Optimizing Deep Residual Networks: Incorporating Separable Convolutions into ResNet50 Architecture

Arun Kumar Ravula; Ram Mohan Rao Kovvur

Author(s)

Arun Kumar Ravula Department of CSE, Research Scholar, University College of Engineering(A), Osmania University,Hyderabad,India

Ram Mohan Rao Kovvur Department of Information Technology, Vasavi College of Engineering,Hyderabad,India

Abstract

Corn crop diseases have a significant impact on agricultural productivity, making efficient and accurate detection critical for timely intervention. This paper introduces a novel approach for classifying corn crop diseases using a hybrid deep learning model that combines ResNet50 with separable convolutions. Separable convolutions decompose standard convolutions into two smaller operations-depthwise and pointwise convolutions-thus reducing computational complexity while preserving high accuracy by processing spatial and channel-wise information separately. The proposed hybrid model is trained to categorize images into four classes: healthy, Cercospora leaf spot, common rust, and Northern leaf blight, using a dataset of 38,520 images. The model's performance is compared with transfer learning approaches like Xception, ResNet50, MobileNetV2, and EfficientNetB0. Experimental results demonstrate that the proposed model achieves an accuracy of 98.3%, while significantly reducing computational complexity by decreasing the number of parameters, approximately 61.3% less than those in ResNet50.

Publication Details

Page(s) 108-116

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 2, Year: 2024

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Keywords

deep learning, Image Classification, ResNet50, Corn Crop Diseases, Separable Convolutions

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