@article {aGene-Molaa,
	title = {KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data},
	journal = {Data in Brief},
	year = {2019},
	month = {07/2019},
	abstract = {<p>This article contains data related to the research article entitle {\textquotedblleft}Multi-modal Deep Learning for Fruit Detection Using RGB-D Cameras and their Radiometric Capabilities{\textquotedblright} [1]. The development of reliable fruit detection and localization systems is essential for future sustainable agronomic management of high-value crops. RGB-D sensors have shown potential for fruit detection and localization since they provide 3D information with color data. However, the lack of substantial datasets is a barrier for exploiting the use of these sensors. This article presents the KFuji RGB-DS database which is composed by 967 multi-modal images of Fuji apples on trees captured using Microsoft Kinect v2 (Microsoft, Redmond, WA, USA). Each image contains information from 3 different modalities: color (RGB), depth (D) and range corrected IR intensity (S). Ground truth fruit locations were manually annotated, labeling a total of 12,839 apples in all the dataset. The current dataset is publicly available at http://www.grap.udl.cat/publicacions/datasets.html.</p>},
	keywords = {Depth cameras; RGB-D, Fruit detection, Fruit reflectance, Fuji apple, Multi-modal dataset},
	doi = {10.1016/j.dib.2019.104289},
	author = {Gen{\'e}-Mola, Jordi and Ver{\'o}nica Vilaplana and Rosell-Polo, Joan R. and Morros, J.R. and Ruiz-Hidalgo, J. and Gregorio, Eduard}
}
