IDEAS home Printed from https://ideas.repec.org/a/caa/jnlrae/v66y2020i1id13-2019-rae.html
   My bibliography  Save this article

Dielectric technique combined with artificial neural network and support vector regression in moisture content prediction of olive

Author

Listed:
  • Mahdi Rashvand

    (Department of Machine Design and Mechatronics, Institute of Mechanics, Iranian Research Organization for Science and Technology, Tehran, Iran)

  • Mahmoud Soltani Firouz

    (Department of Mechanics of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran)

Abstract

Olives are one of the most important agriculture crops in the world, which are harvested in different stages of growth for various uses. One of the ways to detect the adequate time to process the olives is to determine their moisture content. In this study, to determine the moisture content of olives, a dielectric technique was used in seven periods of harvesting and three different varieties of olive including Oily, Mary and Fishemi. The dielectric properties of the olive fruits were measured using an electronic device in the range of 0.1-30 MHz. Artificial Neural Network (ANN) and Support Vector Regression (SVR) methods were applied to develop the prediction models by using the obtained data acquired by the system. The best results (R = 0.999 and MSE = 0.014) were obtained by the ANN model with a topology of 384-12-1 (384 features in the input vector, 12 neurons in the hidden layer and 1 output). The results obtained indicated the acceptable accuracy of the dielectric technique combined with the ANN model.

Suggested Citation

  • Mahdi Rashvand & Mahmoud Soltani Firouz, 2020. "Dielectric technique combined with artificial neural network and support vector regression in moisture content prediction of olive," Research in Agricultural Engineering, Czech Academy of Agricultural Sciences, vol. 66(1), pages 1-7.
    • Handle: RePEc:caa:jnlrae:v:66:y:2020:i:1:id:13-2019-rae
    DOI: 10.17221/13/2019-RAE
    as

    Download full text from publisher

    File URL: http://rae.agriculturejournals.cz/doi/10.17221/13/2019-RAE.html
    Download Restriction: free of charge
    File URL: http://rae.agriculturejournals.cz/doi/10.17221/13/2019-RAE.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.17221/13/2019-RAE?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:caa:jnlrae:v:66:y:2020:i:1:id:13-2019-rae. 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: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

    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.