High-throughput phenotyping in grapevine breeding research: technologies and applications

Abstract

In times of highly effective and cost-efficient genotyping technologies routinely applied in plant research and breeding, the need for comparable high-throughput (HT) and high-resolution phenotyping tools has increased substantially. As a perennial plant, grapevines have very specific requirements for HT phenotyping. Depending on the trait, it can be applied in laboratories or greenhouses, but it is also very important under field conditions to rate the full phenotypic variability of traits like yield or plant vigour throughout the season. For more than a decade, researchers have strived to improve grapevine phenotyping by sensors and automation to dissolve the phenotyping bottleneck. The core goal of the present review is the illustration of promising and reliable opportunities for HT phenotyping in grapevine research and breeding. Therefore, different imaging sensor technologies and their data analysis, including artificial intelligence (AI), will be discussed, focusing on traits that are important for breeding new grapevine varieties. However, the expected outcome of any HT phenotyping approach is similar: transfer of a low-throughput method into an approach that acquires objective, precise, and reliable data for plant evaluation with high spatial and temporal resolution. Furthermore, the collection of large phenotypic data sets and their linkage with environmental or genomic data will provide new or extended insights into the response of grapevines to biotic and abiotic stresses and will significantly support the evaluation of traits, identification of new QTLs, or implementation of breeding strategies like genomic prediction. These advancements offer an improvement of precision and scalability within seedling selection and can additionally contribute to increased sustainability in viticulture.

Plant phenotyping relevance

ブドウ育種における高スループット表現型解析技術、画像センサー、データ解析、AIを中心に扱う方法論レビューであり、植物表現型解析手法が中核である。

Code and data availability

The supplied blocks contain only the acknowledgements and reference list of this grapevine phenotyping review. There are no data availability statements, no author-deposited phenotype datasets, images, code, or model checkpoints, and no public repository identifiers. All URLs are citations to prior published work, not

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