Abstract
Morphological information of plants is an essential resource for different agricultural machine vision applications, which can be obtained from 3D models through reconstruction algorithms. Three dimensional modeling of a plant is an XYZ spatial representation used to determine its physical parameters from, for example, a point cloud. Currently two low-cost methods have gained popularity in terms of 3D object reconstructions in 360\(^{\circ }\) employing rotating platforms, based on 2D LiDAR and Kinect. In this paper, these two techniques are compared by getting a 3D model of a Dracaena braunii specie and evaluating their performance. The results are shown in terms of their accuracy and time consumption using a Kinect V1 and a LiDAR URG-04LX-UG01, a well-performance low-cost scanning rangefinder from Hokuyo manufacturer. In terms of error calculation, the Kinect-based system probed to be more accurate than the LiDAR-based, with an error less than 20% in all plant measurements. In addition, the point cloud density reached with Kinect was approximately four times higher than with LiDAR. But, acquisition and processing time was about twice than LiDAR system.
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Acknowledgment
This work was funded by the OMICAS program: Optimización Multiescala In-silico de Cultivos Agrícolas Sostenibles (Infraestructura y validación en Arroz y Caña de Azúcar), anchored at the Pontificia Universidad Javeriana in Cali and funded within the Colombian Scientific Ecosystem by The World Bank, the Colombian Ministry of Science, Technology and Innovation, the Colombian Ministry of Education, the Colombian Ministry of Industry and Tourism, and ICETEX, under grant FP44842-217-2018 and OMICAS Award ID: 792-61187.
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Murcia, H., Sanabria, D., Méndez, D., Forero, M.G. (2021). A Comparative Study of 3D Plant Modeling Systems Based on Low-Cost 2D LiDAR and Kinect. In: Roman-Rangel, E., Kuri-Morales, Á.F., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2021. Lecture Notes in Computer Science(), vol 12725. Springer, Cham. https://doi.org/10.1007/978-3-030-77004-4_26
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