b'MACHINE LEARNING and COMPUTER VISION areBOLSTERING BREEDINGA revolution in soybean breeding for root traits has begun, with the presentation of a successful new pipeline involving the latest cutting-edge technology.Treena Hein WHEN CROP BREEDERSlong ago learned of single nucleotide polymorphismsSNPs, differences in a single building block/nucleotide such as cytosine in place of thymine, in a given stretch of DNAthey have wanted to be able to correlate these differences with plant traits, taking breed-ing to an entirely new level. Now, building on past achievements in the field, a team of scientists in the departments of Agronomy and Mechanical Engineering at Iowa State University and at the U.S. Department of Agriculture (USDA) Agricultural Research Service have published results on their new soybean breeding framework that links genetic informa-tion with root traits using computer vision and machine learning (ML) tools.Project co-lead Asheesh K. Singh (a professor in the Department of Agronomy at Iowa State University) notes that some of these methods have already been used with tremendous success to measure crop traits or predict yield. More mainstream deployment has happened in the past five to six years, he explains. However, in terms of the study of roots for plant breeding applications, our pro-ject is among the first to establish a user-friendly pipeline built on ML tools and then use the pipeline to study traits in a way, and at a scale, that was previously difficult.Manitobas Kevin Falk (who was Singhs PhD stu-dent) and Zaki Jubery (scientist in the group of co-lead Baskar Ganapathysubramanian) and their colleagues imaged thousands of soybean root systems and used ML to measure root traits and correlate differences in root system architecture (RSA) to the genome of the plants. Those plants with RSA that enables greater drought tolerance and nutrient acquisition are of particular inter-est in breeding for better plant performance and higher yield.Root phenotype combined with SNP-based geno-PhD student Clayton Carley is working on using deep learning coupledtype allowed us to correlate genetics with morphology. with image processing tools to measure above-ground traits that couldIts a first step that could allow breeders to select for be correlated with soybean root traits.root morphologies, such as drought-tolerant root sys-36GERMINATION.CANOVEMBER 2020'