Real‑Time Detection and Segmentation of Tomato Pests with YOLOv8

Yavuz Selim Şahin; Nimet Sema Gençer; Hasan Şahin

doi:10.15832/ankutbd.1681258

Real‑Time Detection and Segmentation of Tomato Pests with YOLOv8

Abstract

Tomato (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.

Keywords

References

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Details

Primary Language

English

Subjects

Agricultural Biotechnology Diagnostics

Journal Section

Research Article

Authors

Yavuz Selim Şahin ^*
0009-0001-8634-0625
Türkiye

Nimet Sema Gençer
0009-0007-2435-2384
Türkiye

Hasan Şahin
0000-0002-8915-000X
Türkiye

Publication Date

January 20, 2026

Submission Date

April 21, 2025

Acceptance Date

August 22, 2025

Published in Issue

Year 2026 Volume: 32 Number: 1

DOI

https://doi.org/10.15832/ankutbd.1681258

IZ

https://izlik.org/JA55FA83KN

Cite

RIS / Bibtex

APA

Şahin, Y. S., Gençer, N. S., & Şahin, H. (2026). Real‑Time Detection and Segmentation of Tomato Pests with YOLOv8. Journal of Agricultural Sciences, 32(1), 119-129. https://doi.org/10.15832/ankutbd.1681258

AMA

1.Şahin YS, Gençer NS, Şahin H. Real‑Time Detection and Segmentation of Tomato Pests with YOLOv8. J Agr Sci-Tarim Bili. 2026;32(1):119-129. doi:10.15832/ankutbd.1681258

Chicago

Şahin, Yavuz Selim, Nimet Sema Gençer, and Hasan Şahin. 2026. “Real‑Time Detection and Segmentation of Tomato Pests With YOLOv8”. Journal of Agricultural Sciences 32 (1): 119-29. https://doi.org/10.15832/ankutbd.1681258.

EndNote

Şahin YS, Gençer NS, Şahin H (January 1, 2026) Real‑Time Detection and Segmentation of Tomato Pests with YOLOv8. Journal of Agricultural Sciences 32 1 119–129.

IEEE

[1]Y. S. Şahin, N. S. Gençer, and H. Şahin, “Real‑Time Detection and Segmentation of Tomato Pests with YOLOv8”, J Agr Sci-Tarim Bili, vol. 32, no. 1, pp. 119–129, Jan. 2026, doi: 10.15832/ankutbd.1681258.

ISNAD

Şahin, Yavuz Selim - Gençer, Nimet Sema - Şahin, Hasan. “Real‑Time Detection and Segmentation of Tomato Pests With YOLOv8”. Journal of Agricultural Sciences 32/1 (January 1, 2026): 119-129. https://doi.org/10.15832/ankutbd.1681258.

JAMA

1.Şahin YS, Gençer NS, Şahin H. Real‑Time Detection and Segmentation of Tomato Pests with YOLOv8. J Agr Sci-Tarim Bili. 2026;32:119–129.

MLA

Şahin, Yavuz Selim, et al. “Real‑Time Detection and Segmentation of Tomato Pests With YOLOv8”. Journal of Agricultural Sciences, vol. 32, no. 1, Jan. 2026, pp. 119-2, doi:10.15832/ankutbd.1681258.

Vancouver

1.Yavuz Selim Şahin, Nimet Sema Gençer, Hasan Şahin. Real‑Time Detection and Segmentation of Tomato Pests with YOLOv8. J Agr Sci-Tarim Bili. 2026 Jan. 1;32(1):119-2. doi:10.15832/ankutbd.1681258