Artificial Intelligence for Sustainable Agriculture

Wednesday, April 12  |  9:30 a.m.

Plant Sciences Building  |  Seminar Rooms A&B

Zhou Zhang

Dr. Zhou Zhang received her B.S degree in astronautics engineering and M.S. degree in instrumentation science and opto-electronics engineering from Beihang University, Beijing, China, in 2010 and 2013, respectively. Then, she got her Ph.D. degree in 2017 in geomatics, civil engineering at Purdue University, USA. Her dissertation topic is about developing new machine learning methods for hyperspectral remote sensing data classification. During 2017-2019, she worked as a Postdoc Scholar at the University of California, Davis on almond yield prediction using satellite remote sensing (Landsat and others)  and machine learning. She is currently an Assistant Professor in Biological Systems Engineering in College of Agriculture and Life Science at the University of Wisconsin-Madison, USA. Her research interests include satellite remote sensing (Landsat, MODIS, Sentinel, etc), drone-based imaging platform developments for precision agriculture, multi-source remote sensing data fusion, artificial intelligence and machine learning in agricultural applications. Dr. Zhang has over 50 publications in peer-reviewed journals and conferences. Dr. Zhang was a recipient of the Best Student Paper (third place) in 2016 IEEE IGARSS Student Paper Competition. 

Research Seminar:

Sustainable agriculture requires intensive data collection and processing to make more efficient use of farm inputs, leading to improved agricultural productivity and environmental quality. Advances in remote sensing technique permit data acquisition from multi-sensors across multi-platforms, and the multi-source data are usually combined for the decision making. The utility of artificial intelligence (AI) for sustainable agriculture will be discussed. Three USDA NIFA projects that use AI tools for agricultural decision making will be covered, including: (1) Developing an Integrated Deep Learning Modeling Framework for County-Level Crop Yield Prediction in support of USDA NASS Operation; (2) AlfAdvisor: A web-based cyber-platform to estimate alfalfa yield and quality to support harvest scheduling; and (3)  Harnessing Machine Learning and Hyperspectral Imaging for High-Throughput Maize Silage Phenotyping. Future work plans in this area will also be demonstrated at the end.