A YOLO-Based Approach for Detection of Olive Knot Disease through UAV and Computer Vision Technologies

Morais, Maurício Herche Fófano de; Mendes, João; Santos, Murillo Ferreira dos; Fernandes, Fernanda Mara; Lima, José; Pereira, Ana I.

http://hdl.handle.net/10198/36087

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Autores

Morais, Maurício Herche Fófano de

Mendes, João

Santos, Murillo Ferreira dos

Fernandes, Fernanda Mara

Lima, José

Pereira, Ana I.

Resumo(s)

This work presents an approach for detecting olive knot disease in olive trees, utilizing Computer Vision (CV), Unmanned Aerial Vehicle (UAV) based imagery, and Machine Learning (ML) within the context of Precision Agriculture (PA). The study focuses on applying the You Only Look Once (YOLO) deep learning architecture to develop a model capable of identifying trees affected by the disease with accuracy and speed. By integrating UAV technology with object detection algorithms, this approach enables real-time monitoring of olive plantations, supporting early detection and targeted interventions. This study emphasizes the potential of combining drone imaging and ML to drive sustainable and practical solutions in PA. Results show that this method can potentially improve crop management by reducing human labor and contributing to the enhancement of disease control strategies.

Palavras-chave

Unmanned aerial vehicle Disease detection Olive knot YOLO Computer vision Deep learning

URI

http://hdl.handle.net/10198/36087

Citação

Morais, Maurício Herche Fófano de; Mendes, João; Santos, Murillo Ferreira dos; Fernandes, Fernanda Mara; Lima, José; Pereira, Ana I. (2025). A YOLO-Based Approach for Detection of Olive Knot Disease through UAV and Computer Vision Technologies. In 26th International Carpathian Control Conference, ICCC 2025. Stary Smokovec:IEEE. p. 1-6. ISBN 979-833150127-3