IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i4p813-d1113340.html
   My bibliography  Save this article

Yield Prediction for Winter Wheat with Machine Learning Models Using Sentinel-1, Topography, and Weather Data

Author

Listed:
  • Oliver Persson Bogdanovski

    (Department of Mathematics, Faculty of Science, Lund University, 221 00 Lund, Sweden
    These authors contributed equally to this work. This paper is a part of their Master Thesis presented at Lund University (Sweden).)

  • Christoffer Svenningsson

    (Department of Mathematics, Faculty of Science, Lund University, 221 00 Lund, Sweden
    These authors contributed equally to this work. This paper is a part of their Master Thesis presented at Lund University (Sweden).)

  • Simon Månsson

    (Niftitech AB, Hedvig Möllers gata 12, 223 55 Lund, Sweden)

  • Andreas Oxenstierna

    (T-Kartor AB, Olof Mohlins väg 12, 291 62 Kristianstad, Sweden)

  • Alexandros Sopasakis

    (Department of Mathematics, Faculty of Science, Lund University, 221 00 Lund, Sweden)

Abstract

We train and compare the performance of two different machine learning algorithms to learn changes in winter wheat production for fields from the southwest of Sweden. As input to these algorithms, we use cloud-penetrating Sentinel-1 polarimetry radar data together with respective field topography and local weather over four different years. We note that all of the input data were freely available. During training, we used information on winter wheat production over the fields of interest which was available from participating farmers. The two machine learning models we trained are the Light Gradient-Boosting Machine and a Feed-forward Neural Network. Our results show that Sentinel-1 data contain valuable information which can be used for training to predict winter wheat yield once two important steps are taken: performing a critical transformation of each pixel in the images to align it to the training data and then following up with despeckling treatment. Using this approach, we were able to achieve a top root mean square error of 0.75 tons per hectare and a top accuracy of 86% using a k-fold method with k = 5 . More importantly, however, we established that Sentinel-1 data alone are sufficient to predict yield with an average root mean square error of 0.89 tons per hectare, making this method feasible to employ worldwide.

Suggested Citation

  • Oliver Persson Bogdanovski & Christoffer Svenningsson & Simon Månsson & Andreas Oxenstierna & Alexandros Sopasakis, 2023. "Yield Prediction for Winter Wheat with Machine Learning Models Using Sentinel-1, Topography, and Weather Data," Agriculture, MDPI, vol. 13(4), pages 1-19, March.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:4:p:813-:d:1113340
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/4/813/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/13/4/813/
    Download Restriction: no
    ---><---

    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:jagris:v:13:y:2023:i:4:p:813-:d:1113340. 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.