High-throughput field phenotyping in vineyards: demand, approaches, objectives
Phenotyping methods and tools are very useful for genetic studies, breeding, and to develop new precision viticulture applications.
Standard methodologies encompass visual scoring of phenotypic traits (e.g., ampelographic traits, yield, and disease resistance) and phenology (e.g., bud burst, flowering, veraison) as well as the use of physiological measurements of rate of photosynthesis and of water status or destructive biochemical measurements for quality assessment, as well as molecular genetic procedures.
Moreover phenotyping in perennials, like grapevine, needs to be done directly in the field.
In recent years, however, there have been significant advances in the development and application of non-destructive and sensor-based phenotyping technologies suitable for use on a vineyard scale and designed for high throughput in breeding programs or even precision applications in viticulture.
The state-of-the-art of research for application of phenotyping in grapevine management and breeding will be discussed such as: i) the needs and technical requirements (field research, traits, throughput) from the perspective of agronomic management practices and breeding programs; ii) the current non-invasive technologies (imaging methods, data analysis pipelines and automation), that have significant chance of application in grapevine, based on the needs and traits; iii) finally, a critical evaluation of the most promising research directions within and some future avenues for solutions for specific use cases.
Kicherer, A., Herzog, K. and Töpfer, R. (2024). High-throughput field phenotyping in vineyards: demand, approaches, objectives. Acta Hortic. 1390, 273-278
DOI: 10.17660/ActaHortic.2024.1390.33
https://doi.org/10.17660/ActaHortic.2024.1390.33
DOI: 10.17660/ActaHortic.2024.1390.33
https://doi.org/10.17660/ActaHortic.2024.1390.33
automation, phenotyping bottleneck, digitalization, precision viticulture, disease detection, phenotyping platform, sensor-based approaches
English