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

Gené Mola, Jordi; Gregorio López, Eduard; Auat Cheein, Fernando A.; Guevara, Javier; Llorens Calveras, Jordi; Sanz Cortiella, Ricardo; Escolà i Agustí, Alexandre; Rosell Polo, Joan Ramon

doi:https://doi.org/10.1016/j.dib.2020.105248

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2020

Author

Gené Mola, Jordi

Gregorio López, Eduard

Auat Cheein, Fernando A.

Guevara, Javier

Llorens Calveras, Jordi

Sanz Cortiella, Ricardo

Escolà i Agustí, Alexandre

Rosell Polo, Joan Ramon

Suggested citation

Gené Mola, Jordi; Gregorio López, Eduard; Auat Cheein, Fernando A.; Guevara, Javier; Llorens Calveras, Jordi; Sanz Cortiella, Ricardo; ... Rosell Polo, Joan Ramon. (2020) . LFuji-air dataset: annotated 3D LiDAR point clouds of Fuji apple trees for fruit detection scanned under different forced air flow conditions. Data in Brief, 2020, vol. 29, 105248 (pp.7). https://doi.org/10.1016/j.dib.2020.105248.

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].

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Data in Brief, 2020, vol. 29, 105248 (pp.7)

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Except where otherwise noted, this item's license is described as cc-by (c) Gené, Jordi et al., 2020