The phenotype of a vine is the sum of all morphological and physiological traits such as yield, grape health or field resistance against pathogens, and quality. The term phenotyping indicates the quantitative analysis of all traits. It is a key technology in plant breeding ever since. The more accurate the plant traits are described, the more efficient is the breeder’s selection. Nowadays, breeding material and genetic resources are evaluated using different descriptors for instance for the development of genetic markers (MAS). Up to now, the evaluation of phenotypic traits is mainly done by manual, visual classification. This method is very time- and labor-intensive and can only be done on selected breeding material. In recent years, it has been shown that these descriptions are only applicable to a limited extent for marker development. The transfer is insufficient especially in the case of very complex traits like yield or grape health as they are highly influenced by environmental factors. For this kind of parameters, the application of sensor-based, objective methods plays a key role to evaluate the importance of single parameters and to define new breeding selection criteria in the end. The digitalization and the use of different sensors plays an increasingly important role in the agriculture sector and thus in viticulture as well. Towards this goal we aim at the linkage of sensor, machine and software for the data based decision support of wine growers for a future oriented viticulture.

The vision of the workgroup phenotyping, digitalization and precision viticulture is the evaluation and establishment of meaningful sensors for the acquisition of morphological and qualitative parameters. This can be achieved only by, building on strong interdisciplinary networks for the development of automated, robust processes for data acquisition in the field, greenhouse and lab, including data management and data analysis. In this way it is possible to (1) acquire precise and objective trait data, (2) increase the sample size, (3) decrease the error variation and (4) evaluate data in retrospective. It is expected to increase the efficiency of grapevine breeding and accelerate this process by several years.

Based on the completed projects CROP.SENSe.net (development of methods) and PHENOvines (application of methods and setup of a self-propelled phenotyping platform PHENObot) the working group has been in place since 2010. Since 2017, the second field sensor platform, the Phenoliner, is available. The groundwork for high-throughput phenotyping under standardized greenhouse and lab conditions has been created through the establishment of phenotyping pipelines for 3D grape bunch architecture, berry surface resistance and hyperspectral imaging.
Since 2019 one of the BMEL funded experimental fields (www.digivine.org) is located at Geilweilerhof. It´s focus is on digitalization within the network of grapevine: from planting to grape delivery.

The focus of the phenotyping research is on yield parameters, plant health, berry skin characteristics, Botrytis, downy mildew, abiotic stress and phenology of grapevine.