Publicação
Automated preprocessing of olive leaf images for cultivar classification using YOLO11
| Resumo: | 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. |
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| Autores principais: | Mendes, João |
| Outros Autores: | Lima, José; Rodrigues, Nuno; Pereira, Ana I. |
| Assunto: | Olive Cultivation Leaf Detection Image Segmentation YOLO11 Smart Agriculture |
| Ano: | 2026 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | 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. |
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