@inproceedings{city32876,
           month = {June},
            note = {{\copyright} 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.},
       booktitle = {2023 31st Mediterranean Conference on Control and Automation (MED)},
           title = {Fruity: A Multi-modal Dataset for Fruit Recognition and 6D-Pose Estimation in Precision Agriculture},
       publisher = {IEEE},
            year = {2023},
         journal = {2023 31st Mediterranean Conference on Control and Automation (MED)},
             doi = {10.1109/med59994.2023.10185851},
           pages = {144--149},
        keywords = {Neural networks, Image processing, Autonomous systems},
             url = {http://dx.doi.org/10.1109/med59994.2023.10185851},
            isbn = {9798350315431},
            issn = {2325-369X},
        abstract = {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.},
          author = {Abdulsalam, M. and Chekakta, Z. and Aouf, N. and Hogan, M.}
}