Knowledge-guided machine learning for the next generation of agroecosystem prediction

Zhenong Jin1, Licheng LIU2, Qi Yang3, Kaiyu Guan4, Rahul Ghosh5, Xiaowei Jia6, Chongya Jiang7, Bin Peng8, Jinyun Tang9, Shaoming Xu5, Junxiong Zhou1, Wang Zhou8, Qing Zhu10 and Vipin Kumar11, (1)University of Minnesota Twin Cities, Department of Bioproducts and Biosystems Engineering, Saint Paul, United States, (2)University of Minnesota-Twin Cities, Department of Bioproducts and Biosystems Engineering, Saint Paul, United States, (3)University of Minnesota, St Paul, United States, (4)University of Illinois at Urbana Champaign, Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, Urbana, United States, (5)University of Minnesota-Twin Cities, Department of Computer Science/Engineering, Minneapolis, United States, (6)University of Pittsburgh, Department of Computer Science, Pittsburgh, PA, United States, (7)University of Illinois at Urbana Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, IL, United States, (8)University of Illinois at Urbana Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, United States, (9)Lawrence Berkeley Natl Lab, Climate and Ecosystem Sciences Division, Berkeley, United States, (10)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (11)University of Minnesota Twin Cities, Department of Computer Science/Engineering, Minneapolis, MN, United States
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