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Machine Learning-based Detection and Classification of Walnut Fungi Diseases

Muhammad Alyas Khan1, Mushtaq Ali1, Mohsin Shah2, Toqeer Mahmood3, Muneer Ahmad4, NZ Jhanjhi5, Mohammad Arif Sobhan Bhuiyan6,*, Emad Sami Jaha7
1 Department of Information Technology, Hazara University, Mansehra, Pakistan
2 Department of Telecommunication, Hazara University, Mansehra, Pakistan
3 Department of Computer Science, National Textile University, Faisalabad, Pakistan
4 Department of Information Systems, Faculty of Computer Science & Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
5 School of Computer Science and Engineering, SCE, Taylor’s University, Subang Jaya, Malaysia
6 Electrical and Electronics Engineering, Xiamen University Malaysia, Bandar Sunsuria, 43900 Sepang, Selangor, Malaysia
7 Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
* Corresponding Author: Mohammad Arif Sobhan Bhuiyan. Email:

Intelligent Automation & Soft Computing 2021, 30(3), 771-785. https://doi.org/10.32604/iasc.2021.018039

Received 22 February 2021; Accepted 09 May 2021; Issue published 20 August 2021

Abstract

Fungi disease affects walnut trees worldwide because it damages the canopies of the trees and can easily spread to neighboring trees, resulting in low quality and less yield. The fungal disease can be treated relatively easily, and the main goal is preventing its spread by automatic early-detection systems. Recently, machine learning techniques have achieved promising results in many applications in the agricultural field, including plant disease detection. In this paper, an automatic machine learning-based detection method for identifying walnut diseases is proposed. The proposed method first resizes a leaf’s input image and pre-processes it using intensity adjustment and histogram equalization. After that, the detected infected area of the leaf is segmented using the Otsu thresholding algorithm. The proposed method extracts color and shape features from the leaf’s segmented area using the gray level co-occurrence matrix (GLCM) and color moments. Finally, the extracted features are provided to the back-propagation neural network (BPNN) classifier to detect and classify walnut leaf diseases. Experimental results demonstrate that the proposed method’s detection accuracy is 95.3%, which is significantly higher than those of the state-of-the-art techniques. The proposed method assists farmers in detecting diseases affecting walnut trees and thus enables them to generate more revenue by improving the productivity and quality of their walnuts.

Keywords

Machine learning; walnut disease; fungi disease; color image features; texture image features

Cite This Article

M. Alyas Khan, M. Ali, M. Shah, T. Mahmood, M. Ahmad et al., "Machine learning-based detection and classification of walnut fungi diseases," Intelligent Automation & Soft Computing, vol. 30, no.3, pp. 771–785, 2021.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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