Arron Hyrum Carter

AGIcam: An open-source IoT-based camera system for automated in-field phenotyping and yield prediction

AGIcam provides an IoT-based camera system for continuous in-field plant phenotyping and yield prediction using time-series imagery and machine learning models.

Classification and Regression Models for Genomic Selection of Skewed Phenotypes: A Case for Disease Resistance in Winter Wheat (Triticum aestivum L.)

This study compares classification and regression models for predicting skewed phenotypes in winter wheat disease resistance, finding that transformed regression and classification models perform best.

Comparison of Single-Trait and Multi-Trait Genome-Wide Association Models and Inclusion of Correlated Traits in the Dissection of the Genetic Architecture of a Complex Trait in a Breeding Program

This study compares single-trait and multi-trait GWAS models to dissect the genetic architecture of seedling emergence in wheat, finding that multi-trait models identify more large-effect markers.

Genome-Wide Association Studies and Genomic Selection for Grain Protein Content Stability in a Nested Association Mapping Population of Spring Wheat

Development of the Wheat Practical Haplotype Graph Database as a Resource for Genotyping Data Storage and Genotype Imputation

The paper develops a wheat haplotype graph database for efficient genotype storage and imputation, showing improved accuracy over existing methods.

Comparison of Genomic Selection Models for Exploring Predictive Ability of Complex Traits in Breeding Programs

This study compares 17 genomic selection models for predicting a complex trait in wheat breeding, finding that parametric models like cBLUP provide consistent moderate accuracy, making them suitable for practical implementation.

Multi-Trait Machine and Deep Learning Models for Genomic Selection using Spectral Information in a Wheat Breeding Program

This study uses multi-trait machine and deep learning models with spectral data to improve genomic selection predictions for grain yield and protein content in wheat.

Breeding with Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance

This paper evaluates genomic selection models for predicting quantitative disease resistance in wheat, finding higher accuracy than marker-assisted selection when combining major and minor gene markers.

Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models

This study uses machine and deep learning models to predict end-use quality traits in wheat breeding, achieving higher accuracy than traditional methods.

Gains through selection for grain yield in a winter wheat breeding program

This study compares selection strategies for grain yield in winter wheat, finding that combining phenotypic selection with genomic selection improves genetic gains.