IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v10y2020i11p567-d449049.html
   My bibliography  Save this article

Application of Artificial Neural Networks to Assess the Mycological State of Bulk Stored Rapeseeds

Author

Listed:
  • Jolanta Wawrzyniak

    (Food Engineering Group, Department of Technology of Plant Origin Food, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, ul. Wojska Polskiego 31, 60-624 Poznań, Poland)

Abstract

Artificial neural networks (ANNs) constitute a promising modeling approach that may be used in control systems for postharvest preservation and storage processes. The study investigated the ability of multilayer perceptron and radial-basis function ANNs to predict fungal population levels in bulk stored rapeseeds with various temperatures (T = 12–30 °C) and water activity in seeds (a w = 0.75–0.90). The neural network model input included a w , temperature, and time, whilst the fungal population level was the model output. During the model construction, networks with a different number of hidden layer neurons and different configurations of activation functions in neurons of the hidden and output layers were examined. The best architecture was the multilayer perceptron ANN, in which the hyperbolic tangent function acted as an activation function in the hidden layer neurons, while the linear function was the activation function in the output layer neuron. The developed structure exhibits high prediction accuracy and high generalization capability. The model provided in the research may be readily incorporated into control systems for postharvest rapeseed preservation and storage as a support tool, which based on easily measurable on-line parameters can estimate the risk of fungal development and thus mycotoxin accumulation.

Suggested Citation

  • Jolanta Wawrzyniak, 2020. "Application of Artificial Neural Networks to Assess the Mycological State of Bulk Stored Rapeseeds," Agriculture, MDPI, vol. 10(11), pages 1-19, November.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:11:p:567-:d:449049
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/10/11/567/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/10/11/567/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kelvin López-Aguilar & Adalberto Benavides-Mendoza & Susana González-Morales & Antonio Juárez-Maldonado & Pamela Chiñas-Sánchez & Alvaro Morelos-Moreno, 2020. "Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter," Agriculture, MDPI, vol. 10(4), pages 1-14, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kazuya Maeda & Dong-Hyuk Ahn, 2021. "Estimation of Dry Matter Production and Yield Prediction in Greenhouse Cucumber without Destructive Measurements," Agriculture, MDPI, vol. 11(12), pages 1-10, November.
    2. Wang, Rong & Sun, Zhaojun & Yang, Dongyan & Ma, Ling, 2022. "Simulating cucumber plant heights using optimized growth functions driven by water and accumulated temperature in a solar greenhouse," Agricultural Water Management, Elsevier, vol. 259(C).
    3. Blaud, Pierre Clement & Haurant, Pierrick & Chevrel, Philippe & Claveau, Fabien & Mouraud, Anthony, 2023. "Multi-flow optimization of a greenhouse system: A hierarchical control approach," Applied Energy, Elsevier, vol. 351(C).
    4. Elzbieta Czembor & Zygmunt Kaczmarek & Wiesław Pilarczyk & Dariusz Mańkowski & Jerzy H. Czembor, 2022. "Simulating Spring Barley Yield under Moderate Input Management System in Poland," Agriculture, MDPI, vol. 12(8), pages 1-20, July.

    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:10:y:2020:i:11:p:567-:d:449049. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.