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Image Analysis Methods in Classifying Selected Malting Barley Varieties by Neural Modelling

Author

Listed:
  • Agnieszka A. Pilarska

    (Department of Plant-Derived Food Technology, Poznań University of Life Sciences, ul. Wojska Polskiego 31, 60-624 Poznan, Poland)

  • Piotr Boniecki

    (Department of Biosystems Engineering, Poznan University of Life Sciences, ul. Wojska Polskiego 50, 60-627 Poznan, Poland)

  • Małgorzata Idzior-Haufa

    (Department of Gerodontology and Oral Pathology, Poznan University of Medical Sciences, ul. Bukowska 70, 60-812 Poznan, Poland)

  • Maciej Zaborowicz

    (Department of Biosystems Engineering, Poznan University of Life Sciences, ul. Wojska Polskiego 50, 60-627 Poznan, Poland)

  • Krzysztof Pilarski

    (Department of Biosystems Engineering, Poznan University of Life Sciences, ul. Wojska Polskiego 50, 60-627 Poznan, Poland)

  • Andrzej Przybylak

    (Department of Biosystems Engineering, Poznan University of Life Sciences, ul. Wojska Polskiego 50, 60-627 Poznan, Poland)

  • Hanna Piekarska-Boniecka

    (Faculty of Horticulture and Landscape Architecture, Poznan University of Life Sciences, 60-637 Poznan, Poland)

Abstract

Quality evaluation of products is a critical stage in the process of production. It also applies to the production of beer and its main ingredients, i.e., hops, yeast, malting barley and other components. The research described in this paper deals with the multifaceted quality evaluation of malting barley needed for the production of malt. The project aims to elaborate on the original methodology used for identifying grain varieties, grain contamination degree and other visual characteristics of malting barley employing new computer technologies, including artificial intelligence (AI) and neural image analysis. The neural modelling and digital image analysis assist in identifying the quality of barley varieties. According to the study, information concerning the colour of barley varieties presented in digital images is sufficient for this purpose. The multi-layer perceptron (MLP)-type neural network generated using a data set describing the colour of kernels presented in digital images was the best model for recognising the analysed malting barley varieties. The proposed procedure may bring specific benefits to malthouses, influencing the beer production quality in the future.

Suggested Citation

  • Agnieszka A. Pilarska & Piotr Boniecki & Małgorzata Idzior-Haufa & Maciej Zaborowicz & Krzysztof Pilarski & Andrzej Przybylak & Hanna Piekarska-Boniecka, 2021. "Image Analysis Methods in Classifying Selected Malting Barley Varieties by Neural Modelling," Agriculture, MDPI, vol. 11(8), pages 1-11, August.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:8:p:732-:d:606591
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    References listed on IDEAS

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    1. 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.
    2. Agnieszka A. Pilarska & Agnieszka Wolna-Maruwka & Krzysztof Pilarski, 2018. "Kraft Lignin Grafted with Polyvinylpyrrolidone as a Novel Microbial Carrier in Biogas Production," Energies, MDPI, vol. 11(12), pages 1-22, November.
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    1. Sławomir Francik & Bogusława Łapczyńska-Kordon & Norbert Pedryc & Wojciech Szewczyk & Renata Francik & Zbigniew Ślipek, 2022. "The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus," Sustainability, MDPI, vol. 14(5), pages 1-26, March.

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