Categorizing Diseases from Leaf Images Using a Hybrid Learning Model

Plant diseases pose a severe threat to crop yield. This necessitates the rapid identification of diseases affecting various crops using modern technologies. Many researchers have developed solutions to the problem of identifying plant diseases, but it is still considered a critical issue due to the...

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Autores principales:	Devi N., Leela Rani P., Guru Gokul AR., Raju Kannadasan, Mohammed H. Alsharif, Abu Jahid, Muhammad Asghar Khan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	CNN GLCM image processing k-means clustering leaf diseases Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/509887ec13b44348a3b4f74e091b1330
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spelling	oai:doaj.org-article:509887ec13b44348a3b4f74e091b13302021-11-25T19:06:30ZCategorizing Diseases from Leaf Images Using a Hybrid Learning Model10.3390/sym131120732073-8994https://doaj.org/article/509887ec13b44348a3b4f74e091b13302021-11-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2073https://doaj.org/toc/2073-8994Plant diseases pose a severe threat to crop yield. This necessitates the rapid identification of diseases affecting various crops using modern technologies. Many researchers have developed solutions to the problem of identifying plant diseases, but it is still considered a critical issue due to the lack of infrastructure in many parts of the world. This paper focuses on detecting and classifying diseases present in the leaf images by adopting a hybrid learning model. The proposed hybrid model uses k-means clustering for detecting the disease area from the leaf and a Convolutional Neural Network (CNN) for classifying the type of disease based on comparison between sampled and testing images. The images of leaves under consideration may be symmetrical or asymmetrical in shape. In the proposed methodology, the images of various leaves from diseased plants were first pre-processed to filter out the noise present to get an enhanced image. This improved image enabled detection of minute disease-affected regions. The infected areas were then segmented using k-means clustering algorithm that locates only the infected (diseased) areas by masking the leaves’ green (healthy) regions. The grey level co-occurrence matrix (GLCM) methodology was used to fetch the necessary features from the affected portions. Since the number of fetched features was insufficient, more synthesized features were included, which were then given as input to CNN for training. Finally, the proposed hybrid model was trained and tested using the leaf disease dataset available in the UCI machine learning repository to examine the characteristics between trained and tested images. The hybrid model proposed in this paper can detect and classify different types of diseases affecting different plants with a mean classification accuracy of 92.6%. To illustrate the efficiency of the proposed hybrid model, a comparison was made against the following classification approaches viz., support vector machine, extreme learning machine-based classification, and CNN. The proposed hybrid model was found to be more effective than the other three.Devi N.Leela Rani P.Guru Gokul AR.Raju KannadasanMohammed H. AlsharifAbu JahidMuhammad Asghar KhanMDPI AGarticleCNNGLCMimage processingk-means clusteringleaf diseasesMathematicsQA1-939ENSymmetry, Vol 13, Iss 2073, p 2073 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	CNN GLCM image processing k-means clustering leaf diseases Mathematics QA1-939
spellingShingle	CNN GLCM image processing k-means clustering leaf diseases Mathematics QA1-939 Devi N. Leela Rani P. Guru Gokul AR. Raju Kannadasan Mohammed H. Alsharif Abu Jahid Muhammad Asghar Khan Categorizing Diseases from Leaf Images Using a Hybrid Learning Model
description	Plant diseases pose a severe threat to crop yield. This necessitates the rapid identification of diseases affecting various crops using modern technologies. Many researchers have developed solutions to the problem of identifying plant diseases, but it is still considered a critical issue due to the lack of infrastructure in many parts of the world. This paper focuses on detecting and classifying diseases present in the leaf images by adopting a hybrid learning model. The proposed hybrid model uses k-means clustering for detecting the disease area from the leaf and a Convolutional Neural Network (CNN) for classifying the type of disease based on comparison between sampled and testing images. The images of leaves under consideration may be symmetrical or asymmetrical in shape. In the proposed methodology, the images of various leaves from diseased plants were first pre-processed to filter out the noise present to get an enhanced image. This improved image enabled detection of minute disease-affected regions. The infected areas were then segmented using k-means clustering algorithm that locates only the infected (diseased) areas by masking the leaves’ green (healthy) regions. The grey level co-occurrence matrix (GLCM) methodology was used to fetch the necessary features from the affected portions. Since the number of fetched features was insufficient, more synthesized features were included, which were then given as input to CNN for training. Finally, the proposed hybrid model was trained and tested using the leaf disease dataset available in the UCI machine learning repository to examine the characteristics between trained and tested images. The hybrid model proposed in this paper can detect and classify different types of diseases affecting different plants with a mean classification accuracy of 92.6%. To illustrate the efficiency of the proposed hybrid model, a comparison was made against the following classification approaches viz., support vector machine, extreme learning machine-based classification, and CNN. The proposed hybrid model was found to be more effective than the other three.
format	article
author	Devi N. Leela Rani P. Guru Gokul AR. Raju Kannadasan Mohammed H. Alsharif Abu Jahid Muhammad Asghar Khan
author_facet	Devi N. Leela Rani P. Guru Gokul AR. Raju Kannadasan Mohammed H. Alsharif Abu Jahid Muhammad Asghar Khan
author_sort	Devi N.
title	Categorizing Diseases from Leaf Images Using a Hybrid Learning Model
title_short	Categorizing Diseases from Leaf Images Using a Hybrid Learning Model
title_full	Categorizing Diseases from Leaf Images Using a Hybrid Learning Model
title_fullStr	Categorizing Diseases from Leaf Images Using a Hybrid Learning Model
title_full_unstemmed	Categorizing Diseases from Leaf Images Using a Hybrid Learning Model
title_sort	categorizing diseases from leaf images using a hybrid learning model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/509887ec13b44348a3b4f74e091b1330
work_keys_str_mv	AT devin categorizingdiseasesfromleafimagesusingahybridlearningmodel AT leelaranip categorizingdiseasesfromleafimagesusingahybridlearningmodel AT gurugokular categorizingdiseasesfromleafimagesusingahybridlearningmodel AT rajukannadasan categorizingdiseasesfromleafimagesusingahybridlearningmodel AT mohammedhalsharif categorizingdiseasesfromleafimagesusingahybridlearningmodel AT abujahid categorizingdiseasesfromleafimagesusingahybridlearningmodel AT muhammadasgharkhan categorizingdiseasesfromleafimagesusingahybridlearningmodel
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Categorizing Diseases from Leaf Images Using a Hybrid Learning Model

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