Remote Sensing U.S. and Global Agriculture

New NSF Funded Deep Learning Project

Sept. 11, 2018—The University of Minnesota announced today that it has received a three-year, $1.43 million grant from the National Science Foundation to advance machine learning techniques to better monitor global agricultural and environmental change—a practice that can help society address the challenges of adapting to a changing climate, managing land use and natural resources, and sustainably feeding a growing population.

The NSF grant funds a group of researchers at  the University’s College of Science and Engineering and G.E.M.S team from the College of Food, Agricultural and Natural Resource Sciences (CFANS) and Minnesota Supercomputing Institute (MSI) to advance the state-of-the-art in machine learning for analyzing data from earth observing satellites and generating actionable information on a global scale.

The project’s main focus will be to advance the state-of-art in machine learning techniques for analyzing spatial and temporal agricultural cropping data and urban landscapes. Such analyses can produce critical information needed for developing sustainable practices for increasing crop yields and for managing water run-off flows and quality. In particular, the team aims to develop and advance deep learning (a subfield of machine learning) techniques, to monitor these global changes by analyzing remote-sensing data obtained from satellites. Collaborators at the Nature Conservancy and the D.C. Water and Sewer Authority will help evaluate the effectiveness of the machine learning techniques developed in this project.

Using changes in NDVI over season to identify soybean crop

 

Crop identification from remote-sensed imagery

The project is one example of the University’s larger capabilities for using big data analytics to drive innovation in the areas of food and agriculture, which are based in the University’s G.E.M.S agroinformatics platform. The G.E.M.S platform does the hard work of making these different data types interoperable and provides the framework for complex analysis that guides decision-making in areas such as responding to emerging diseases, developing sustainable farming practices, and increasing resource use efficiency.