Automated preprocessing of olive leaf images for cultivar classification using YOLO11

Mendes, João; Lima, José; Rodrigues, Nuno; Pereira, Ana I.

http://hdl.handle.net/10198/35171

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Abstract(s)

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.

Keywords

Olive Cultivation Leaf Detection Image Segmentation YOLO11 Smart Agriculture

URI

http://hdl.handle.net/10198/35171

Citation

Mendes, João; Lima, José; Rodrigues, Nuno; Pereira, Ana I. (2026). Automated Preprocessing of Olive Leaf Images for Cultivar Classification Using YOLO11. In 5th International Conference OL2A. Cham: Springer Nature. p. 286–297. ISBN 9783032001368