientNet-B7 from 92\% to
an impressive 97%. Conversely, the application of Local Binary Patterns (LBP)
demonstrated more conservative performance enhancements. Moreover, employing
Gradient-weighted Class Activation Mapping (Grad-CAM) unveiled that HOG
integration resulted in heightened attention to disease-specific features,
corroborating the performance enhancements observed. Visual representations
further validated HOG's notable influence, showcasing a discernible surge in
accuracy across epochs due to focused attention on disease-affected regions.
These results underscore the pivotal role of feature extraction, particularly
HOG, in refining representations and bolstering classification accuracy. The
study's significant highlight was the achievement of 97% accuracy with
EfficientNet-B7 employing HOG and Grad-CAM, a noteworthy advancement in
optimizing pre-trained CNN-based rice disease identification systems. The
findings advocate for the strategic integration of advanced feature extraction
techniques with cutting-edge pre-trained CNN architectures, presenting a
promising avenue for substantially augmenting the precision and effectiveness
of image-based disease classification systems in agricultural contexts.
