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

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Victoriano, M (Author) Other; Oliveira, L (Author) Other; Oliveira, HP (Author) FCUP

Conference proceedings International

Title: VISIGRAPP (2): VISAPP

Pages: 458-465

19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2024

Rome, 27 February 2024 through 29 February 2024

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-010-4J8

DOI: 10.5220/0012361800003660

Abstract (EN): 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. © 2024 by SCITEPRESS ¿ Science and Technology Publications, Lda.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7