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Neural Visual Detection of Grain Weevil ( Sitophilus granarius L.)

Author

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  • Piotr Boniecki

    (Institute of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-625 Poznan, Poland)

  • Krzysztof Koszela

    (Institute of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-625 Poznan, Poland)

  • Krzysztof Świerczyński

    (Institute of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-625 Poznan, Poland)

  • Jacek Skwarcz

    (Faculty of Production Engineering, Lublin University of Life Sciences, 20-950 Lublin, Poland)

  • Maciej Zaborowicz

    (Institute of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-625 Poznan, Poland)

  • Jacek Przybył

    (Institute of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-625 Poznan, Poland)

Abstract

A significant part of cereal production is intended for agri-food processing, which implies a necessity to search for and implement modern storage systems for this product. Stored grain is exposed to many unfavorable factors, particularly caryopsis macro-damage caused mainly by grain weevil ( Sitophilus granarius L.). This triggers a substantial decrease in the value of the stored material, thus resulting in serious economic losses. Due to this fact, it is necessary to take steps to effectively detect this pest’s presence when grain is delivered to storage facilities. The purpose of this work was to identify the representative physical characteristics of wheat caryopsis affected by grain weevil. An automated visual system was developed to ease the detection of damaged kernels and adult weevils. In order to obtain the empirical data, a decision was made to take advance of SKCS 4100 (the Perten Single Kernel Characterization System). The measurements obtained were used to build the training sets necessary in the process of ANN (artificial neural network) learning with digital neural classifiers. Next, a set of identifying neural models was created and verified, and then the optimal topology was selected. The utilitarian goal of the research was to support the decision-making process taking place during grain storage.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:1:p:25-:d:310924
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    References listed on IDEAS

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    1. Jun Sun & Xiaofei He & Xiao Ge & Xiaohong Wu & Jifeng Shen & Yingying Song, 2018. "Detection of Key Organs in Tomato Based on Deep Migration Learning in a Complex Background," Agriculture, MDPI, vol. 8(12), pages 1-15, December.
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    Cited by:

    1. Bożena Kordan & Mariusz Nietupski & Emilia Ludwiczak & Beata Gabryś & Robert Cabaj, 2023. "Selected Cultivar-Specific Parameters of Wheat Grain as Factors Influencing Intensity of Development of Grain Weevil Sitophilus granarius (L.)," Agriculture, MDPI, vol. 13(8), pages 1-13, July.
    2. Andrzej Przybylak & Radosław Kozłowski & Ewa Osuch & Andrzej Osuch & Piotr Rybacki & Przemysław Przygodziński, 2020. "Quality Evaluation of Potato Tubers Using Neural Image Analysis Method," Agriculture, MDPI, vol. 10(4), pages 1-11, April.
    3. Piotr Boniecki & Maciej Zaborowicz & Agnieszka Pilarska & Hanna Piekarska-Boniecka, 2020. "Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN," Agriculture, MDPI, vol. 10(6), pages 1-9, June.
    4. Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
    5. 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.

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