LFuji-air dataset: Annotated 3D LiDAR point clouds of Fuji apple trees for fruit detection scanned under different forced air flow conditions

Jordi Gené-Mola; Eduard Gregorio; Fernando Auat Cheein; Javier Guevara; Jordi Llorens; Ricardo Sanz-Cortiella; Alexandre Escolà; Joan R Rosell-Polo

doi:10.1016/j.dib.2020.105248

LFuji-air dataset: Annotated 3D LiDAR point clouds of Fuji apple trees for fruit detection scanned under different forced air flow conditions

Data Brief. 2020 Feb 7:29:105248. doi: 10.1016/j.dib.2020.105248. eCollection 2020 Apr.

Authors

Jordi Gené-Mola¹, Eduard Gregorio¹, Fernando Auat Cheein², Javier Guevara², Jordi Llorens¹, Ricardo Sanz-Cortiella¹, Alexandre Escolà¹, Joan R Rosell-Polo¹

Affiliations

¹ Research Group in AgroICT & Precision Agriculture, Department of Agricultural and Forest Engineering, Universitat de Lleida (UdL) - Agrotecnio Center, Lleida, Catalonia, Spain.
² Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile.

Abstract

This article presents the LFuji-air dataset, which contains LiDAR based point clouds of 11 Fuji apples trees and the corresponding apples location ground truth. A mobile terrestrial laser scanner (MTLS) comprised of a LiDAR sensor and a real-time kinematics global navigation satellite system was used to acquire the data. The MTLS was mounted on an air-assisted sprayer used to generate different air flow conditions. A total of 8 scans per tree were performed, including scans from different LiDAR sensor positions (multi-view approach) and under different air flow conditions. These variability of the scanning conditions allows to use the LFuji-air dataset not only for training and testing new fruit detection algorithms, but also to study the usefulness of the multi-view approach and the application of forced air flow to reduce the number of fruit occlusions. The data provided in this article is related to the research article entitled "Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow" [1].

Keywords: Forced air flow; Fruit detection; Fruit location; Fruit reflectance; LiDAR; MTLS; Yield prediction.