Application of artificial intelligence to enhance potato breeding and genetics

Quantitative genetics and predictive breeding are data-intensive methods that associate haplotype inheritance and phenotypic characteristics in structured breeding populations. Scientists in the Temperate Tree Fruit and Vegetable Research Unit located in Prosser, WA and cooperators at Washington State University are using automation and machine vision to rapidly and inexpensively capture biologically important measurements from potato tubers. Our team developed an RGB-D imaging conveyor system that utilizes artificial intelligence to detect, track, capture, and extract measurements from images of individual potatoes. This platform was used to evaluate >75,000 individual tubers derived from ~1,300 samples (~32 breeding families). Our approach enables us to rigorously assess the inheritance of potato yield components (size of tubers, number of tubers), tuber shape descriptors, and potato skin color characteristics. We are currently working to train additional deep learning models to detect potato tuber defects including sprouting, growth cracks, secondary growth, and tuber greening.