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High-throughput phenotyping and digital image processing have generated new digital trait acquisition that can be used in the characterization and evaluation of plant genetic resources conserved in genebanks. Maize is of importance to world agriculture and has been fundamental to the food security of peasant and indigenous communities in Colombia. In Colombia, there are maize races that have been dispersed and conserved by communities leading to an increase in phenotypic variation in ear shape and kernel color, driven by domestication processes and random crosses among the diversity of races. In this chapter, we relate the morphological and colorimetric variation of ears of 24 Colombian maize races using digital image processing, machine learning, and functional diversity indices that allow characterization and selection of potential accessions for genetic improvement from digital images. We generate a methodological approach to understand and support the use of digital images in processes of characterization of genetic resources, in addition to integrating functional diversity indexes in the exploration of functional traits and selection of potential accessions in genebanks as well as in working collections in breeding programs.

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Phenomic Selection and Digital Imaging for Characterizing Plant Genetic Resources: A Case Study from the Colombian Maize Collection

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  • 发表日期：

    2025年

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  • Diego Felipe Conejo-Rodríguez,
  • Creuci Maria Caetano,
  • Héctor Estrada Marin,
  • Daniel Orlando Osorio Garcia,
  • Ana María Serna Landaeta,
  • Vanesa Diaz Giraldo,
  • Amanda Ortiz Escobar,
  • Carlos Iván Cardozo Conde,
  • Milan Oldřich Urban

摘要

High-throughput phenotyping and digital image processing have generated new digital trait acquisition that can be used in the characterization and evaluation of plant genetic resources conserved in genebanks. Maize is of importance to world agriculture and has been fundamental to the food security of peasant and indigenous communities in Colombia. In Colombia, there are maize races that have been dispersed and conserved by communities leading to an increase in phenotypic variation in ear shape and kernel color, driven by domestication processes and random crosses among the diversity of races. In this chapter, we relate the morphological and colorimetric variation of ears of 24 Colombian maize races using digital image processing, machine learning, and functional diversity indices that allow characterization and selection of potential accessions for genetic improvement from digital images. We generate a methodological approach to understand and support the use of digital images in processes of characterization of genetic resources, in addition to integrating functional diversity indexes in the exploration of functional traits and selection of potential accessions in genebanks as well as in working collections in breeding programs.