Wheat Powdery Mildew Detection with YOLOv8 Object Detection Model

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dc.authoridKOYCU, Nagehan Desen/0000-0003-2511-6096
dc.authoridONLER, ERAY/0000-0001-7700-3742
dc.contributor.authorOnler, Eray
dc.contributor.authorKoycu, Nagehan Desen
dc.date.accessioned2024-10-29T17:59:19Z
dc.date.available2024-10-29T17:59:19Z
dc.date.issued2024
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractWheat powdery mildew is a fungal disease that significantly impacts wheat yield and quality. Controlling this disease requires the use of resistant varieties, fungicides, crop rotation, and proper sanitation. Precision agriculture focuses on the strategic use of agricultural inputs to maximize benefits while minimizing environmental and human health effects. Object detection using computer vision enables selective spraying of pesticides, allowing for targeted application. Traditional detection methods rely on manually crafted features, while deep learning-based methods use deep neural networks to learn features autonomously from the data. You Look Only Once (YOLO) and other one-stage detectors are advantageous due to their speed and competition. This research aimed to design a model to detect powdery mildew in wheat using digital images. Multiple YOLOv8 models were trained with a custom dataset of images collected from trial areas at Tekirdag Namik Kemal University. The YOLOv8m model demonstrated the highest precision, recall, F1, and average precision values of 0.79, 0.74, 0.770, 0.76, and 0.35, respectively.
dc.identifier.doi10.3390/app14167073
dc.identifier.issn2076-3417
dc.identifier.issue16en_US
dc.identifier.scopus2-s2.0-85202449300
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/app14167073
dc.identifier.urihttps://hdl.handle.net/20.500.11776/14700
dc.identifier.volume14
dc.identifier.wosWOS:001305054700001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectYOLO
dc.subjectobject detection
dc.subjectcomputer vision
dc.subjectdeep learning
dc.subjectpowdery mildew
dc.titleWheat Powdery Mildew Detection with YOLOv8 Object Detection Model
dc.typeArticle

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