A Real-Time Olive Fruit Detection for Harvesting Robot Based on Yolo Algorithms

ALJAAFREH, Ahmad, ELZAGZOUG, ,Ezzaldeen Y., ABUKHAIT, Jafar, SOLIMAN, Abdel-Hamid, ALJA‘AFREH, Saqer S., SIVANATHAN, Aparajithan and HUGHES, James (2023) A Real-Time Olive Fruit Detection for Harvesting Robot Based on Yolo Algorithms. Acta Technologica Agriculturae, 26 (3). pp. 121-132. ISSN 1338-5267

Preview

Text 10.2478_ata-2023-0017.pdf - Publisher's typeset copy Available under License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). Download (1MB) | Preview

Abstract or description

Deep neural network models have become powerful tools of machine learning and artificial intelligence. They can approximate functions and dynamics by learning from examples. This paper reviews the state-of-art of deep learning-based object detection frameworks that are used for fruit detection in general and for olive fruit in particular. A dataset of olive fruit on the tree is built to train and evaluate deep models. The ultimate goal of this work is the capability of on-edge real-time olive fruit detection on the tree from digital videos. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed You Only Look Once version five (YOLOv5). This paper builds a dataset of 1.2 K source images of olive fruit on the tree and evaluates the latest object detection algorithms focusing on variants of YOLOv5 and YOLOR. The results of the YOLOv5 models show that the YOLOv5 new network models are able to extract rich olive features from images and detect the olive fruit with a high precision of higher than 0.75 mAP_0.5. YOLOv5s performs better for real-time olive fruit detection on the tree over other YOLOv5 variants and YOLOR.

Item Type:	Article
Uncontrolled Keywords:	object detection; olive harvesting; YOLO; deep learning; computer vision
Faculty:	School of Digital, Technologies and Arts > Engineering
Depositing User:	Abdel-Hamid SOLIMAN
Date Deposited:	10 Oct 2023 16:04
Last Modified:	11 Oct 2023 04:30
URI:	https://eprints.staffs.ac.uk/id/eprint/7932

Actions (login required)

View Item