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Machine Learning Models for Corn Yield Prediction A Survey of Literature

Author

Listed:
  • Mohsen Shahhosseini
  • Guiping Hu

    (Department of Industrial and Manufacturing Systems Engineering, Iowa State University, USA)

Abstract

The ability to predict crop yields enables the timely and effective decision making for crop management, and regional agriculture system planning. The field crop corn is the largest crop in the U.S. and hence significant efforts have been devoted to predicting corn yields through various means. The present survey reviews the studies that used machine learning models and their variations to predict corn yield.

Suggested Citation

  • Mohsen Shahhosseini & Guiping Hu, 2020. "Machine Learning Models for Corn Yield Prediction A Survey of Literature," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 25(3), pages 80-83, July.
    • Handle: RePEc:adp:ijesnr:v:25:y:2020:i:3:p:80-83
    DOI: 10.19080/IJESNR.2020.25.556161
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    References listed on IDEAS

    as
    1. Jig Han Jeong & Jonathan P Resop & Nathaniel D Mueller & David H Fleisher & Kyungdahm Yun & Ethan E Butler & Dennis J Timlin & Kyo-Moon Shim & James S Gerber & Vangimalla R Reddy & Soo-Hyung Kim, 2016. "Random Forests for Global and Regional Crop Yield Predictions," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-15, June.
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    More about this item

    Keywords

    earth and environment journals; environment journals; open access environment journals; peer reviewed environmental journals; open access; juniper publishers; ournal of Environmental Sciences; juniper publishers journals ; juniper publishers reivew;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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