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Plant leaf disease classification using Wide Residual Networks

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Abstract

Most plant diseases have clear symptoms that can be recognized and diagnosed with the naked eye by an experienced plant pathologist. The process of diagnosing a disease with the naked eye is manual and slow, and its success rate depends on the ability of the pathologist. Today, many machine learning (ML) models are used to detect and classify plant diseases. In this article, an effort is made to apply the WRN (Wide Residual Networks) model in the field of plant disease classification. This model was trained to perform this classification task. WRN training is promoted using a transfer learning approach. In addition, a detailed experimental study of the WRN for the plant disease classification task on a PlantVillage test image set that includes 55,480 images is presented. The choice of WRN was mainly due to its enormous potential for image classification for various databases and its homogeneous structure. The WRN model represents a shallowest network with also a shorter training time and improved accuracy compared to the residual network (ResNet). The proposed WRN model achieved an accuracy of 99.9611% on a test set, illustrating the viability of the proposed model. The results obtained from the test showed that the model achieved the highest values ​​compared to other deep learning models in the PlantVillage datasets. Overall, the process of training WRN models provides a robust way to classify plant diseases using automated networks on a huge global scale.

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Data availability

The data used to support the findings of this study are included within the article.

Abbreviations

CNN:

Convolutional neural network

WRN:

Wide Residual Networks

CPU:

Central Processing Unit

GPU:

Graphics Processing Unit

RAM:

Random Access Memory

DDR:

Double Data Rate

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Acknowledgements

This work was supported by the Electronics and Microelectronics Laboratory.

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Authors and Affiliations

  1. Faculty of Sciences of Monastir, Laboratory of Electronics and Microelectronics, LR99ES30, Monastir University, 5000, Monastir, Tunisia

    Hmidi Alaeddine & Malek Jihene

  2. Higher Institute of Applied Sciences and Technology of Sousse, Sousse University, 4000, Sousse, Tunisia

    Malek Jihene

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  1. Hmidi Alaeddine
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Correspondence to Hmidi Alaeddine.

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Alaeddine, H., Jihene, M. Plant leaf disease classification using Wide Residual Networks. Multimed Tools Appl 82, 40953–40965 (2023). https://doi.org/10.1007/s11042-023-15226-y

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