Artificial Intelligence (AI) For Crop Yield Forecasting
, , Clyde Fraisse, Yiannis Ampatzidis, Sandra Guzmán, Won Suk Lee, Sanjay Shukla, andZiwen Yu
Release Date: 3/19/2026
Agriculture
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.
About this Publication
This document is AE571, one of a series of the Department of Agricultural and Biological Engineering, UF/IFAS Extension. Original publication date April 2022. Revised October 2025. Visit the EDIS website at https://edis.ifas.ufl.edu for the currently supported version of this publication.
How to Cite
Fraisse, C., Ampatzidis, Y., Guzmán, S., Lee, W., Martinez, C., Shukla, S., Singh, A., & Yu, Z. (2022). Artificial Intelligence (AI) for Crop Yield Forecasting. EDIS, 2022(2). https://doi.org/10.32473/edis-ae571-2022






