Leveraging Pre-trained CNNs for Efficient Feature Extraction in Rice Leaf Disease Classification

• URL: http://arxiv.org/abs/2405.00025v1
• Date: Mon, 26 Feb 2024 07:19:48 GMT
• Title: Leveraging Pre-trained CNNs for Efficient Feature Extraction in Rice Leaf Disease Classification
• Authors: Md. Shohanur Islam Sobuj, Md. Imran Hossen, Md. Foysal Mahmud, Mahbub Ul Islam Khan,
• Abstract summary: We rigorously evaluate the impact of integrating feature extraction methodologies within pre-trained convolutional neural networks (CNNs) Histogram of Oriented Gradients (HOG) yielded substantial improvements across architectures, notably propelling the accuracy of EfficientNet-B7 from 92% to an impressive 97%. Grad-CAM unveiled that HOG integration resulted in heightened attention to disease-specific features, corroborating the performance enhancements observed.
• Score: 1.4874449172133892
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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