Agriculture Robotics

Background: The genomics revolution provides unprecedented power to engineer new and advanced crop cultivars with the gene combinations needed to support the rapidly increasing world population while adapting to the climate changes. Currently, relating molecular signatures to key differences in phenotype (such as plant or root architecture, yield, and stress or pest resistance) has been laborious, expensive, and imprecise, requiring manual assessment of one plant at a time for traits that may be difficult to score visually. As such, rapid and repeatable measurement of crop phenotypic parameters is a major bottleneck in plant breeding programs.

The main objective of this project is to develop robot-assisted high-throughput phenotyping technologies that can quickly scan thousands of individuals using an array of advanced sensor and data analytic tools that are crucial for improving our ability to dissect the genetics of quantitative traits such as yield and stress tolerance. This project develops ground and aerial robotic systems equipped with advanced sensors (LiDAR, 3D, color, thermal, and multispectral) and artificial intelligence methods for plant phenotypic traits measurement. The success of the project will contribute significantly to the global food security and sustainable development.

Support: NSF/USDA National Robotics Initiative (NRI) program (2017-2020)

Collaborators: Prof. Charlie Li (UGA CENGR) and Prof. Andrew Paterson (UGA Dept. of Genetics)