Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Saleem, Rabia | - |
dc.contributor.author | Shah, Jamal Hussain | - |
dc.contributor.author | Sharif, Muhammad | - |
dc.contributor.author | Yasmin, Mussarat | - |
dc.contributor.author | Yong, Hwan-Seung | - |
dc.contributor.author | Cha, Jaehyuk | - |
dc.date.accessioned | 2022-07-06T02:16:53Z | - |
dc.date.available | 2022-07-06T02:16:53Z | - |
dc.date.created | 2022-01-26 | - |
dc.date.issued | 2021-12 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/138480 | - |
dc.description.abstract | Mango fruit is in high demand. So, the timely control of mango plant diseases is necessary to gain high returns. Automated recognition of mango plant leaf diseases is still a challenge as manual disease detection is not a feasible choice in this computerized era due to its high cost and the non-availability of mango experts and the variations in the symptoms. Amongst all the challenges, the segmentation of diseased parts is a big issue, being the pre-requisite for correct recognition and identification. For this purpose, a novel segmentation approach is proposed in this study to segment the diseased part by considering the vein pattern of the leaf. This leaf vein-seg approach segments the vein pattern of the leaf. Afterward, features are extracted and fused using canonical correlation analysis (CCA)-based fusion. As a final identification step, a cubic support vector machine (SVM) is implemented to validate the results. The highest accuracy achieved by this proposed model is 95.5%, which proves that the proposed model is very helpful to mango plant growers for the timely recognition and identification of diseases. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Cha, Jaehyuk | - |
dc.identifier.doi | 10.3390/app112411901 | - |
dc.identifier.scopusid | 2-s2.0-85121209715 | - |
dc.identifier.wosid | 000735791400001 | - |
dc.identifier.bibliographicCitation | Applied Sciences, v.11, no.24, pp.1 - 12 | - |
dc.relation.isPartOf | Applied Sciences | - |
dc.citation.title | Applied Sciences | - |
dc.citation.volume | 11 | - |
dc.citation.number | 24 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 12 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.subject.keywordAuthor | mango leaf | - |
dc.subject.keywordAuthor | CCA | - |
dc.subject.keywordAuthor | vein pattern | - |
dc.subject.keywordAuthor | leaf disease | - |
dc.subject.keywordAuthor | cubic SVM | - |
dc.identifier.url | https://www.mdpi.com/2076-3417/11/24/11901 | - |
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