Harnessing AI to transform agriculture and inform agricultural research

Authors: PETERS, DEBRA; Rivers, Adam; HATFIELD, JERRY; Lemay, Danielle; Liu, Simon; BASSO, BRUNO - MICHIGAN STATE UNIVERSITY

Submitted to: IEEE IT Professional
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/29/2020
Publication Date: 5/1/2020
Citation: Peters, D.C., Rivers, A.R., Hatfield, J.L., Lemay, D.G., Liu, S.Y., Basso, B. 2020. Harnessing AI to transform agriculture and inform agricultural research. IEEE IT Professional. 22(3):16-21. https://doi.org/10.1109/MITP.2020.2986124.
DOI: https://doi.org/10.1109/MITP.2020.2986124

Interpretive Summary: We provide an overview of the special issue on current advances, challenges, and opportunities for AI technologies in agriculture. We illustrate the potential of AI using four major components of the food system: production; distribution, consumption, and uncertainty. We recognize that the transformation of agriculture will require new tools to more precisely manage fields to increase production while minimizing the environmental risk to water and air quality. Combining AI with other technologies will be needed to provide effective production management strategies for a given combination of soil, climate, pest complexes, and vegetation. New methods will be needed to determine production limitations, and effective management options. The agricultural enterprise is prime for the use of AI and other technologies if they can be adapted for the unique characteristics of agro-ecosystems, including variability and directional changes in climate and other global change drivers as well as novel management and policy decisions, and economic market volatility.

Technical Abstract: We provide an overview of the special issue on current advances, challenges, and opportunities for AI technologies in agriculture. We illustrate the potential of AI using four major components of the food system: production; distribution, consumption, and uncertainty. We recognize that the transformation of agriculture will require new tools to more precisely manage fields to increase production while minimizing the environmental risk to water and air quality. Combining AI with other technologies will be needed to provide effective production management strategies for a given combination of soil, climate, pest complexes, and vegetation. New methods will be needed to determine production limitations, and effective management options. The agricultural enterprise is prime for the use of AI and other technologies if they can be adapted for the unique characteristics of agro-ecosystems, including variability and directional changes in climate and other global change drivers as well as novel management and policy decisions, and economic market volatility.