Classification of tomato leaf images for detection of plant disease using conformable polynomials image features

dc.authorid Ibrahim, Rabha W./0000-0001-9341-025X
dc.authorscopusid 57204916510
dc.authorscopusid 16319225300
dc.authorwosid Ibrahim, Rabha W./D-3312-2017
dc.contributor.author Al-Shamasneh, Ala'a R.
dc.contributor.author Ibrahim, Rabha W.
dc.date.accessioned 2024-09-11T07:43:00Z
dc.date.available 2024-09-11T07:43:00Z
dc.date.issued 2024
dc.department Okan University en_US
dc.department-temp [Al-Shamasneh, Ala'a R.] Prince Sultan Univ, Coll Comp & Informat Sci, Dept Comp Sci, Rafha St, Riyadh 11586, Saudi Arabia; [Ibrahim, Rabha W.] Istanbul Okan Univ, Fac Engn & Nat Sci, Adv Comp Lab, Istanbul 34959, Turkiye; [Ibrahim, Rabha W.] Alayen Univ, Sci Res Ctr, Informat & Commun Technol Res Grp, Nile St, Dhi Qar 64001, Iraq en_US
dc.description Ibrahim, Rabha W./0000-0001-9341-025X en_US
dc.description.abstract Plant diseases can spread rapidly, leading to significant crop losses if not detected early. By accurately identifying diseased plants, farmers can target treatment only to the affected areas, reducing the number of pesticides or fungicides needed and minimizing environmental impact. Tomatoes are among the most significant and extensively consumed crops worldwide. The main factor affecting crop yield quantity and quality is leaf disease. Various diseases can affect tomato production, impacting both yield and quality. Automated classification of leaf images allows for the early identification of diseased plants, enabling prompt intervention and control measures. Many creative approaches to diagnosing and categorizing specific illnesses have been widely employed. The manual method is costly and labor-intensive. Without the assistance of an agricultural specialist, disease detection can be facilitated by image processing combined with machine learning algorithms. In this study, the diseases in tomato leaves will be detected using new feature extraction method using conformable polynomials image features for accurate solution and faster detection of plant diseases through a machine learning model. The methodology of this study based on: center dot Preprocessing, feature extraction, dimension reduction and classification modules. center dot Conformable polynomials method is used to extract the texture features which is passed classifier. center dot The proposed texture feature is constructed by two parts the enhanced based term, and the texture detail part for textual analysis. center dot The tomato leaf samples from the plant village image dataset were used to gather the data for this model. The disease detected are 98.80 % accurate for tomato leaf images using SVM classifier. In addition to lowering financial loss, the suggested feature extraction method can help manage plant diseases effectively, improving crop yield and food security. en_US
dc.description.sponsorship Prince Sultan University, PSU en_US
dc.description.woscitationindex Emerging Sources Citation Index
dc.identifier.citationcount 0
dc.identifier.doi 10.1016/j.mex.2024.102844
dc.identifier.issn 2215-0161
dc.identifier.issn 2215-0161
dc.identifier.pmid 39092277
dc.identifier.scopus 2-s2.0-85197752423
dc.identifier.scopusquality Q1
dc.identifier.uri https://doi.org/10.1016/j.mex.2024.102844
dc.identifier.uri https://hdl.handle.net/20.500.14517/6280
dc.identifier.uri https://doi.org/10.1016/j.mex.2024.102844
dc.identifier.volume 13 en_US
dc.identifier.wos WOS:001347381100001
dc.language.iso en
dc.publisher Elsevier en_US
dc.relation.ispartof MethodsX en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 1
dc.subject Tomato disease detection en_US
dc.subject Feature extraction en_US
dc.subject Conformable polynomials en_US
dc.subject Classification en_US
dc.title Classification of tomato leaf images for detection of plant disease using conformable polynomials image features en_US
dc.type Article en_US
dc.wos.citedbyCount 1

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