Robotics Colloquium: Guest Speaker- Ce Yang
Title: Drone and Ground-Based Remote Sensing for Precision Agriculture and Phenotyping
Abstract: Minnesota is a lead-producing state for several staple crops, including corn, soybean, and wheat. AI and remote sensing applied to agricultural fields enhance variable rate technology, which addresses field spatial variations and improves crop production by eliminating diseases and managing stresses more efficiently. Deep learning disease/stress detection modeling using remote sensing data and AI can also provide agricultural researchers with high-throughput solutions in field studies, e.g., scoring for diseases, pests, and stresses. This talk focuses on drone and ground-based remote sensing and AI modeling in Ce Yang's group for precision agriculture and high-throughput phenotyping.
Bio: Ce Yang is an associate professor working on remote sensing for precision agriculture and high-throughput phenotyping. She leads the Agricultural Robotics Lab at the University of Minnesota to work on nutrient management, yield prediction, and disease detection of staple food crops. The Ag Robotics Lab’s mission is to apply advanced ideas of robotics, remote sensing, data mining, and information technology to precision agriculture. Their core techniques include multispectral/hyperspectral imaging, spectroscopy, machine learning, geographic information systems (GIS), digital mapping, biochemical sensing, etc. The tools available for carrying out her research are unmanned aerial vehicles, unmanned ground vehicles, video cameras, multispectral cameras, hyperspectral cameras, DGPS, and various electrical, optical, and chemical sensors. Ce Yang obtained her Ph.D. in Agricultural Engineering and MS in Computer Science and Engineering at the University of Florida.