High‐throughput phenotyping for breeding targets—Current status and future directions of strawberry trait automation

Abstract

Societal Impact Statement Strawberry breeders are faced with increasing demands by propagators, growers, retailers and consumers for particular agronomic traits. This and the volume of plants requiring assessment during selection constrain breeders to rapid and qualitative rating methods. High‐throughput systems for assessing these traits automatically could indicate which families, or individual genotypes, should be singled out for further, more thorough evaluation, thus significantly increasing the selection intensity and accuracy. This review assesses the current status of and future potential for automated phenotyping in strawberry crops, highlighting key advances and the gaps which need to be addressed to facilitate the development of such technology. Summary Automated image‐based phenotyping has become widely accepted in crop phenotyping, particularly in cereal crops, yet few traits used by breeders in the strawberry industry have been automated. Early phenotypic assessment remains largely qualitative in this area since the manual phenotyping process is laborious and domain experts are constrained by time. Precision agriculture, facilitated by robotic technologies, is increasing in the strawberry industry, and the development of quantitative automated phenotyping methods is essential to ensure that breeding programs remain economically competitive. In this review, we investigate the external morphological traits relevant to the breeding of strawberries that have been automated and assess the potential for automation of traits that are still evaluated manually, highlighting challenges and limitations of the approaches used, particularly when applying high‐throughput strawberry phenotyping in real‐world environmental conditions.

Keywords

• automation potential
• computer vision
• high‐throughput
• phenotyping
• strawberry breeding
• trait automation