Determination of the severity level of yellow rust disease in wheat by using convolutional neural networks

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dc.authoridVarcin, Fatih/0000-0002-5100-3012
dc.authoridErbay, Hasan/0000-0002-7555-541X
dc.authoridHAYIT, TOLGA/0000-0001-5367-7988
dc.contributor.authorHayit, Tolga
dc.contributor.authorErbay, Hasan
dc.contributor.authorVarcin, Fatih
dc.contributor.authorHayit, Fatma
dc.contributor.authorAkci, Nilufer
dc.date.accessioned2025-01-21T16:37:29Z
dc.date.available2025-01-21T16:37:29Z
dc.date.issued2021
dc.departmentKırıkkale Üniversitesi
dc.description.abstractYellow rust disease caused by Puccinia striiformis f. sp. tritici, a pathogen in wheat, results in significant losses in wheat production worldwide due to its high destructive property. On the other side, yellow rust can be taken under control by growing resistant cultivars, by the application of fungicides, and by the use of appropriate cultural practices. Thus, it is crucial to detect the disease at an early stage. The current study offers to use computerized models in determining the infection type of yellow rust disease in wheat. Herein, a deep convolutional neural networks-based model, named Yellow-Rust-Xception, was proposed. The model inputs the wheat leaf image and classifies it as no disease, resistant, moderately resistant, moderately susceptible, or susceptible according to the rust severity, i.e. percentage. The convolutional neural networks, a state-of-art approach, have layered structures those inspired by the human brain and able to learn discriminative features from data automatically; thus networks performance match and even surpass humans in task-specific applications, a newly developed dataset containing yellow rust-infected wheat leaf images, was used to train, validate, and test Yellow-Rust-Xception, in result, the test accuracy was 91%. Thus, Yellow-Rust-Xception can be used in determining wheat yellow rust and its severity level.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TuBTAK) [120O960]
dc.description.sponsorshipThis study was supported with project 120O960 by The Scientific and Technological Research Council of Turkey (TuBTAK). In addition, we would like to thank the Republic of Turkey Ministry of Agriculture and Forestry Directorate of Field Crops Central Research Institute which allowed us to use its resources, Mehmet AYDOGDU who is a permanent worker there, smail KARAKAS who is we consulted for photoshoots and used his digital camera and Yozgat Bozok University Boazlyan Vocational High School Manager Asst. Prof. Mustafa KOCAKAYA supported us.
dc.identifier.doi10.1007/s42161-021-00886-2
dc.identifier.endpage934
dc.identifier.issn1125-4653
dc.identifier.issn2239-7264
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85109256991
dc.identifier.scopusqualityQ2
dc.identifier.startpage923
dc.identifier.urihttps://doi.org/10.1007/s42161-021-00886-2
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24488
dc.identifier.volume103
dc.identifier.wosWOS:000669758300002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of Plant Pathology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectDeep Learning; Image classification; Image pre-processing; Puccinia striiformis
dc.titleDetermination of the severity level of yellow rust disease in wheat by using convolutional neural networks
dc.typeArticle

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