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Open AccessArticle

Phenoliner: A New Field Phenotyping Platform for Grapevine Research

Julius Kühn-Institut, Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, 76833 Siebeldingen, Germany
Fraunhofer Institute for Factory Operation and Automation (IFF), Biosystems Engineering, Sandtorstr. 22, 39108 Magdeburg, Germany
Institute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany
Institute of Geodesy and Geoinformation, Department of Photogrammetry, University of Bonn, Nussallee 15, 53115 Bonn, Germany
ERO-Gerätebau GmbH, Simmerner Str. 20,55469 Niederkumbd, Germany
Author to whom correspondence should be addressed.
Sensors 2017, 17(7), 1625;
Received: 3 May 2017 / Revised: 23 June 2017 / Accepted: 11 July 2017 / Published: 14 July 2017
(This article belongs to the Special Issue Sensors in Agriculture)
In grapevine research the acquisition of phenotypic data is largely restricted to the field due to its perennial nature and size. The methodologies used to assess morphological traits and phenology are mainly limited to visual scoring. Some measurements for biotic and abiotic stress, as well as for quality assessments, are done by invasive measures. The new evolving sensor technologies provide the opportunity to perform non-destructive evaluations of phenotypic traits using different field phenotyping platforms. One of the biggest technical challenges for field phenotyping of grapevines are the varying light conditions and the background. In the present study the Phenoliner is presented, which represents a novel type of a robust field phenotyping platform. The vehicle is based on a grape harvester following the concept of a moveable tunnel. The tunnel it is equipped with different sensor systems (RGB and NIR camera system, hyperspectral camera, RTK-GPS, orientation sensor) and an artificial broadband light source. It is independent from external light conditions and in combination with artificial background, the Phenoliner enables standardised acquisition of high-quality, geo-referenced sensor data. View Full-Text
Keywords: big data; geo-information; plant phenotyping; grapevine breeding; Vitis vinifera big data; geo-information; plant phenotyping; grapevine breeding; Vitis vinifera
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Kicherer, A.; Herzog, K.; Bendel, N.; Klück, H.-C.; Backhaus, A.; Wieland, M.; Rose, J.C.; Klingbeil, L.; Läbe, T.; Hohl, C.; Petry, W.; Kuhlmann, H.; Seiffert, U.; Töpfer, R. Phenoliner: A New Field Phenotyping Platform for Grapevine Research. Sensors 2017, 17, 1625.

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