Enhancing Strawberry Harvesting Efficiency through Yolo-v7 Object Detection Assessment

Basit Öğe Kaydı
dc.contributor.authorNergiz, Mehmet
dc.date.accessioned2025-02-22T14:13:27Z
dc.date.available2025-02-22T14:13:27Z
dc.date.issued2023
dc.departmentDicle Üniversitesien_US
dc.description.abstractStrawberry fruits which are rich in vitamin A and carotenoids offer benefits for maintaining healthy epithelial tissues and promoting maturity and growth. The intensive cultivation and swift maturation of strawberries make them susceptible to premature harvesting, leading to spoilage and financial losses for farmers. This underscores the need for an automated detection method to monitor strawberry development and accurately identify growth phases of fruits. To address this challenge, a dataset called Strawberry-DS, comprising 247 images captured in a greenhouse at the Agricultural Research Center in Giza, Egypt, is utilized in this research. The images of the dataset encompass various viewpoints, including top and angled perspectives, and illustrate six distinct growth phases: \"green\", “red”, \"white\", \"turning\", \"early-turning\" and \"late-turning\". This study employs the Yolo-v7 approach for object detection, enabling the recognition and classification of strawberries in different growth phases. The achieved mAP@.5 values for the growth phases are as follows: 0.37 for \"green,\" 0.335 for \"white,\" 0.505 for \"early-turning,\" 1.0 for \"turning,\" 0.337 for \"late-turning,\" and 0.804 for \"red\". The comprehensive performance outcomes across all classes are as follows: precision at 0.792, recall at 0.575, mAP@.5 at 0.558, and mAP@.5:.95 at 0.46. Notably, these results show the efficacy of the proposed research, both in terms of performance evaluation and visual assessment, even when dealing with distracting scenarios involving imbalanced label distributions and unclear labeling of developmental phases of the fruits. This research article yields advantages such as achieving reasonable and reliable identification of strawberries, even when operating in real-time scenarios which also leads to a decrease in expenses associated with human labor.en_US
dc.identifier.doi10.55525/tjst.1342555
dc.identifier.endpage533en_US
dc.identifier.issn1308-9099
dc.identifier.issue2en_US
dc.identifier.startpage519en_US
dc.identifier.trdizinid1274606en_US
dc.identifier.urihttps://doi.org/10.55525/tjst.1342555
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1274606
dc.identifier.urihttps://hdl.handle.net/11468/29964
dc.identifier.volume18en_US
dc.indekslendigikaynakTR-Dizin
dc.institutionauthorNergiz, Mehmet
dc.language.isoenen_US
dc.relation.ispartofTurkish Journal of Science & Technologyen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_TR_20250222
dc.subjectDeep learningen_US
dc.subjectObject detectionen_US
dc.subjectStrawberryen_US
dc.subjectAgricultureen_US
dc.subjectYolo-v7en_US
dc.titleEnhancing Strawberry Harvesting Efficiency through Yolo-v7 Object Detection Assessmenten_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

TR-Dizin İndeksli Yayınlar Koleksiyonu