Dr. Srinivas Sridharan, UIUC Site Leader and Lead Imaging Data Scientist, Corteva Agriscience will give a presentation during the CAII Seminar Series on Monday, March 29 at 11:00 a.m. The talk is titled “Satellites to Sequences: Techniques and Applications of Machine Learning in Agriculture."
View Seminar here: https://go.ncsa.illinois.edu/CAIISpringSemesterSeriesSP21
Abstract: Agriculture today has changed drastically in the past few decades due to ever growing demands to feed an increasing population. At Corteva we aim to enrich the lives of those who produce and those who consume, ensuring progress for generations to come. Our customers are posed with multiple challenges that limit the yield and quality potential of our seed products. There is a great need to apply state-of-the-art tools and technology to develop integrated solutions to improve crop productivity with the ultimate goal of feeding the world.
This talk will mainly focus on satellites, drones, mobile devices, LIDAR, microscopes, hyperspectral/florescence imagers, IoT sensors, state-of-the-art machine learning and deep learning algorithms to phenotype our germplasm in lab and field environments. In this talk I will give a broad overview of imaging in agriculture, a brief introduction to the deep learning revolution, and examples of how we utilize these techniques to augment our research pipeline.
Speaker Bio: Dr Srinivas Sridharan joined Corteva Agrisciences in 2017 as an Imaging Data Scientist in the Applied Imaging and Computer Vision group in Data Science and is also the Corteva UIUC research park site leader. His work focuses on developing machine learning and deep learning algorithms to phenotype germplasm in lab and field environments and build robust, ubiquitous, and cost-effective image analytic solutions for customers. Before Corteva, he worked as an assistant professor in the computer science department at Stevens Institute of Technology, Hoboken, NJ. Srinivas received his Master’s in Electrical Engineering and Ph.D. in Computing and Information Sciences from Rochester Institute of Technology, Rochester, NY. His Ph.D. dissertation focused on machine learning and applied perception in guiding visual attention, task-based eye movement prediction, and real-world search task inference using eye tracking. His research interests are machine learning, deep learning, computer vision, and virtual and augmented reality for 3D graphics and visualization.
This presentation will be not be recorded.