Document Type
Article
Source of Publication
Frontiers in Plant Science
Publication Date
11-24-2022
Abstract
Bacteriosis is one of the most prevalent and deadly infections that affect peach crops globally. Timely detection of Bacteriosis disease is essential for lowering pesticide use and preventing crop loss. It takes time and effort to distinguish and detect Bacteriosis or a short hole in a peach leaf. In this paper, we proposed a novel LightWeight (WLNet) Convolutional Neural Network (CNN) model based on Visual Geometry Group (VGG-19) for detecting and classifying images into Bacteriosis and healthy images. Profound knowledge of the proposed model is utilized to detect Bacteriosis in peach leaf images. First, a dataset is developed which consists of 10000 images: 4500 are Bacteriosis and 5500 are healthy images. Second, images are preprocessed using different steps to prepare them for the identification of Bacteriosis and healthy leaves. These preprocessing steps include image resizing, noise removal, image enhancement, background removal, and augmentation techniques, which enhance the performance of leaves classification and help to achieve a decent result. Finally, the proposed LWNet model is trained for leaf classification. The proposed model is compared with four different CNN models: LeNet, Alexnet, VGG-16, and the simple VGG-19 model. The proposed model obtains an accuracy of 99%, which is higher than LeNet, Alexnet, VGG-16, and the simple VGG-19 model. The achieved results indicate that the proposed model is more effective for the detection of Bacteriosis in peach leaf images, in comparison with the existing models.
DOI Link
ISSN
Publisher
Frontiers Media SA
Volume
13
First Page
1064854
Last Page
1064854
Disciplines
Computer Sciences | Life Sciences
Keywords
peach leaves, Bacteriosis detection, Bacteriosis classification, deep learning, convolutional neural network (CNN), LWNet
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Akbar, Muneer; Ullah, Mohib; Shah, Babar; Khan, Rafi Ullah; Hussain, Tariq; Ali, Farman; Alenezi, Fayadh; Syed, Ikram; and Kwak, Kyung Sup, "An effective deep learning approach for the classification of Bacteriosis in peach leave" (2022). All Works. 5502.
https://zuscholars.zu.ac.ae/works/5502
Indexed in Scopus
no
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series