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Recognition and Classification of Pomegranate Leaves Diseases by Image Processing and Machine Learning Techniques

Mangena Venu Madhavan1, Dang Ngoc Hoang Thanh2, Aditya Khamparia1,*, Sagar Pande1, Rahul Malik1, Deepak Gupta3

1 School of Computer Science Engineering, Lovely Professional University, Punjab, 144411, India
2 University of Economics Ho Chi Minh City, Ho Chi Minh City, 70000, Vietnam
3 Maharaja Agrasen Institute of Technology, 110086, India

* Corresponding Author: Aditya Khamparia. Email:

(This article belongs to this Special Issue: Intelligent Decision Support Systems for Complex Healthcare Applications)

Computers, Materials & Continua 2021, 66(3), 2939-2955.


Disease recognition in plants is one of the essential problems in agricultural image processing. This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly. The framework utilizes image processing techniques such as image acquisition, image resizing, image enhancement, image segmentation, ROI extraction (region of interest), and feature extraction. An image dataset related to pomegranate leaf disease is utilized to implement the framework, divided into a training set and a test set. In the implementation process, techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features. An image classification will then be implemented by combining a supervised learning model with a support vector machine. The proposed framework is developed based on MATLAB with a graphical user interface. According to the experimental results, the proposed framework can achieve 98.39% accuracy for classifying diseased and healthy leaves. Moreover, the framework can achieve an accuracy of 98.07% for classifying diseases on pomegranate leaves.


Cite This Article

M. Venu Madhavan, D. Ngoc Hoang Thanh, A. Khamparia, S. Pande, R. Malik et al., "Recognition and classification of pomegranate leaves diseases by image processing and machine learning techniques," Computers, Materials & Continua, vol. 66, no.3, pp. 2939–2955, 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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