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Optimization of Management Processes in Assessing the Quality of Stored Grain Using Vision Techniques and Artificial Neural Networks

1
Opole University of Technology, Faculty of Production Engineering and Logistic 45-758 Opole, Poland
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Opole University of Technology, Faculty of Economics and Management 45-758 Opole, Poland
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Agropol Spółka Jawna [General Partnership], 49-330 Łosiów, Poland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(16), 5721; https://doi.org/10.3390/app10165721
Received: 18 July 2020 / Revised: 13 August 2020 / Accepted: 14 August 2020 / Published: 18 August 2020
(This article belongs to the Section Computing and Artificial Intelligence)
The paper presents the method of using vision techniques and artificial neural networks to assess the degree of contamination of cereal during grain reception. The aim of the work is to optimize the management of the contaminant evaluation process of grain mass in warehouse and during purchase using vision techniques based on computer image analysis in order to expedite laboratory work. The obtained photographs of wheat seed samples were analyzed using the “Agropol V06” computer application and neural analysis of the obtained empirical results was performed. The application of computer image analysis reduced the time necessary for the quality assessment of the examined material compared to traditional methods. The generated models were characterized by good parameters and high quality, obtaining a high R2 coefficient at the level of 0.999. As part of the investment project, savings resulting from the time of goods receipt and further production process were made. Profitability was estimated at 191.43% per day. The analysis was made without taking into account other costs related to the business activity. The straight payback period is 3 years. View Full-Text
Keywords: management optimization; artificial neural networks; vision techniques; quality assessment; grain storage; grain contamination management optimization; artificial neural networks; vision techniques; quality assessment; grain storage; grain contamination
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Szwedziak, K.; Grzywacz, Ż.; Polańczyk, E.; Bębenek, P.; Olejnik, M. Optimization of Management Processes in Assessing the Quality of Stored Grain Using Vision Techniques and Artificial Neural Networks. Appl. Sci. 2020, 10, 5721.

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