Translating High-Throughput Phenotyping into Genetic Gain

By Jose Luis Araus, Shawn C. Kefauver, M. Zaman-Allah, M. Olsen, Jill E. Cairns. Trends in Plant Science, 2018

630 citations recorded by the source corpus.

Inability to efficiently implement high-throughput field phenotyping is increasingly perceived as a key component that limits genetic gain in breeding programs. Field phenotyping must be integrated into a wider context than just choosing the correct selection traits, deployment tools, evaluation platforms, or basic data-management methods. Phenotyping means more than conducting such activities in a resource-efficient manner; it also requires appropriate trial management and spatial variability handling, definition of key constraining conditions prevalent in the target population of environments, and the development of more comprehensive data management, including crop modeling. This review will provide a wide perspective on how field phenotyping is best implemented. It will also outline how to bridge the gap between breeders and ‘phenotypers’ in an effective manner.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/66fa3de81309233c3ebae0fe29a2b5c1992fa1d8

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Citations in the corpus

High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture (2019, background): “In this aspect, high-throughput phenotyping provides possibilities for increasing selection intensity, improving selection accuracy and improving the decision support system [21].”
High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms (2020, background): “The relatively recent availability of high-throughput crop phenotyping methods has influenced developments in crop selection and improvement [5], [6], and previous reviews have presented new opportunities for using ground and aerial phenotyping platforms that carry different sensors [7], [8].”
Crop Phenomics and High-throughput Phenotyping: Past Decades, Current Challenges and Future Perspectives. (2020, neutral): “Phenotyping is a key informant for establishing the accuracy of statistical models in conventional breeding, marker-assisted selection (MAS), or GS; however, high-throughput field phenotyping is still a bottleneck in these fields (Araus et al., 2018).”
Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective (2018, positive): “Thus, high-throughput phenotyping aided by computer vision with various sensors and algorithms for image analysis will play a crucial role for crop yield improvement in scenarios related to population demography and climate change [9].”