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Article

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

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Department of Plant-Derived Food Technology, Poznań University of Life Sciences, ul. Wojska Polskiego 31, 60-624 Poznan, Poland
2
Department of Biosystems Engineering, Poznan University of Life Sciences, ul. Wojska Polskiego 50, 60-627 Poznan, Poland
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Department of Gerodontology and Oral Pathology, Poznan University of Medical Sciences, ul. Bukowska 70, 60-812 Poznan, Poland
4
Faculty of Horticulture and Landscape Architecture, Poznan University of Life Sciences, 60-637 Poznan, Poland
*
Author to whom correspondence should be addressed.
Academic Editor: Francesco Marinello
Received: 5 June 2021 / Revised: 20 July 2021 / Accepted: 30 July 2021 / Published: 1 August 2021
(This article belongs to the Special Issue Latest Advances for Smart and Sustainable Agriculture)
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. View Full-Text
Keywords: malting barley; variety classification; neural processing of image; artificial intelligence methods malting barley; variety classification; neural processing of image; artificial intelligence methods
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MDPI and ACS Style

Pilarska, A.A.; Boniecki, P.; Idzior-Haufa, M.; Zaborowicz, M.; Pilarski, K.; Przybylak, A.; Piekarska-Boniecka, H. Image Analysis Methods in Classifying Selected Malting Barley Varieties by Neural Modelling. Agriculture 2021, 11, 732. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11080732

AMA Style

Pilarska AA, Boniecki P, Idzior-Haufa M, Zaborowicz M, Pilarski K, Przybylak A, Piekarska-Boniecka H. Image Analysis Methods in Classifying Selected Malting Barley Varieties by Neural Modelling. Agriculture. 2021; 11(8):732. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11080732

Chicago/Turabian Style

Pilarska, Agnieszka A., Piotr Boniecki, Małgorzata Idzior-Haufa, Maciej Zaborowicz, Krzysztof Pilarski, Andrzej Przybylak, and Hanna Piekarska-Boniecka. 2021. "Image Analysis Methods in Classifying Selected Malting Barley Varieties by Neural Modelling" Agriculture 11, no. 8: 732. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11080732

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