Autor(es): Mendes, João ; Berger, Guido ; Lima, José ; Costa, Lino ; Pereira, Ana I.
Data: 2024
Identificador Persistente: http://hdl.handle.net/10198/31181
Origem: Biblioteca Digital do IPB
Assunto(s): UAVs; Computer vision; Precision agriculture; Pest control; Olive fly; Bactrocera oleae; YOLOv8; Segmentation
This study compares two computer vision methods to detect yellow sticky traps using unmanned autonomous vehicles in olive tree cultivation. The traps aim to combat and monitor the density of the Bactrocera oleae, an important pest that damages olive fruit, leading to substantial economic losses annually. The evaluation encompassed two distinct methods: firstly, an algorithm employing conventional segmentation techniques like thresholding and contour localization, and secondly, a contemporary artificial intelligence approach utilizing YOLOv8, a state-of-the-art technology. A specific dataset was created to train and adjust the two algorithms. At the end of the study, both were able to locate the trap precisely. The segmentation algorithm demonstrated superior performance at proximal distances (50 cm), outperforming the outcomes achieved by YOLOv8. In contrast, YOLOv8 exhibited sustained precision, irrespective of the distance under examination. These findings affirm the versatility of both algorithms, highlighting their adaptability to various contexts based on distinct application demands. Consideration of trade-offs between accuracy and processing speed is essential in determining the most appropriate algorithm for a given application.
