Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-29T03:25:58.206Z Has data issue: false hasContentIssue false
  • English
  • Français

Application of the Kinect sensor for three dimensional characterization of vine canopy

Published online by Cambridge University Press:  01 June 2017

F. Marinello ,
A. Pezzuolo ,
F. Meggio ,
J. A. Martínez-Casasnovas ,
T. Yezekyan  and
L. Sartori
F. Marinello*
Affiliation:
TeSAF, Department of Land, Environment, Agriculture and Forestry, University of Padova, Viale dell’Università 16, Legnaro, Italy
A. Pezzuolo
Affiliation:
TeSAF, Department of Land, Environment, Agriculture and Forestry, University of Padova, Viale dell’Università 16, Legnaro, Italy
F. Meggio
Affiliation:
DAFNAE, Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell’Università 16, Legnaro, Italy
J. A. Martínez-Casasnovas
Affiliation:
Research Group in AgroICT and Precision Agriculture – Agrotecnio Center, University of Lleida, Spain
T. Yezekyan
Affiliation:
Institute of Technology, Estonian University of Life Sciences, Fr. R. Kreutzwaldi 1, Tartu, Estonia
L. Sartori
Affiliation:
TeSAF, Department of Land, Environment, Agriculture and Forestry, University of Padova, Viale dell’Università 16, Legnaro, Italy
*
†E-mail: francesco.marinello@unipd.it
Get access

Abstract

Monitoring grapevine canopy size and evolution during time is of great interest for the management of the vineyard. An interesting and cost effective solution for 3D characterization is provided by the Kinect sensor. To assess its practical applicability, field experiments were carried out on two different grapevines varieties (Glera and Merlot) for a three months period. The results from 3D digital imaging were compared with those achieved by direct hand-made measurements. Estimated volume was then effectively correlated to the number of leaves and to the leaf area index. The experiments demonstrated how a low cost 3D sensor can be applied for fast and repeatable reconstruction of vine vegetation, opening up for new potential improvements in variable rate application or pruning

Keywords

3D dynamic measurementRGB-DLAIvine canopy
Type
Precision Horticulture and Viticulture
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. 525 - 529
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Arnó, J, Escolà, A, Vallès, JM, Llorens, J, Sanz, R, Masip, J, Palacín, J and Rosell-Polo, JR 2013. Leaf area index estimation in vineyards using a ground-based LiDAR scanner. Precision Agriculture 14 (3), 290–306.Google Scholar
Del-Moral-Martínez, I, Rosell-Polo, JR, Company, J, Sanz, R, Escolà, A, Masip, J, Martínez-Casasnovas, JA and Arnó, J 2016. Mapping vineyard leaf area using mobile terrestrial laser scanners: Should rows be scanned on-the-go or discontinuously sampled? Sensors 16 (1), 119.Google Scholar
Liu, W, Kantor, G, De la Torre, F and Zheng, N 2012. Image-based Tree Pruning. Proceedings of the 2012 IEEE International Conference on Robotics and Biomimetics, pp. 2072–2076.Google Scholar
Marinello, F, Pezzuolo, A, Gasparini, F and Sartori, L 2013. Three-dimensional sensor for dynamic characterization of soil microrelief. In: Precision Agriculture ’13: Proceedings of the 8th European Conference on Precision Agriculture, edited by JV Stafford, BIOS Scientific Publishers Ltd, Oxford, UK, pp. 71–78.Google Scholar
Marinello, F, Schiavuta, P, Cavalli, R, Pezzuolo, A, Carmignato, S and Savio, E 2014. Critical factors in cantilever near-field scanning optical microscopy. IEEE Sensors Journal 14 (9), 3236–3244.Google Scholar
Marinello, F, Pezzuolo, A, Gasparini, F, Arvidsson, J and Sartori, L 2015a. Application of the Kinect sensor for dynamic soil surface characterization. Precision Agriculture 5, 601–612.Google Scholar
Marinello, F, Pezzuolo, A, Simonetti, A, Grigolato, S, Boscaro, D, Mologni, O, Gasparini, F, Cavalli, R and Sartori, L 2015b. Tractor cabin ergonomics analyses by means of Kinect motion capture technology. Contemporary Engineering Sciences 8 (25-28), 1339–1349.Google Scholar
Mathews, AJ and Jensen, JLR 2013. Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud. Remote Sensing 5, 2164–2183.Google Scholar
Monsó, A, Arnó, J and Martínez-Casasnovas, JA 2013. A simplified index to assess the opportunity for selective wine grape harvesting from vigour maps. In: Precision Agriculture ’13: Proceedings of the 8th European Conference on Precision Agriculture, edited by JV Stafford, BIOS Scientific Publishers Ltd, Oxford, UK, pp. 625–632.Google Scholar
Pajares, G, Peruzzi, A and Gonzalez-de-Santos, P 2013. Sensors in Agriculture and Forestry. Sensors 13, 12132–12139.CrossRefGoogle ScholarPubMed
Pezzuolo, A, Basso, B, Marinello, F and Sartori, L 2014. Using SALUS model for medium and long term simulations of energy efficiency in different tillage systems. Applied Mathematical Sciences 8, 129–132.Google Scholar
Rinaldi, M, Llorens, J and Gil, E 2013. Electronic characterization of the phenological stages of grapevine using a LIDAR sensor. In: Precision Agriculture ’13: Proceedings of the 8th European Conference on Precision Agriculture, edited by JV Stafford, BIOS Scientific Publishers Ltd, Oxford, UK, pp. 603–609.Google Scholar
Su, X and Zhang, Q 2010. Dynamic 3-D shape measurement method: A review. Optics and Lasers in Engineering 48 (2), 191–204.Google Scholar