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Sensors 2018, 18(3), 763; https://doi.org/10.3390/s18030763

High-Precision Phenotyping of Grape Bunch Architecture Using Fast 3D Sensor and Automation

1
Julius Kühn-Institut, Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, 76833 Siebeldingen, Germany
2
Institute of Computer Science 4, University of Bonn, Endenicher Allee 19 A, 53115 Bonn, Germany
3
Institute of Crop Science and Resource Conservation (INRES)–Plant Breeding, University of Bonn, 53113 Bonn, Germany
*
Author to whom correspondence should be addressed.
Received: 10 January 2018 / Revised: 24 February 2018 / Accepted: 28 February 2018 / Published: 2 March 2018
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Germany)
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Abstract

Wine growers prefer cultivars with looser bunch architecture because of the decreased risk for bunch rot. As a consequence, grapevine breeders have to select seedlings and new cultivars with regard to appropriate bunch traits. Bunch architecture is a mosaic of different single traits which makes phenotyping labor-intensive and time-consuming. In the present study, a fast and high-precision phenotyping pipeline was developed. The optical sensor Artec Spider 3D scanner (Artec 3D, L-1466, Luxembourg) was used to generate dense 3D point clouds of grapevine bunches under lab conditions and an automated analysis software called 3D-Bunch-Tool was developed to extract different single 3D bunch traits, i.e., the number of berries, berry diameter, single berry volume, total volume of berries, convex hull volume of grapes, bunch width and bunch length. The method was validated on whole bunches of different grapevine cultivars and phenotypic variable breeding material. Reliable phenotypic data were obtained which show high significant correlations (up to r2 = 0.95 for berry number) compared to ground truth data. Moreover, it was shown that the Artec Spider can be used directly in the field where achieved data show comparable precision with regard to the lab application. This non-invasive and non-contact field application facilitates the first high-precision phenotyping pipeline based on 3D bunch traits in large plant sets. View Full-Text
Keywords: grapevine phenotyping; bunch compactness; sphere detection; Random-Sample-Consensus (RANSAC); Organization of Vine and Wine (OIV) descriptor 204; Botrytis; Vitis vinifera grapevine phenotyping; bunch compactness; sphere detection; Random-Sample-Consensus (RANSAC); Organization of Vine and Wine (OIV) descriptor 204; Botrytis; Vitis vinifera
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Rist, F.; Herzog, K.; Mack, J.; Richter, R.; Steinhage, V.; Töpfer, R. High-Precision Phenotyping of Grape Bunch Architecture Using Fast 3D Sensor and Automation. Sensors 2018, 18, 763.

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