Advances in plant phenomics and digital breeding in Korea

Abstract

Global demographic expansion and accelerating climate change are heightening the need for sustainable enhancement of crop productivity and resilience to environmental stresses. As conventional breeding approaches based on empirical selection approach their limits, digital breeding is emerging as an integrated framework that combines high-throughput imaging, multi-source data, and artificial intelligence (AI). Although next-generation sequencing (NGS) and smart-farming systems have enabled large-scale accumulation of genomic and environmental datasets, the phenotypic dimension remains a critical bottleneck in predictive breeding. To overcome this limitation, Korea has established six large-scale national phenotyping platforms equipped with advanced RGB, hyperspectral, and thermal imaging systems for high-throughput, precision phenotypic data acquisition. These platforms support quantitative and non-destructive monitoring of plant morphology, stress responses, and developmental dynamics and employ AI-driven classification and predictive modeling to derive biologically relevant traits. The integration of genomic, phenomic, and environmental datasets through AI-based analytical pipelines is expected to accelerate digital breeding, facilitating the development of climate-resilient and consumer-oriented cultivars within a data-driven agricultural paradigm. Collectively, plant phenomics research is evolving beyond image-based observation toward a comprehensive, predictive framework that provides the technological basis for next-generation precision agriculture.

Code and data availability

This is a review article surveying Korean phenomics infrastructure and prior studies; it presents no original phenotype datasets, images, code, or models. Its only data statement is on-request availability, and no public paper-specific asset URL is provided.

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