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.
Highlights
With the fast development of genotyping methods to support grapevine breeding based on SSR (Simple Sequence Repeats) [1,2] or SNP (Single Nucleotide Polymorphism) analyses, including generation DNA sequencing [3], genotyping efficiency has been greatly improved and costs have been reduced contemporaneously
The development and implementation of high-throughput phenotyping platforms is a key tool to improve the efficiency of grapevine (Vitis vinifera L. subsp. vinifera) or, more generally, plant breeding
Image analysis was conducted by using the MATLAB®-based tool, BIVcolor (Berries in Vineyards-color)
Summary
With the fast development of genotyping methods to support grapevine breeding based on SSR (Simple Sequence Repeats) [1,2] or SNP (Single Nucleotide Polymorphism) analyses, including generation DNA sequencing [3], genotyping efficiency has been greatly improved and costs have been reduced contemporaneously. The application of non-invasive, high-throughput sensor technologies is required to increase the efficiency of grapevine breeding by increasing the phenotyping efficiency (number of plants per time), improving the quality of phenotypic data recording and reducing the error variation. Such new methods progressively increase the amount of data that needs to be handled. An automated phenotyping platform in grapevine breeding is needed to screen for phenotypic traits on a single-plant-level in a reasonable time, unlike the application in precision farming, whereas the overall appearance of a plot or at least single areas of a plot are of greatest interest. We demonstrate the application of the pipeline in the grapevine repository at Geilweilerhof
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