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Facial recognition software can help save multibillion dollar grape crop

An unusual collaboration between a biologist and an engineer might greatly enhance the current to protect grape crops. The new technology, using robotics and AI to identify grape plants infected with a devastating fungus, will soon be available to researchers nationwide working on a wide array of plant and animal research.

Biologist Lance Cadle-Davidson, an adjunct professor in the School of Integrative Plant Science (SIPS), is working to develop grape varieties that are more resistant to powdery mildew, but his lab’s research was bottlenecked by the need to manually assess thousands of grape leaf samples for evidence of infection.

Cadle-Davidson is also a research plant pathologist with the U.S. Department of Agriculture’s Agricultural Research Service (USDA-ARS). He works in the Grape Genetics Research Unit in Geneva, New York, and his team developed prototypes of imaging robots that could scan grape leaf samples automatically. The process is called high-throughput phenotyping and is a partnership with the Light and Health Research Center. This partnership led to the creation of a robotic camera they named “BlackBird.”

The BlackBird robot can gather information at a scale of 1.2 micrometers per pixel – equivalent to a regular optical microscope. For each 1-centimeter leaf sample being examined, the robot provides 8,000 by 5,000 pixels of information.

Using breakthroughs in deep neural networks developed for computer vision tasks like face recognition, an analysis of the microscopic images of the grape leaves was possible. In addition, a network of inferential processes was set up, which help biologists better understand the analysis process and build confidence with AI models.


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