Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and
environmental data. ML algorithms automatically identify relevant features and use cross …

Grain yield is increasingly affected by climate factors such as drought and heat. To develop
resilient and high-yielding cultivars, high-throughput phenotyping (HTP) techniques are …

Estimation of breeding values through Best Linear Unbiased Prediction (BLUP) using
pedigree‐based kinship and Marker‐Assisted Selection (MAS) are the two fundamental …

The performance of genomic prediction (GP) on genetically correlated traits can be
improved through an interdependence multi-trait model under a multi-environment context …

Wheat quality improvement is an important objective in all wheat breeding programs.
However, due to the cost, time and quantity of seed required, wheat quality is typically …

Implementing genomic-based prediction models in genomic selection requires an
understanding of the measures for evaluating prediction accuracy from different models and …

Multi-trait and multi-environment analyses can improve genomic prediction by exploiting
between-trait correlations and genotype-by-environment interactions. In the context of …

It is of paramount importance in plant breeding to have methods dealing with large numbers
of predictor variables and few sample observations, as well as efficient methods for dealing …

Genomic selection (GS) changed the way plant breeders select genotypes. GS takes
advantage of phenotypic and genotypic information to training a statistical machine learning …

Genomic prediction has enabled plant breeders to estimate breeding values of unobserved
genotypes and environments. The use of genomic prediction will be extremely valuable for …