A deep learning based approach for the detection of diseases in pepper and potato leaves

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dc.contributor.authorsert, eser
dc.date.accessioned2025-10-24T18:04:24Z
dc.date.available2025-10-24T18:04:24Z
dc.date.issued2021
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractThe present study proposes a Faster R-CNN Object Detection Approach with GoogLeNet Classifier (Faster R-CNN-GC) using image stitching, Faster R-CNN and GoogLeNet to detect pepper and potato leaves as well as leaf diseases in them. It is widely known that for a successful object detection performance, Faster R-CNN requires performing image labelling on a very high number of data, which will later train Faster R-CNN. However, this process is often very time-consuming. The present study mainly aims to shorten this process by designing an object detection approach which benefits from Faster R-CNN and GoogLeNet architecture. Firstly, Faster R-CNN and GoogLeNet were trained. Later, for the testing process, some of two-piece images were combined using an image stitching approach. Finally, using Faster R-CNN and GoogLeNet, pepper and potato leaves are detected and diseases are written on them. In addition, the proposed system was compared with Faster R-CNN Object Detection Approach with AlexNet Classifier (Faster R-CNN-AC), Faster R-CNN Object Detection Approach with SequezeNet Classifier (Faster R-CNN-SC) and Faster R-CNN. The findings of the experimental studies demonstrated that Faster R-CNN-GC displayed a higher object detection performance compared to other approaches.
dc.identifier.doi10.7161/omuanajas.805152
dc.identifier.endpage178
dc.identifier.issn1308-8750
dc.identifier.issn1308-8769
dc.identifier.issue2
dc.identifier.startpage167
dc.identifier.trdizinid1151937
dc.identifier.urihttps://doi.org/10.7161/omuanajas.805152
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1151937
dc.identifier.urihttps://hdl.handle.net/20.500.12899/2811
dc.identifier.volume36
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofAnadolu Tarım Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzTR-Dizin_20251023
dc.subjectBilgisayar Bilimleri
dc.subjectYazılım Mühendisliği
dc.subjectBahçe Bitkileri
dc.subjectGörüntüleme Bilimi ve Fotoğraf Teknolojisi
dc.subjectBitki Bilimleri
dc.subjectBilgisayar Bilimleri
dc.subjectYapay Zeka
dc.subjectAlexNet
dc.subjectFaster R-CNN
dc.subjectObject Detection
dc.subjectGoogLeNet
dc.subjectLeaf Disease Detection
dc.subjectSequezeNet
dc.titleA deep learning based approach for the detection of diseases in pepper and potato leaves
dc.typeArticle

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