IN 2018 SOYBEAN WAS WORTH $39 BILLION IN THE U.S. PLANT BREEDING CONTRIBUTES TO THE HIGH YIELDS OF SOYBEAN. UNMANNED AERIAL SYSTEMS (UAS) AND IMAGE ANALYSIS ALLOW PLANT BREEDERS TO MEASURE GROWTH AND DEVELOPMENT IN THEIR FIELD TRIALS, AND CAN INCREASE ACCURACY OF YIELD ESTIMATION AND PREDICTION. CURRENTLY, ANALYTICAL METHODS IN PLANT BREEDING DO NOT ADEQUATELY EXPLOIT GENETIC RELATIONSHIPS AMONG TRAITS OR OBSERVATIONS COLLECTED AT DIFFERENT TIME POINTS. IN THIS PROJECT WE WILL IMPROVE AUTOMATED PHENOTYPING METHODS FROM UAS IMAGERY AND AUGMENT LEVERAGED EXPERIMENTS WITH SOPHISTICATED STATISTICAL GENOMIC METHODS. THE RESEARCH ACTIVITIES TAKE ADVANTAGE OF OUR REAL-WORLD SOYBEAN BREEDING PIPELINE AND OUR WELL-DEVELOPED UAS PHENOTYPING CAPABILITIES. OUR OBJECTIVES ARE TO: 1)IMPROVE THE ACCURACY AND AUTOMATION OF UAS IMAGE ANALYSES TO DEVELOP NOVEL LONGITUDINAL PHENOTYPES FOR GENETIC IMPROVEMENT OF SOYBEAN YIELD, AND VALIDATE THE PHENOTYPES IN PUBLIC SOYBEAN BREEDING GERMPLASM FROM THE NORTH CENTRAL REGION; AND 2) IN A SELECTION EXPERIMENT IN A BREEDING PIPELINE, TEST GENOMIC PREDICTION MODELS INCORPORATING IMAGE-BASED LONGITUDINAL PHENOTYPES FOR GENOMIC SELECTION OF YIELD IN SOYBEAN BREEDING PROGRAMS USING RANDOM REGRESSION MODELS, GENOME-WIDE MARKERS AND PEDIGREE DATA. THE OUTCOMES WILL INCLUDE RESOURCES FOR SOYBEAN BREEDERS THROUGHOUT NORTH CENTRAL REGION. ?