Metabolome selection for enhancing abiotic stress resilience: advances in phenomics, prospects and challenges for breeding applications

Abiotic stresses such as drought, extreme heat, salinity limit growth and development of crop plants, and reduce yield and productivity. Genomics assisted breeding is paving the way for the accelerated genetic gain. However, marker-trait association studies like association mapping, and genomic selection are constrained by the 'phenotyping bottleneck' arising from laborious, resource-intensive traditional phenotyping methods. Advent of high throughput phenotyping methods using diverse imaging sensors, and associated advances in their mounting platforms, imaging data analytics by deep learning have improved the efficiency of phenotyping of morphological-, physiological traits and metabolites. In this review, we summarized the recent studies associated with high throughput phenotyping of physiological traits and metabolites including the metabolome underlying stress tolerance traits. Imaging sensors and machine-vision based phenomics, especially hyperspectral imaging, will further accelerate the identification of stress tolerant parents, physiological traits, genes and their corresponding proteins and metabolites, and their introgression into elite parents, to develop climate resilient crops towards enhanced agricultural productivity and sustainability.