The study explores the impact of integrating feature extraction techniques, such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP), within pre-trained CNN models for efficient rice disease classification.
Initial investigations into baseline models, without feature extraction, revealed commendable performance, with ResNet-50 and ResNet-101 achieving accuracies of 91% and 92%, respectively.
Subsequent integration of HOG yielded substantial improvements across architectures, notably propelling the accuracy of EfficientNet-B7 from 92% to an impressive 97%. Conversely, the application of LBP demonstrated more conservative performance enhancements.
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 findings 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 research advocates 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.
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by Md. Shohanur... at arxiv.org 05-02-2024
https://arxiv.org/pdf/2405.00025.pdfDeeper Inquiries