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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #372851

Research Project: Science and Technologies for the Sustainable Management of Western Rangeland Systems

Location: Range Management Research

Title: Scaling up agricultural research with artificial intelligence

Author
item Bestelmeyer, Brandon
item MARCILLO, GUILLERMO - Iowa State University
item McCord, Sarah
item Mirsky, Steven
item Moglen, Glenn
item Neven, Lisa
item Peters, Debra - Deb
item Sohoulande Djebou, Dagbegnon
item Wakie, Tewodros

Submitted to: IEEE IT Professional
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/29/2020
Publication Date: 5/1/2020
Citation: Bestelmeyer, B.T., Marcillo, G., McCord, S.E., Mirsky, S.B., Moglen, G.E., Neven, L.G., Peters, D.C., Sohoulande Djebou, D.C., Wakie, T. 2020. Scaling up agricultural research with artificial intelligence. IEEE IT Professional. 22:32-38.

Interpretive Summary: Agricultural systems are enormously variable in space and time. New and developing artificial intelligence (AI)-based tools can leverage site-based science and big data to help farmers and land managers make site-specific decisions. These tools are improving information about soils and vegetation that forms the basis for investments in management actions, provides early warning of pest and disease outbreaks, and facilitates the selection of sustainable cropland management practices. Continued progress with AI will require more observational data across a wide range of agricultural settings, over long time periods.

Technical Abstract: Agricultural systems are enormously variable in space and time. New and developing artificial intelligence (AI)-based tools can leverage site-based science and big data to help farmers and land managers make site-specific decisions. These tools are improving information about soils and vegetation that forms the basis for investments in management actions, provides early warning of pest and disease outbreaks, and facilitates the selection of sustainable cropland management practices. Continued progress with AI will require more observational data across a wide range of agricultural settings, over long time periods.