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

Agnieszka A. Pilarska, Piotr Boniecki, Małgorzata Idzior-Haufa, Maciej Zaborowicz, Krzysztof Pilarski, Andrzej Przybylak and Hanna Piekarska-Boniecka
Additional contact information
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

Agriculture, 2021, vol. 11, issue 8, 1-11

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.

Keywords: malting barley; variety classification; neural processing of image; artificial intelligence methods (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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