Developing Sensing and Robotics Technologies for Plant Phenomics

Li, Changying

Title:

Developing Sensing and Robotics Technologies for Plant Phenomics

dc.contributor.author	Li, Changying
dc.contributor.corporatename	Georgia Institute of Technology. Institute for Robotics and Intelligent Machines	en_US
dc.contributor.corporatename	University of Georgia. School of Electrical and Computer Engineering	en_US
dc.date.accessioned	2017-11-20T15:05:57Z
dc.date.available	2017-11-20T15:05:57Z
dc.date.issued	2017-10-25
dc.description	Presented on October 25, 2017 from 12:15 p.m.-1:15 p.m. in the Georgia Tech Manufacturing Institute (GTMI), Calloway Building Auditorium, Georgia Tech.	en_US
dc.description	Changying “Charlie” Li is a professor in the School of Electrical and Computer Engineering in the College of Engineering of the University of Georgia. The long-term goal of Li’s research program is to develop sensing and robotics technologies for high-throughput plant phenotyping. His research efforts have resulted in more than 100 peer-reviewed journal articles and conference proceedings. Several major awards, such as the Information and Electrical Technology Division Best Paper Award in 2014 and 2017, the Rain Bird Engineering Concept of the Year Award in 2017, and the New Holland Young Researcher Award in 2016—all from the ASABE— have recognized his work. Li is currently the lead PI of two large national initiative projects working on plant high-throughput phenotyping and robotics through the National Robotics Initiative and Specialty Crop Research Initiative. Since 2014, Li has been leading the Agricultural Sensing and Robotics Initiative (one of the three CENGR Strategic Research Initiative Projects) and the Cyber Physical Collaboratory at the CENGR, as well as the inter-college (CENGRCAES) collaborative research initiative in field-based high-throughput phenotyping.	en_US
dc.description	Runtime: 58:25 minutes	en_US
dc.description.abstract	Feeding a world population of 9 billion people by 2050 amidst increasing climate variability is one of the greatest challenges facing humanity. The genomics revolution provides unprecedented power to develop new and advanced crop cultivars with the gene combinations needed to address these global challenges. However, rapid and repeatable measurement of crop phenotypic parameters remains a major bottleneck in plant genomics and breeding programs. High-throughput phenotyping technologies that can quickly and repeatedly scan tens of thousands of individuals using an array of advanced sensor and data analytics tools are critical to improving the ability of scientists to dissect the genetics of quantitative traits such as yield and stress tolerance. Our interdisciplinary team is developing a robot-assisted field-based high throughput phenotyping system that integrates both ground and unmanned aerial elements to quantitatively measure a suite of key traits iteratively throughout the growing season. This project is expected to unmask plant responses that will inform a new level and quality of decision-making in the selection of crop genotypes for specific production conditions. A deep learning convolutional neural network was used to identify flowers and the flowering peak of cotton plants. The task coordination between ground and aerial vehicles will result in new discoveries in the area of partitioning and coverage control. In a different but related project, the talk will introduce a Berry Impact Recording Device (BIRD) sensor that has been successfully developed to simulate a real berry to quantitatively measure mechanical impacts on the real fruit created by machine harvesters, packing lines, and transportation vehicles.	en_US
dc.format.extent	58:25 minutes
dc.identifier.uri	http://hdl.handle.net/1853/58971
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.relation.ispartofseries	IRIM Seminar Series
dc.subject	Agriculture	en_US
dc.subject	Climate	en_US
dc.subject	Genomics	en_US
dc.subject	Global hunger	en_US
dc.subject	High‐throughput phenotyping	en_US
dc.subject	Phenotyping	en_US
dc.subject	Robotics	en_US
dc.title	Developing Sensing and Robotics Technologies for Plant Phenomics	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.corporatename	Institute for Robotics and Intelligent Machines (IRIM)
local.relation.ispartofseries	IRIM Seminar Series
relation.isOrgUnitOfPublication	66259949-abfd-45c2-9dcc-5a6f2c013bcf
relation.isSeriesOfPublication	9bcc24f0-cb07-4df8-9acb-94b7b80c1e46