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

arXiv:2405.00025v1 Announce Type: cross
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 …

abstract arxiv classification cnns convolutional convolutional neural networks cs.cv cs.lg disease extraction feature feature extraction impact investigations networks neural networks performance research study type