Abstract
This publication aims to introduce readers to recent crop yield forecasting approaches based on artificial intelligence (AI) and to provide examples of how AI can potentially improve yield forecasting at the field and regional levels. Written by Clyde Fraisse, Yiannis Ampatzidis, Sandra Guzmán, Wonsuk Lee, Christopher Martinez, Sanjay Shukla, Aditya Singh, and Ziwen Yu, and published by the UF/IFAS Department of Agricultural and Biological Engineering, April 2022.
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CITATION STYLE
Fraisse, C., Ampatzidis, Y., Guzmán, S., Lee, W., Martinez, C., Shukla, S., … Yu, Z. (2022). Artificial Intelligence (AI) for Crop Yield Forecasting. EDIS, 2022(2). https://doi.org/10.32473/edis-ae571-2022
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