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Títol:
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Fruit detection in an apple orchard using a mobile terrestrial laser scanner
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Autor/a:
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Gené Mola, Jordi; Gregorio López, Eduard; Guevara, Javier; Auat Cheein, Fernando A.; Sanz Cortiella, Ricardo; Escolà i Agustí, Alexandre; Llorens Calveras, Jordi; Morros Rubió, Josep Ramon; Ruiz Hidalgo, Javier; Vilaplana Besler, Verónica; Rosell Polo, Joan Ramon
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Notes:
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The development of reliable fruit detection and localization systems provides an opportunity to improve the crop value and management by limiting fruit spoilage and optimised harvesting practices. Most proposed systems for fruit detection are based on RGB cameras and thus are affected by intrinsic constraints, such as variable lighting conditions. This work presents a new technique that uses a mobile terrestrial laser scanner (MTLS) to detect and localise Fuji apples. An experimental test focused on Fuji apple trees (Malus domestica Borkh. cv. Fuji) was carried out. A 3D point cloud of the scene was generated using an MTLS composed of a Velodyne VLP-16 LiDAR sensor synchronised with an RTK-GNSS satellite navigation receiver. A reflectance analysis of tree elements was performed, obtaining mean apparent reflectance values of 28.9%, 29.1%, and 44.3% for leaves, branches and trunks, and apples, respectively. These results suggest that the apparent reflectance parameter (at 905 nm wavelength) can be useful to detect apples. For that purpose, a fourstep fruit detection algorithm was developed. By applying this algorithm, a localization success of 87.5%, an identification success of 82.4%, and an F1-score of 0.858 were obtained in relation to the total amount of fruits. These detection rates are similar to those obtained by RGB-based systems, but with the additional advantages of providing direct 3D fruit location information, which is not affected by sunlight variations. From the experimental results, it can be concluded that LiDAR-based technology and, particularly, its reflectance information, has potential for remote apple detection and 3D location.
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 (projects AGL2013-48297-C2-2-Rand MALEGRA, TEC2016-75976-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 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 for grant 1171431 and CONICYT FB0008. Nufri (especially Santiago Salamero and Oriol Morreres) and Vicens Maquinària Agrícola S.A. are also thanked for their support during the data acquisition. |
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Matèries:
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-lidar
-Terrestrial LIDAR
-fruit detection
-agricultural robotics |
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Drets:
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cc-by-nc-nd (c) Academic Press, 2019
http://creativecommons.org/licenses/by-nc-nd/3.0/es
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Tipus de document:
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Article
Article - Versió acceptada |
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Publicat per:
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Academic Press. Published by Elsevier
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Compartir:
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