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Sensors 2015, 15(3), 4823-4836; doi:10.3390/s150304823

An Automated Field Phenotyping Pipeline for Application in Grapevine Research

1
Julius Kühn-Institut, Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, 76833 Siebeldingen, Germany
2
Julius Kühn-Institut, Federal Research Centre for Cultivated Plants, Institute for Plant Protection in Field Crops and Grassland, Messeweg 11-12, 38104 Braunschweig, Germany
3
Leibniz Institute for Agricultural Engineering Potsdam-Bornim, Department Horticultural Engineering, Max-Eyth-Allee 100, 14469 Potsdam, Germany
4
University of Bonn, Department of Geodesy, Institute for Geodesy and Geoinformation (IGG), Nussallee 17, 53115 Bonn, Germany
5
Geisenheim University, Department of Viticultural Engineering, Brentanostraße 9, 65366 Geisenheim, Germany
6
Julius Kühn-Institut, Federal Research Centre for Cultivated Plants, Department of Data Processing, Erwin-Baur-Str. 27, 06484 Quedlinburg, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 6 January 2015 / Revised: 12 February 2015 / Accepted: 15 February 2015 / Published: 26 February 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [4909 KB, uploaded 26 February 2015]   |  

Abstract

Due to its perennial nature and size, the acquisition of phenotypic data in grapevine research is almost exclusively restricted to the field and done by visual estimation. This kind of evaluation procedure is limited by time, cost and the subjectivity of records. As a consequence, objectivity, automation and more precision of phenotypic data evaluation are needed to increase the number of samples, manage grapevine repositories, enable genetic research of new phenotypic traits and, therefore, increase the efficiency in plant research. In the present study, an automated field phenotyping pipeline was setup and applied in a plot of genetic resources. The application of the PHENObot allows image acquisition from at least 250 individual grapevines per hour directly in the field without user interaction. Data management is handled by a database (IMAGEdata). The automatic image analysis tool BIVcolor (Berries in Vineyards-color) permitted the collection of precise phenotypic data of two important fruit traits, berry size and color, within a large set of plants. The application of the PHENObot represents an automated tool for high-throughput sampling of image data in the field. The automated analysis of these images facilitates the generation of objective and precise phenotypic data on a larger scale. View Full-Text
Keywords: robot; geoinformation; high-throughput analysis; image acquisition; plant phenotyping; grapevine breeding; Vitis vinifera robot; geoinformation; high-throughput analysis; image acquisition; plant phenotyping; grapevine breeding; 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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MDPI and ACS Style

Kicherer, A.; Herzog, K.; Pflanz, M.; Wieland, M.; Rüger, P.; Kecke, S.; Kuhlmann, H.; Töpfer, R. An Automated Field Phenotyping Pipeline for Application in Grapevine Research. Sensors 2015, 15, 4823-4836.

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