Fruit detection in an apple orchard using a mobile terrestrial laser scanner

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

doi:https://doi.org/10.1016/j.biosystemseng.2019.08.017

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Issue date

2019-09-21

Author

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

Suggested citation

Gené Mola, Jordi; Gregorio López, Eduard; Guevara, Javier; Auat Cheein, Fernando A.; Sanz Cortiella, Ricardo; Escolà i Agustí, Alexandre; ... Rosell Polo, Joan Ramon. (2019) . Fruit detection in an apple orchard using a mobile terrestrial laser scanner. Biosystems Engineering, 2019, vol. 187, p. 171-184. https://doi.org/10.1016/j.biosystemseng.2019.08.017.

Abstract

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.

URI

http://hdl.handle.net/10459.1/66745

DOI

https://doi.org/10.1016/j.biosystemseng.2019.08.017

Is part of

Biosystems Engineering, 2019, vol. 187, p. 171-184

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Except where otherwise noted, this item's license is described as cc-by-nc-nd (c) Academic Press, 2019