LFuji-air dataset: annotated 3D LiDAR point clouds of Fuji apple trees for fruit detection scanned under different forced air flow conditions
2020
Gené Mola, Jordi | Gregorio López, Eduard | Auat Cheein, Fernando | Guevara, Javier | Llorens Calveras, Jordi | Sanz Cortiella, Ricardo | Escolà i Agustí, Alexandre | Rosell Polo, Joan Ramon
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].
Показать больше [+] Меньше [-]This work was partly funded by the Secretaria d’Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya (grant 2017 SGR 646), the Spanish Ministry of Economy and Competitiveness (project AGL2013-48297-C2-2-R) and the Spanish Ministry of Science, Innovation and Universities (project RTI2018-094222-B-I00). The Spanish Ministry of Education is thanked for Mr. J. Gené's pre-doctoral fellowships (FPU15/03355). The work of Jordi Llorens was supported by the Spanish Ministry of Economy, Industry and Competitiveness through a postdoctoral position named Juan de la Cierva Incorporación (JDCI-2016-29464_N18003). We would also like to thank CONICYT FONDECYT 1171431 and CONICYT FB0008.
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