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dc.contributor.authorEntuni, Chyntia Jaby
dc.contributor.authorAfendi Zulcaffle, Tengku Mohd
dc.contributor.authorKipli, Kuryati
dc.contributor.authorKurugollu, Fatih
dc.date.accessioned2021-04-15T15:12:00Z
dc.date.available2021-04-15T15:12:00Z
dc.date.issued2020-11-09
dc.identifier.citationEntuni, C.J., Afendi Zulcaffle, T.M., Kipli, K., Kurugollu, F. (2020). ‘Severity Estimation of Plant Leaf Diseases Using Segmentation Method ‘. Applied Science and Engineering Progress, 14(1), pp. 108–119.en_US
dc.identifier.issn26729156
dc.identifier.doi10.14416/j.asep.2020.11.004
dc.identifier.urihttp://hdl.handle.net/10545/625712
dc.description.abstractPlants have assumed a significant role in the history of humankind, for the most part as a source of nourishment for human and animals. However, plants typically powerless to different sort of diseases such as leaf blight, gray spot and rust. It will cause a great loss to farmers and ranchers. Therefore, an appropriate method to estimate the severity of diseases in plant leaf is needed to overcome the problem. This paper presents the fusions of the Fuzzy C-Means segmentation method with four different colour spaces namely RGB, HSV, L*a*b and YCbCr to estimate plant leaf disease severity. The percentage of performance of proposed algorithms are recorded and compared with the previous method which are K-Means and Otsu’s thresholding. The best severity estimation algorithm and colour space used to estimate the diseases severity of plant leaf is the combination of Fuzzy C-Means and YCbCr color space. The average performance of Fuzzy C-Means is 91.08% while the average performance of YCbCr is 83.74%. Combination of Fuzzy C-Means and YCbCr produce 96.81% accuracy. This algorithm is more effective than other algorithms in terms of not only better segmentation performance but also low time complexity that is 34.75s in average with 0.2697s standard deviation.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.relation.urlhttp://ojs.kmutnb.ac.th/index.php/ijst/article/view/3542en_US
dc.subjectCornen_US
dc.subjectFuzzy C-Meansen_US
dc.subjectK-Meansen_US
dc.subjectOtsu’sen_US
dc.subjectPlant leaf disease detectionen_US
dc.titleSeverity Estimation of Plant Leaf Diseases Using Segmentation Methoden_US
dc.typeArticleen_US
dc.identifier.eissn26730421
dc.contributor.departmentUniversiti Malaysia Sarawak, Malaysiaen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.identifier.journalApplied Science and Engineering Progressen_US
dc.identifier.eid2-s2.0-85100302614
dc.identifier.scopusidSCOPUS_ID:85100302614
dc.source.journaltitleApplied Science and Engineering Progress
dc.source.volume14
dc.source.issue1
dc.source.beginpage108
dc.source.endpage119
dcterms.dateAccepted2020-09-21
refterms.dateFOA2021-04-15T15:12:01Z
dc.author.detail785317en_US


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