<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

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

College of Engineering, Computer and Communication Engineering Department

The University of Sheffield, AMRC North West

Staffordshire University, School of Digital

Technologies and Arts, Department of Engineering

Acta Technologica Agriculturae

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"A Real-Time Olive Fruit Detection for Harvesting Robot Based on YOLO Algorithms"}

Deep neural network models have become powerful tools of machine learning and artificial intelligence. They can approximate functions and dynamics by learning...

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

Pubblicato online: 18 ago 2023

Pagine: 121 - 132

DOI: https://doi.org/10.2478/ata-2023-0017

Parole chiave
object detection, olive harvesting, YOLO, deep learning, computer vision

© 2023 Ahmad Aljaafreh et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Pubblicato online: 18 ago 2023

Pagine: 121 - 132

DOI: https://doi.org/10.2478/ata-2023-0017

Parole chiaveobject detection, olive harvesting, YOLO, deep learning, computer vision

© 2023 Ahmad Aljaafreh et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
object detection, olive harvesting, YOLO, deep learning, computer vision