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Application of Artificial Neural Networks to Analyze the Concentration of Ferulic Acid, Deoxynivalenol, and Nivalenol in Winter Wheat Grain

Author

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  • Gniewko Niedbała

    (Institute of Biosystems Engineering, Faculty of Agronomy and Bioengineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland)

  • Danuta Kurasiak-Popowska

    (Department of Genetics and Plant Breeding, Faculty of Agronomy and Bioengineering, Poznan University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland)

  • Kinga Stuper-Szablewska

    (Department of Chemistry, Faculty of Wood Technology, Poznan University of Life Sciences, Wojska Polskiego 75, 60-625 Poznań, Poland)

  • Jerzy Nawracała

    (Department of Genetics and Plant Breeding, Faculty of Agronomy and Bioengineering, Poznan University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland)

Abstract

Biotic stress, which includes infection by pathogenic fungi, causes losses of wheat yield in terms of quantity and quality. Ear Fusarium is caused by strains of F. graminearum and F. culmorum , which can produce mycotoxins—deoxynivalenol (DON) and nivalenol (NIV). One of the wheat’s defense mechanisms against stressors is the activation of biosynthesis pathways of antioxidant compounds, including ferulic acid. The aim of the study was to conduct pilot studies on the basis of which neural models were created that would examine the impact of the variety and weather conditions on the concentration of ferulic acid, and link its content with the concentration of deoxynivalenol and nivalenol. The plant material was 23 winter wheat genotypes with different Fusarium resistance. The field experiment was conducted in 2011–2013 in Poland in three experimental combinations, namely: with full chemical protection; without chemical protection, but infested with natural disease (control); and in the absence of fungicidal protection, with artificial inoculation by genus Fusarium fungi. As a result of the pilot studies, three neural models—FERUANN analytical models (ferulic acid content), DONANN (deoxynivalenol content) and NIVANN (nivalenol content)—were produced. Each model was based on 14 independent features, 12 of which were in the form of quantitative data, and the other two were presented as qualitative data. The structure of the created models was based on an artificial neural network (ANN) of the multilayer perceptron (MLP) with two hidden layers. The sensitivity analysis of the neural network showed the two most important features determining the concentration of ferulic acid, deoxynivalenol, and nivalenol in winter wheat seeds. These are the experiment variant (VAR) and winter wheat variety (VOW).

Suggested Citation

  • Gniewko Niedbała & Danuta Kurasiak-Popowska & Kinga Stuper-Szablewska & Jerzy Nawracała, 2020. "Application of Artificial Neural Networks to Analyze the Concentration of Ferulic Acid, Deoxynivalenol, and Nivalenol in Winter Wheat Grain," Agriculture, MDPI, vol. 10(4), pages 1-12, April.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:4:p:127-:d:345254
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    References listed on IDEAS

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    1. Piotr Boniecki & Krzysztof Koszela & Krzysztof Świerczyński & Jacek Skwarcz & Maciej Zaborowicz & Jacek Przybył, 2020. "Neural Visual Detection of Grain Weevil ( Sitophilus granarius L.)," Agriculture, MDPI, vol. 10(1), pages 1-9, January.
    2. Ga-Ae Ryu & Aziz Nasridinov & HyungChul Rah & Kwan-Hee Yoo, 2020. "Forecasts of the Amount Purchase Pork Meat by Using Structured and Unstructured Big Data," Agriculture, MDPI, vol. 10(1), pages 1-14, January.
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    Cited by:

    1. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2022. "Prediction of Protein Content in Pea ( Pisum sativum L.) Seeds Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    2. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2021. "Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data," Land, MDPI, vol. 10(6), pages 1-21, June.
    3. Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
    4. Katarzyna Szwedziak & Ewa Polańczyk & Żaneta Grzywacz & Gniewko Niedbała & Wiktoria Wojtkiewicz, 2020. "Neural Modeling of the Distribution of Protein, Water and Gluten in Wheat Grains during Storage," Sustainability, MDPI, vol. 12(12), pages 1-14, June.
    5. Mohsen Niazian & Gniewko Niedbała, 2020. "Machine Learning for Plant Breeding and Biotechnology," Agriculture, MDPI, vol. 10(10), pages 1-23, September.
    6. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2023. "Prediction of Pea ( Pisum sativum L.) Seeds Yield Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(3), pages 1-19, March.

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