Comparative Study Between Object Detection Models, for Olive Fruit Fly Identification

Margarida Victoriano, Lino Oliveira, Hélder Oliveira, Hélder Oliveira

2024

Abstract

Climate change is causing the emergence of new pest species and diseases, threatening economies, public health, and food security. In Europe, olive groves are crucial for producing olive oil and table olives; however, the presence of the olive fruit fly ( Bactrocera Oleae) poses a significant threat, causing crop losses and financial hardship. Early disease and pest detection methods are crucial for addressing this issue. This work presents a pioneering comparative performance study between two state-of-the-art object detection models, YOLOv5 and YOLOv8, for the detection of the olive fruit fly from trap images, marking the first-ever application of these models in this context. The dataset was obtained by merging two existing datasets: the DIRT dataset, collected in Greece, and the CIMO-IPB dataset, collected in Portugal. To increase its diversity and size, the dataset was augmented, and then both models were fine-tuned. A set of metrics were calculated, to assess both models performance. Early detection techniques like these can be incorporated in electronic traps, to effectively safeguard crops from the adverse impacts caused by climate change, ultimately ensuring food security and sustainable agriculture.

Paper Citation

in Harvard Style

Victoriano M., Oliveira L. and Oliveira H. (2024). Comparative Study Between Object Detection Models, for Olive Fruit Fly Identification. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 458-465. DOI: 10.5220/0012361800003660

in Bibtex Style

@conference{visapp24,
author={Margarida Victoriano and Lino Oliveira and Hélder Oliveira},
title={Comparative Study Between Object Detection Models, for Olive Fruit Fly Identification},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={458-465},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012361800003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Comparative Study Between Object Detection Models, for Olive Fruit Fly Identification
SN - 978-989-758-679-8
AU - Victoriano M.
AU - Oliveira L.
AU - Oliveira H.
PY - 2024
SP - 458
EP - 465
DO - 10.5220/0012361800003660
PB - SciTePress