Using recent trends in machine learning/AI, models will be developed and tested to select germplasm suitable for production under adverse environmental conditions. The focus will test small grains crop systems using input factors from database resources associated with genomes, traits, environments, among other trends. Models will be identified and tasked to predict optimized germplasm sets which can be benchmarked against plant breeder selection lines and tested in established field trials.
