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Advisor(s)
Abstract(s)
Identifying the maturation stage is an added value for olive oil producers
and consumers, whether this is done to predict the best harvest time, give
us more information about the olive oil, or even adapt techniques and extraction
parameters in the olive oil mill. In this way, the proposed work presents a
new method to identify and count the number of olives that enter the mill as
well as their stage of maturation. It is based on artificial intelligence (AI) and
deep learning algorithms, using the two most recent versions of YOLO, YOLOv7
and YOLOv8. The obtained results demonstrate the possibility of using this type
of application in a real environment, managing to obtain a mAP of approximately
79% with YOLOv8 in the five maturation stages, with a processing rate
of approximately 16 FPS increasing this with YOLOv7 to 36.5 FPS reaching a
66% mAP.
Description
Keywords
Olive ripening stages You Only Look Once (YOLO) Intelligent System Computer Vision Precision Agriculture
Citation
Mendes, João; Lima, José; Costa, Lino A.; Rodrigues, Nuno; Leitão, Paulo; Pereira, Ana I. (2024). An Artificial Intelligence-Based Method to Identify the Stage of Maturation in Olive Oil Mills. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 2, p. 63–77. ISBN 978-3-031-53035-7.
Publisher
Springer Nature