Artificial intelligence (AI) akin to that used in facial-recognition software is accelerating the grape-breeding process by accurately identifying those individual vines that carry favorable genetic characteristics, notably those that provide mildew resistance and higher fruit quality. The robotic camera system developed at Cornell University, called Blackbird, could also help select parent breeding stock resistant to other pathogens and, as a more immediate benefit for growers, could be used to determine optimum fungicide combinations for different geographic localities. The Cornell-led, U.S. Department of Agriculture-funded VitisGen2 project uses a high-tech genetic sequencing approach, known as the rhAmpSeq system, to sift through the part of the genome that is common to all grapes and find DNA markers -- bits of genetic code -- that are associated with genetic traits of special interest to breeders. Even with these advances, technicians still had to spend hours hunched over microscopes, manually scanning small circular leaf samples for signs of powdery and downy mildew infection. This entailed clearing away the chlorophyll from the leaf tissue, staining each leaf disk so the mildew's filamentous and otherwise-transparent hyphae would show up, and assessing the presence and extent of the infection, said USDA research plant pathologist Lance Cadle-Davidson, who was part of the USDA-Cornell University team that developed rhAmpSeq for use on grape leaves.