Real?Time Detection and Segmentation of Tomato Pests with YOLOv8

Basit Öğe Kaydı
dc.contributor.authorŞahin, Yavuz Selim
dc.contributor.authorGençer, Nimet Sema
dc.contributor.authorŞahin, Hasan
dc.date.accessioned2026-02-08T15:05:08Z
dc.date.available2026-02-08T15:05:08Z
dc.date.issued2026
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractTomato (Solanum lycopersicum L.) is vital for global nutrition and economic stability, yet it is threatened by pests such as Tuta absoluta, Helicoverpa armigera, and Bemisia tabaci. Effective pest management is crucial to prevent significant crop losses. Traditional pest detection methods relying on human observation are labor-intensive, time consuming, and prone to errors. In contrast, artificial intelligence (AI)based models such as YOLO provide timely and accurate pest identification, enhancing pest management practices. In this study, images captured throughout the tomato plant’s development, from seedling to fruit stage, were used for model training. The capabilities of the YOLOv8 model in detecting and segmenting tomato pests were evaluated. The results demonstrated significant improvements in both detection and segmentation tasks, with precision and recall reaching 98.91% and 98.98% for detection, and 97.47% and 98.81% for segmentation, respectively. These findings underscore the accuracy and robustness of the YOLOv8 model in monitoring diverse pest species, highlighting its potential to improve agricultural pest management practices. Although YOLO-based detectors have recently been tested on a limited set of pest species, comprehensive field-scale evaluations remain scarce. By assessing YOLOv8 across eleven pest taxa under commercial field conditions, this study delivers among the more comprehensive practice-oriented benchmarks to date for multi-species pest monitoring. This research suggests that integrating AI models like YOLOv8 into pest monitoring systems can contribute to more efficient and sustainable agricultural practices by minimizing human error and labor demands. Furthermore, future applications could extend this approach to other crops and pest species, validating the model’s versatility and supporting long-term farming sustainability.
dc.identifier.doi10.15832/ankutbd.1681258
dc.identifier.endpage129
dc.identifier.issn1300-7580
dc.identifier.issn2148-9297
dc.identifier.issue1
dc.identifier.scopus2-s2.0-105029042911
dc.identifier.scopusqualityN/A
dc.identifier.startpage119
dc.identifier.urihttps://doi.org/10.15832/ankutbd.1681258
dc.identifier.urihttps://hdl.handle.net/20.500.12885/4458
dc.identifier.volume32
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAnkara Üniversitesi
dc.relation.ispartofTarım Bilimleri Dergisi
dc.relation.ispartofJournal of Agricultural Sciences
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_20260207
dc.subjectAgricultural Biotechnology Diagnostics
dc.subjectTarımsal Biyoteknolojik Tanılama
dc.titleReal?Time Detection and Segmentation of Tomato Pests with YOLOv8
dc.typeArticle

Dosyalar

Koleksiyon

Öksüz Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu