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GENERAL INFORMATION
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1. Dataset Title: KFuji RGB-DS dataset

2. Authors: Jordi Gené-Mola (a,*), Verónica Vilaplana (b), Joan R. Rosell-Polo (a), Josep-Ramon Morros (b), Javier Ruiz-Hidalgo (b), Eduard Gregorio (a)

  (a) Research Group in AgroICT & Precision Agriculture, Department of Agricultural and Forest Engineering, Universitat de Lleida (UdL) – Agrotecnio Center, Lleida, Catalonia, Spain.
  (b) Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Catalonia, Spain.
  Corresponding author
  (*) Corresponding author: jordi.genemola@udl.cat

3. Data description
The KFuji RGB-DS dataset 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. 

4. Methodology: The reader is referred to visit articles [1] and [2] for a description of methodology and further information about this dataset.

5. Aknowledgements
This work was partly funded by the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya, the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (ERDF) under Grants 2017 SGR 646, AGL2013-48297-C2-2-R and MALEGRA, TEC2016-75976-R. The Spanish Ministry of Education is thanked for Mr. J. Gené’s pre-doctoral fellowships (FPU15/03355). We would also like to thank Nufri and VicensMaquinaria Agrícola S.A. for their support during data acquisition.

6. License
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC-BY-NC-SA)

7. Others
This database is available only for research and educational purpose and not for any commercial use. If you use the database in any publications or reports, you must refer to the following papers:

1] Gené-Mola J, Vilaplana V, Rosell-Polo JR, Morros JR, Ruiz-Hidalgo J, Gregorio E. 2019. Multi-modal Deep Learning for Fruit Detection Using RGB-D Cameras and their Radiometric Capabilities. Computers and Electronics in Agriculture, 162, 689-698. DOI: 10.1016/j.compag.2019.05.016

[2] Gené-Mola J, Vilaplana V, Rosell-Polo JR, Morros JR, Ruiz-Hidalgo J, Gregorio E. 2019. KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data. Data in brief, 25 (2019), 104289. DOI: 10.1016/j.dib.2019.104289