relation: https://openaccess.city.ac.uk/id/eprint/32876/
title: Fruity: A Multi-modal Dataset for Fruit Recognition and 6D-Pose Estimation in Precision Agriculture
creator: Abdulsalam, M.
creator: Chekakta, Z.
creator: Aouf, N.
creator: Hogan, M.
subject: QA75 Electronic computers. Computer science
subject: TJ Mechanical engineering and machinery
subject: TK Electrical engineering. Electronics Nuclear engineering
subject: TL Motor vehicles. Aeronautics. Astronautics
description: The application of robotic platforms for precision agriculture is gaining traction in modern research. However, the demand for a complete fruit dataset is still not satisfied. In this paper, we present fruity, a multi-modal fruit dataset with a variety of use cases such as 6D-pose estimation, fruit detection, fruit picking applications, etc. To the best of our knowledge, this dataset is the first-ever multi-modal fruit dataset tailored specifically for fruit 6D pose estimation in precision agriculture. The dataset is collected over a range of multiple sensors consisting of an RGB-D camera, thermal camera and an indoor tracking camera for ground truth poses. Fruity features RGB images, stereo depth images, thermal images, camera 6Dposes, fruit 6D-poses and relative 6D-poses between the cameras and fruits. The classes of the dataset are commonly harvested fruits which include: apples, oranges, bananas, avocados and lemons. It is also enriched with a clustered class to account for occlusion scenario. The dataset is recorded over multiple trajectories implemented with multiple platforms encompassing a robotic manipulator and an Unmanned Aerial Vehicle (UAV). The dataset alongside the documentation and utility tools is publicly available at: https://github.com/MahmoudYidi/Fruity.git.
publisher: IEEE
date: 2023-06-26
type: Conference or Workshop Item
type: PeerReviewed
format: text
language: en
identifier: https://openaccess.city.ac.uk/id/eprint/32876/1/Fruity%20A%20Multi-modal%20Dataset%20for%20Fruit%20Classification%20and%206D%20Pose%20Estimation%20in%20Precision%20Agriculture.pdf
identifier:       Abdulsalam, M., Chekakta, Z. <https://openaccess.city.ac.uk/view/creators_id/zakaria=2Echekakta.html> ORCID: 0000-0002-4664-6283 <http://orcid.org/0000-0002-4664-6283>, Aouf, N. <https://openaccess.city.ac.uk/view/creators_id/nabil=2Eaouf.html> ORCID: 0000-0001-9291-4077 <http://orcid.org/0000-0001-9291-4077>  & Hogan, M.view all authorsEPJS_limit_names_shown_load( 'creators_name_32876_et_al', 'creators_name_32876_rest' ); (2023).  Fruity: A Multi-modal Dataset for Fruit Recognition and 6D-Pose Estimation in Precision Agriculture.  In:     2023 31st Mediterranean Conference on Control and Automation (MED).  2023 31st Mediterranean Conference on Control and Automation (MED), 26-29 Jun 2023, Limassol, Cyprus.    doi: 10.1109/med59994.2023.10185851 <https://doi.org/10.1109/med59994.2023.10185851>  
relation: http://dx.doi.org/10.1109/med59994.2023.10185851
relation: 10.1109/med59994.2023.10185851
identifier: 10.1109/med59994.2023.10185851