IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v14y2021i9p397-d621181.html
   My bibliography  Save this article

Stochastic Analysis and Neural Network-Based Yield Prediction with Precision Agriculture

Author

Listed:
  • Humayra Shoshi

    (Department of Mathematics, North Dakota State University, Fargo, ND 58108-6050, USA)

  • Erik Hanson

    (Department of Agribusiness and Applied Economics, North Dakota State University, Fargo, ND 58108-6050, USA)

  • William Nganje

    (Department of Agribusiness and Applied Economics, North Dakota State University, Fargo, ND 58108-6050, USA)

  • Indranil SenGupta

    (Department of Mathematics, North Dakota State University, Fargo, ND 58108-6050, USA)

Abstract

In this paper, we propose a general mathematical model for analyzing yield data. The data analyzed in this paper come from a characteristic corn field in the upper midwestern United States. We derive expressions for statistical moments from the underlying stochastic model. Consequently, we illustrate how a particular feature variable contributes to the statistical moments (and in effect, the characteristic function) of the target variable (i.e., yield). We also analyze the data with neural network techniques and provide two methods of data analysis. This mathematical model and neural network-based data analysis allow for better understanding of the variability within the data set, which is useful to farm managers attempting to make current and future decisions using the yield data. Lenders and risk management consultants may benefit from the insights of this mathematical model and neural network-based data analysis regarding yield expectations.

Suggested Citation

  • Humayra Shoshi & Erik Hanson & William Nganje & Indranil SenGupta, 2021. "Stochastic Analysis and Neural Network-Based Yield Prediction with Precision Agriculture," JRFM, MDPI, vol. 14(9), pages 1-17, August.
  • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:9:p:397-:d:621181
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/14/9/397/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1911-8074/14/9/397/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Minglian Lin & Indranil SenGupta & William Wilson, 2023. "Estimation of VaR with jump process: application in corn and soybean markets," Papers 2311.00832, arXiv.org, revised Jun 2024.
    2. Humayra Shoshi & Indranil SenGupta, 2023. "Some asymptotics for short maturity Asian options," Papers 2302.05421, arXiv.org, revised Sep 2024.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:14:y:2021:i:9:p:397-:d:621181. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.