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. 2022 Nov 24:13:1064854.
doi: 10.3389/fpls.2022.1064854. eCollection 2022.

An effective deep learning approach for the classification of Bacteriosis in peach leave

Muneer Akbar  1 , Mohib Ullah  1 , Babar Shah  2 , Rafi Ullah Khan  1 , Tariq Hussain  3 , Farman Ali  4 , Fayadh Alenezi  5 , Ikram Syed  6 , Kyung Sup Kwak  7
Affiliations

An effective deep learning approach for the classification of Bacteriosis in peach leave

Muneer Akbar et al. Front Plant Sci. .

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.

Keywords: Bacteriosis classification; Bacteriosis detection; LWNet; convolutional neural network (CNN); deep learning; peach leaves.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Data flow chart of the proposed LWNet model methodology.
Figure 2
Figure 2
Healthy image of the peach leaf.
Figure 3
Figure 3
Bacterial spot of the peach leaf.
Figure 4
Figure 4
(A) Original size of healthy leaf (B) original size of bacteriosis leaf.
Figure 5
Figure 5
(A) Resize image of healthy leaf (B) resize image of bacteriosis leaf.
Figure 6
Figure 6
(A) Original image (B) brighten image.
Figure 7
Figure 7
Activation function works.
Figure 8
Figure 8
Sigmoid function.
Figure 9
Figure 9
Hyperbolic tangent function.
Figure 10
Figure 10
ReLU activation function.
Figure 11
Figure 11
Training and validation models accuracy.
Figure 12
Figure 12
Training and validation model loss.
Figure 13
Figure 13
Performance parameter of the LWNet model.
Figure 14
Figure 14
Confusion matrix.
Figure 15
Figure 15
Accuracy (%) of different models.
Figure 16
Figure 16
Accuracy gap of different models.
Figure 17
Figure 17
Mean square error of the simulation results.
See this image and copyright information in PMC

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