MnRI Seminar: Aaron Lorenz and Suma Sreekanta
Developing Phenotyping Solutions Towards Optimizing Soybean Shoot Architecture
Attaining a 50% increase in the yield of major crops by 2050 to feed the expected population ranks among one of our greatest societal challenges. It has been suggested that one promising way to break through this dilemma is to revolutionize the efficiency of crop light harvesting systems. Soybean, the second most widely grown crop in the U.S., is well known to be sub- optimal in its canopy structure, limiting its light interception efficiency. Despite this, very little is known about the soybean shoot architecture properties creating an optimal canopy structure for light interception.
Soybean plants are made up of repeating units of branches and leaves. The dimensions and orientation of these units change in time and space in response to the environment giving way to remarkable phenotypic diversity. An estimation of how the overall architecture influences light penetration and scatter within the soybean canopy requires us to be able to accurately image and analyze structural data on many thousands of plants over a short period of time. Soybean phenotyping at mass scale requires novel high-throughput robotics based imaging techniques. It also makes for a compelling argument for applying a computer vision- and machine learning-based approach, a massive step up from the current manual ineffective process currently in use.
About Aaron Lorenz
Aaron Lorenz is an Associate Professor of Soybean Breeding and Genetics in the Department of Agronomy and Plant Genetics at the University of Minnesota. The University of Minnesota Soybean Breeding Program develops specialty, food-type, and general-use soybean varieties adapted to the Upper Midwest. Dr. Lorenz’s research focuses on the optimization and application of genomics and phenomics to an applied cultivar development program. Additional areas of research include the mapping of genes underlying complex traits relevant to soybean production and the development of soybean varieties adapted to new cropping systems.
About Suma Sreekanta
Suma Sreekanta is a plant biologist and geneticist by training and technology junkie by enthusiasm. She received her BS in Botany from Miami University studying flooding stress tolerance in soybeans. She received her PhD from the University of Minnesota studying plant immune signaling in the model system Arabidopsis thaliana. As a post-doctoral researcher, she studies soybean canopy architecture and its influence on canopy coverage and light interception. She is actively engaged in collaborative projects involving expertise in agriculture, engineering, computer science and machine learning to forward a more data driven agenda to field phenotyping in crops. She is keen on working with others who are just as enthusiastic about pushing boundaries to field phenotyping considered difficult if not impossible.