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

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 Vicens Maquinaria Agrícola S.A. for their support during data acquisition.