IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i10p1779-d1495014.html
   My bibliography  Save this article

Linear Discriminant Analysis for Predicting Net Blotch Severity in Spring Barley with Meteorological Data in Finland

Author

Listed:
  • Outi Ruusunen

    (Control Engineering, Environmental and Chemical Engineering Research Unit, University of Oulu, 90014 Oulu, Finland)

  • Marja Jalli

    (Natural Resources Institute Finland, 31600 Jokioinen, Finland)

  • Lauri Jauhiainen

    (Natural Resources Institute Finland, 31600 Jokioinen, Finland)

  • Mika Ruusunen

    (Control Engineering, Environmental and Chemical Engineering Research Unit, University of Oulu, 90014 Oulu, Finland)

  • Kauko Leiviskä

    (Control Engineering, Environmental and Chemical Engineering Research Unit, University of Oulu, 90014 Oulu, Finland)

Abstract

Predictive information on plant diseases could help to reduce and optimize the usage of pesticides in agriculture. This research presents classification procedures with linear discriminant analysis to predict three possible severity levels of net blotch in spring barley in Finland. The weather data utilized for classification included mathematical transformations, namely features of outdoor temperature and air humidity with calculated dew point temperature values. Historical field observations of net blotch density were utilized as a target class for the identification of classifiers. The performance of classifiers was analyzed in sliding data windows of two weeks with selected, cumulative, summed feature values. According to classification results from 36 yearly data sets, the prediction of net blotch occurrence in spring barley in Finland can be considered as a linearly separable classification task. Furthermore, this can be achieved with linear discriminant analysis by combining the output probabilities of separate binary classifiers identified for each severity level of net blotch disease. In this case, perfect classification with a resolution of three different net blotch severity levels was achieved during the first 50 days from the beginning of the growing season. This strongly suggests that real-time classification based on a few weather variables measured on a daily basis can be applied to estimate the severity of net blotch in advance. This allows application of the principles of integrated pest management (IPM) and usage of pesticides only when there is a proven need.

Suggested Citation

  • Outi Ruusunen & Marja Jalli & Lauri Jauhiainen & Mika Ruusunen & Kauko Leiviskä, 2024. "Linear Discriminant Analysis for Predicting Net Blotch Severity in Spring Barley with Meteorological Data in Finland," Agriculture, MDPI, vol. 14(10), pages 1-18, October.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:10:p:1779-:d:1495014
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/10/1779/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/14/10/1779/
    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:14:y:2024:i:10:p:1779-:d:1495014. 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.