{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T03:40:28Z","timestamp":1721533228614},"reference-count":19,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"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\u2019 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.<\/jats:p>","DOI":"10.3390\/sym13112073","type":"journal-article","created":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T02:14:52Z","timestamp":1635905692000},"page":"2073","source":"Crossref","is-referenced-by-count":6,"title":["Categorizing Diseases from Leaf Images Using a Hybrid Learning Model"],"prefix":"10.3390","volume":"13","author":[{"given":"Devi","family":"N.","sequence":"first","affiliation":[{"name":"Department of Information Technology, Sri Venkateswara College of Engineering, Chennai 602117, India"}]},{"given":"Leela Rani","family":"P.","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Sri Venkateswara College of Engineering, Chennai 602117, India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0337-0122","authenticated-orcid":false,"given":"Guru Gokul","family":"AR.","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Sri Venkateswara College of Engineering, Chennai 602117, India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7622-8261","authenticated-orcid":false,"given":"Raju","family":"Kannadasan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Chennai 602117, India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8579-5444","authenticated-orcid":false,"given":"Mohammed H.","family":"Alsharif","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7942-3801","authenticated-orcid":false,"given":"Abu","family":"Jahid","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1351-898X","authenticated-orcid":false,"given":"Muhammad Asghar","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Hamdard University, Islamabad Campus, Islamabad 44000, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alsharif, M.H., Kelechi, A.H., Yahya, K., and Chaudhry, S.A. 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