IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i6p747-d823752.html
   My bibliography  Save this article

Nondestructive Detection Method for the Calcium and Nitrogen Content of Living Plants Based on Convolutional Neural Networks (CNN) Using Multispectral Images

Author

Listed:
  • Grzegorz Kunstman

    (Active Text, 30-519 Krakow, Poland)

  • Paweł Kunstman

    (Active Text, 30-519 Krakow, Poland)

  • Łukasz Lasyk

    (Active Text, 30-519 Krakow, Poland)

  • Jacek Stanisław Nowak

    (The National Institute of Horticultural Research, 96-100 Skierniewice, Poland)

  • Agnieszka Stępowska

    (The National Institute of Horticultural Research, 96-100 Skierniewice, Poland)

  • Waldemar Kowalczyk

    (The National Institute of Horticultural Research, 96-100 Skierniewice, Poland)

  • Jakub Dybaś

    (Jagiellonian Centre for Experimental Therapeutics, Jagiellonian University, 30-348 Krakow, Poland)

  • Ewa Szczęsny-Małysiak

    (Jagiellonian Centre for Experimental Therapeutics, Jagiellonian University, 30-348 Krakow, Poland)

Abstract

Herein, we present the novel method targeted for determination of plant nutritional state with the use of computer vision and Neural Networks. The method is based on multispectral imaging performed by an exclusively designed Agroscanner and a dedicated analytical system for further data analysis with Neural Networks. An Agroscanner is a low-cost mobile construction intended for multispectral measurements at macro-scale, operating at four wavelengths: 470, 550, 640 and 850 nm. Together with developed software and implementation of a Neural Network it was possible to design a unique approach to process acquired plant images and assess information about plant physiological state. The novelty of the developed technology is focused on the multispectral, macro-scale analysis of individual plant leaves, rather than entire fields. Such an approach makes the method highly sensitive and precise. The method presented herein determines the basic physiological deficiencies of crops with around 80% efficiency.

Suggested Citation

  • Grzegorz Kunstman & Paweł Kunstman & Łukasz Lasyk & Jacek Stanisław Nowak & Agnieszka Stępowska & Waldemar Kowalczyk & Jakub Dybaś & Ewa Szczęsny-Małysiak, 2022. "Nondestructive Detection Method for the Calcium and Nitrogen Content of Living Plants Based on Convolutional Neural Networks (CNN) Using Multispectral Images," Agriculture, MDPI, vol. 12(6), pages 1-15, May.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:6:p:747-:d:823752
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/6/747/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/6/747/
    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:12:y:2022:i:6:p:747-:d:823752. 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.