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3D monitoring of woody crops using an unmanned ground vehicle

Published online by Cambridge University Press:  01 June 2017

A. Ribeiro ,
J. M. Bengochea-Guevara ,
J. Conesa-Muñoz ,
N. Nuñez ,
K. Cantuña  and
D. Andújar
A. Ribeiro*
Affiliation:
Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, Madrid 28500, Spain
J. M. Bengochea-Guevara
Affiliation:
Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, Madrid 28500, Spain
J. Conesa-Muñoz
Affiliation:
Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, Madrid 28500, Spain
N. Nuñez
Affiliation:
Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, Madrid 28500, Spain
K. Cantuña
Affiliation:
Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, Madrid 28500, Spain
D. Andújar
Affiliation:
Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, Madrid 28500, Spain
*
†E-mail: angela.ribeiro@csic.es
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Abstract

This paper presents an inspection system integrated into an on-ground autonomous platform with range of approximately 80 km. The vehicle is prepared to autonomously cover a field following a predefined route plan. Two types of cameras were integrated in the platform. An RGB-D sensor and a reflex camera were placed in a fixture and connected to a high-performance computer. The heterogeneous information acquired from the RGB-D was later integrated to automatically generate 3D maps of the crops by using custom software developed in the authors’ previous work. The inspection system performance was tested in actual vineyards by conducting several samplings in 2016. Results show that the proposed technology is viable and can provide complementary information to other inspection alternatives.

Keywords

3D woody crop reconstructioncanopy volume estimationRGB-D sensor
Type
Crop Sensors and Sensing
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 210 - 215
Copyright
© The Animal Consortium 2017 

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