TomatoweedDet: a real-field multi-class weed detection dataset and YOLO benchmark for tomato production systems

Alperen Mehmet Sevinç, Çağrı Vakkas Yıldırım, Ahmet Kerim Ağırman

Open source

DOI: 10.3389/fpls.2026.1861020
Published: 2026-06-18
Container: Frontiers in Plant Science
Publisher: Frontiers Media SA
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3389/fpls.2026.1861020,
  title = {TomatoweedDet: a real-field multi-class weed detection dataset and YOLO benchmark for tomato production systems},
  author = {Alperen Mehmet Sevinç and Çağrı Vakkas Yıldırım and Ahmet Kerim Ağırman},
  year = {2026},
  journal = {Frontiers in Plant Science},
  doi = {10.3389/fpls.2026.1861020},
  url = {https://doi.org/10.3389/fpls.2026.1861020}
}

RIS

TY  - JOUR
TI  - TomatoweedDet: a real-field multi-class weed detection dataset and YOLO benchmark for tomato production systems
AU  - Alperen Mehmet Sevinç
AU  - Çağrı Vakkas Yıldırım
AU  - Ahmet Kerim Ağırman
PY  - 2026
JO  - Frontiers in Plant Science
DO  - 10.3389/fpls.2026.1861020
UR  - https://doi.org/10.3389/fpls.2026.1861020
ER  -

APA

Sevinç, A. M., Yıldırım, Ç. V., & Ağırman, A. K. (2026). TomatoweedDet: a real-field multi-class weed detection dataset and YOLO benchmark for tomato production systems. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2026.1861020

Source records

crossref · retrieved 2026-11-04T20:12:19.367Z