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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Awoniran Olalekan Monsir
Bowen University

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Convolutional neural network-based crop disease detection model using transfer learning approach Segun Adebayo; Halleluyah Oluwatobi Aworinde; Akinwale O. Akinwunmi; Adebamiji Ayandiji; Awoniran Olalekan Monsir
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp365-374

Abstract

Crop diseases disrupt the crop's physiological constitution by affecting the crop's natural state. The physical recognition of the symptoms of the various diseases has largely been used to diagnose cassava infections. Every disease has a distinct set of symptoms that can be used to identify it. Early detection through physical identification, however, is quite difficult for a vast crop field. The use of electronic tools for illness identification then becomes necessary to promote early disease detection and control. Convolutional neural networks (CNN) were investigated in this study for the electronic identification and categorization of photographs of cassava leaves. For feature extraction and classification, the study used databases of cassava images and a deep convolutional neural network model. The methodology of this study retrained the models' current weights for visual geometry group (VGG-16), VGG-19, SqueezeNet, and MobileNet. Accuracy, loss, model complexity, and training time were all taken into consideration when evaluating how well the final layer of CNN models performed when trained on the new cassava image datasets.
Co-Authors Adebamiji Ayandiji Akinwale O. Akinwunmi Halleluyah Oluwatobi Aworinde Segun Adebayo