Mendes, JoãoLima, JoséCosta, LinoRodrigues, NunoLeitão, PauloPereira, Ana I.2024-10-072024-10-072024Mendes, 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 978-3-031-53035-7.978-3-031-53035-7978-3-031-53036-4http://hdl.handle.net/10198/30326Identifying 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.engOlive ripening stagesYou Only Look Once (YOLO)IntelligentSystemComputer VisionPrecision Agricultureconference object10.1007/978-3-031-53036-4_5