AI Enhanced Pre-breeding of Lettuce for Sustainable Production

Lettuce is one of the most valuable, widely consumed vegetable crops and a major contributor to the quality of the American diet. This highly collaborative, multi-state, long-term project provides the foundation of public and private sector lettuce improvement programs. It is currently challenging to exploit the vast amounts of genomic, developmental, physiological, and phenotypic data for cultivated lettuce and its wild relatives that we and others have generated. Therefore, we are training an AI foundation model on these diverse datasets in order be able to use these data resources for lettuce improvement. There are four work packages. The first is focused on training the foundation model, followed by experimental validation of the model. This will greatly assist breeders to work in this data-rich environment. It will assist in predicting the phenotypic consequences of allelic variation as well as help in the selection of superior genotypes; it will also predict the consequences of gene editing. The second work package is using genome editing to domesticate wild accessions that we have previously shown to exhibit tolerance to abiotic stressors by knocking out known domestication genes. This will provide germplasm with abiotic stress tolerance that has a cultivated phenotype and therefore can be more readily used in breeding programs. The third work package is using AI to make the successful CropManage software more accessible and increase its adoption. This will immediately impact water and nitrogen use efficiencies of lettuce crop production. The fourth work package is investigating the economics of adoption of Crop Manage and of genome edited lettuce. This project will result in more efficient production of high-quality lettuce for the consumer as well as making major contributions to the economies of lettuce producing areas in California, Arizona, and Florida. 

Principal Directors and Investigators