Autor(es): Mendes, João ; Lima, José ; Rodrigues, Nuno ; Pereira, Ana I.
Data: 2026
Identificador Persistente: http://hdl.handle.net/10198/35171
Origem: Biblioteca Digital do IPB
Assunto(s): Olive Cultivation; Leaf Detection; Image Segmentation; YOLO11; Smart Agriculture
Olive cultivation is a pillar of Mediterranean agriculture, deeply rooted in both tradition and economic importance. This paper presents a novel two-phase methodology for the automated preprocessing of olive leaf images to facilitate accurate cultivar classification. Leveraging the state-of-the-art YOLO11 framework, two models (YOLO11n and YOLO11s) were employed for detection and segmentation tasks. A comprehensive dataset, combining in-situ captured images with publicly available data, was meticulously annotated using both manual and semi-automatic processes. The detection model identifies individual olive leaves, while the segmentation model isolates the leaves by replacing the background with a uniform white, thereby simulating laboratory conditions. Experimental results demonstrate that YOLO11n outperforms YOLO11s in terms of mean Average Precision and F1-score, confirming the feasibility of deploying the system on mobile devices for real-time, in-field classification.
