Erik J. Sacks

PhenoStream automates crop trait extraction from aerial imagery, enabling high-throughput phenotyping with reduced latency and manual effort.

This study evaluates MTME genomic prediction models for Miscanthus sacchariflorus, finding they improve prediction for some traits but not all, depending on the cross-validation scenario.

This study evaluates sparse testing designs for genomic prediction in Miscanthus breeding to reduce phenotyping costs while maintaining predictive accuracy.

This study optimizes genomic prediction in Miscanthus sacchariflorus by incorporating weather data to improve model performance across different locations.

The paper evaluates cross-validation schemes for genomic selection in perennial Miscanthus species, showing improved predictive ability by incorporating genotype-by-environment interactions.

A machine learning approach accelerates plant phenotyping by reducing the need for large amounts of human-annotated data, enabling faster and more efficient detection of a plant trait using aerial imagery.

The paper introduces a statistic to filter paralogs in genotyping-by-sequencing data from duplicated genomes, enabling efficient locus filtering before genotype calling.

polyRAD improves genotype calling in polyploids and diploids by using Bayesian methods with flexible priors, reducing errors from low read depth and improving genotype accuracy.