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Review
. 2022 Feb;64(2):592-618.
doi: 10.1111/jipb.13191.

Crop phenotyping in a context of global change: What to measure and how to do it

Jose Luis Araus  1   2 , Shawn Carlisle Kefauver  1   2 , Omar Vergara-Díaz  3 , Adrian Gracia-Romero  1   2 , Fatima Zahra Rezzouk  1   2 , Joel Segarra  1   2 , Maria Luisa Buchaillot  1   2 , Melissa Chang-Espino  1   2 , Thomas Vatter  1   2 , Rut Sanchez-Bragado  1   2 , José Armando Fernandez-Gallego  1   2   4 , Maria Dolores Serret  1   2 , Jordi Bort  1   2
Affiliations
Review

Crop phenotyping in a context of global change: What to measure and how to do it

Jose Luis Araus et al. J Integr Plant Biol. 2022 Feb.

Abstract

High-throughput crop phenotyping, particularly under field conditions, is nowadays perceived as a key factor limiting crop genetic advance. Phenotyping not only facilitates conventional breeding, but it is necessary to fully exploit the capabilities of molecular breeding, and it can be exploited to predict breeding targets for the years ahead at the regional level through more advanced simulation models and decision support systems. In terms of phenotyping, it is necessary to determined which selection traits are relevant in each situation, and which phenotyping tools/methods are available to assess such traits. Remote sensing methodologies are currently the most popular approaches, even when lab-based analyses are still relevant in many circumstances. On top of that, data processing and automation, together with machine learning/deep learning are contributing to the wide range of applications for phenotyping. This review addresses spectral and red-green-blue sensing as the most popular remote sensing approaches, alongside stable isotope composition as an example of a lab-based tool, and root phenotyping, which represents one of the frontiers for field phenotyping. Further, we consider the two most promising forms of aerial platforms (unmanned aerial vehicle and satellites) and some of the emerging data-processing techniques. The review includes three Boxes that examine specific case studies.

Keywords: crop phenotyping; deep learning; models; photosynthesis; platforms; remote sensing; roots; satellites; sensors; stable isotopes.

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References

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