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Fruity: A Multi-modal Dataset for Fruit Recognition and 6D-Pose Estimation in Precision Agriculture

Abdulsalam, M., Chekakta, Z. ORCID: 0000-0002-4664-6283, Aouf, N. ORCID: 0000-0001-9291-4077 & Hogan, M. (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


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:

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Neural networks, Image processing, Autonomous systems
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TJ Mechanical engineering and machinery
T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Departments: School of Science & Technology
School of Science & Technology > Engineering
SWORD Depositor:
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