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GENERAL INFORMATION
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1. Dataset Title: KEvOr dataset

2. Authors: Jordi Gené-Mola (a,*), Jordi Llorens (a), Joan R. Rosell-Polo (a), Eduard Gregorio (a), Jaume Arnó (a), Francesc Solanelles (b), José A. Martínez-Casasnovas (c) and Alexandre Escolà (a,*)
  (a) Rearch Group in AgroICT & Precision Agriculture, Department of Agricultural and Forest Engineering, University of Lleida (UdL) – Agrotecnio Centre, Lleida, Catalonia, Spain
  (b) Department of Agriculture, Livestock, Fisheries and Food, Generalitat de Catalunya, Lleida, Catalunya, Spain
  (c) Research Group in AgroICT & Precision Agriculture, Department of Environmental and Soil Sciences, University of Lleida (UdL) – Agrotecnio Centre, Lleida, Catalonia, Spain
  (*) Corresponding author: jordi.genemola@udl.cat (J.G-M.), alex.escola@udl.cat (A.E.)

3. Data description
The Kinect Evaluation in Orchard conditions (KEvOr) dataset is comprised of a set of RGB-D captures carried out with the Microsoft Kinect v2 to evaluate the performance of this sensor at different lighting conditions in agricultural orchards and from different distances to the measured target. 
Three Microsoft Kinect v2 sensors (K2S1, K2S2 and K2S3) were used to scan Fuji apple trees along the afternoon and evening, from the higher sun illuminance (55000 lux) until achieving dark conditions (0.1 lux), obtaining a total of 252 captures: 28 lighting conditions * 3 sensors * 3 repetitions. The data provided for each capture is: the acquired point cloud, illuminance level at the center of the measured scene, and wind speed.
The sensors where placed as follows:
    K2S1: Oriented to the north, measuring the row of trees side under direct sunlight. This sensor was placed at 2.5 m from the measured target.
    K2S2: Oriented to the north, measuring the row of trees side under direct sunlight. This sensor was placed at 1.5 m from the measured target.
    K2S3 Oriented to the south, measuring the row of trees side under indirect sunlight. This sensor was placed at 2.5 m from the measured target.
The dataset also includes two additional registered captures measuring the same scene from 1.5 m and from 2.5 m.

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

5. Aknowledgements
The authors would like to express their gratitude to Manel Ribes Dasi and Xavier Torrent for their contribution in the data processing.
This research was funded by the Spanish Ministry of Economy and Competitiveness and the Ministry of Science, Innovation and Universities through the program Plan Estatal I+D+i Orientada a los Retos de la Sociedad, grant numbers AGL2013-48297-C2-2-R and RTI2018-094222-B-I00, respectively.

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, Jordi Llorens, Joan R. Rosell-Polo, Eduard Gregorio, Jaume Arnó, Francesc Solanelles, José A. Martínez-Casasnovas and Alexandre Escolà. 2020. Assessing RGB-D sensor for 3D fruit crop canopy characterization in different operating and lighting conditions. (Submitted)