Grapes are protected with the help of face recognition technology

A radical collaboration between a biologist and an engineer is working to protect grapes. The technology developed using robotics and AI to identify vines contaminated with fungi has recently been made available to researchers working in various botanical and animal studies.

Biologist, Lance Cadley-Davidson, Ph.D. “03”, an assistant professor at the Institute of Plant Sciences (SIPS), is working to develop grapes that are more resistant to powdery mildew, but the laboratory study was forced to manually evaluate thousands of leaf samples. Infectious disease.

The fungus, which attacks many plants, including mildew, wine, and table wine, leaves white scars on leaves and fruits, costing wine producers around the world billions of dollars a year in lost fruit and mushrooms.

Cadley-Davidson is a pathogen in the USDA-ARS research plant. He works in the Department of Genetics Research in Geneva, New York, and his team developed prototype robots protocols that can automatically scan grape samples: a process called phenotyping – funded by USDA-ARS through the VitisGen2 Vinegar Project and Partner with Light and Health Research Center. This partnership led to the creation of a robot camera called the BlackBerry.

But extracting relevant biological information from these images was still an important interest.

Enter Engineer and Computer Scientist – Yu Jiang, Assistant Research Professor in Cornell Agritech at SIPS Gardening Department. Jiang’s research focuses on system engineering, data analysis, and artificial intelligence. A blackboard robot can measure data at 1.2 micrometers – with a standard optical microscope. Each 1 cm of leaf sample is examined and the robot provides information at 8,000 by 5,000 pixels.

Extracting useful information from such a large, high-resolution image was Yanyan’s challenge, and his team used AI. Using face-to-face discoveries in deep neural networks designed for computer vision, Jiang applied this knowledge to microscopic grapes. Jiang and his team also applied the visualization of network transmission processes to help biologists better understand the analysis process and build trust in II models.

Working together, the Cadle-Davidson team examines and verifies the robots’ vision, enabling the Jiang team to teach them how to more effectively identify biological characteristics. The results are impressive, says Cadley-Davison. Six months of research experiments to complete the entire laboratory team now take BlackBerry robots in just one day.

“Our science has revolutionized,” says Cadley-Davidson. And we know that II tools really do a better job of explaining the genes of these grapes.

In July alone, the partnership received a prize and two new grants. On July 1, the team received a $ 100,000 grant from USDA-ARS to distribute to BlackBard field offices working on other high-yielding crops.

“We look forward to finding cooperative laboratories that will join us in using this tool,” Jiang said. We look at possible applications for this research in herbal studies, animal husbandry, or medical purposes.

At the 2021 Annual International Conference of the American Association of Agricultural and Biological Engineers, the team won the Best Paper Award for Information Technology, Sensor and Control Systems. And on July 27, the Cornell Digital Agricultural Research Innovation Fund was awarded $ 150,000 for two years to begin upgrading the BlackBerry robot to infrared beyond the red-green-blue tissue.

Plant diseases such as powdery mildew can appear in infrared before they are visible to the naked eye; If researchers are able to develop tools to help early detection of disease, it will allow farmers to target fungal pesticides before the infection spreads, which means reducing the number of fungi and lost crops. They are working with scientists to better integrate AI into data analysis.

“This work is accelerating the growth of wine and genetics,” said Donald Brown, president of the National Wine Research Alliance. Traditionally, when we invest in research in industries, we know that we will never see the results of our investments in our lifetime: it is really a trust-based investment in the next generation of manufacturers. But now this technology is really shortening that time line for the benefit of farmers and consumers.

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