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

• Published in: Applied sciences Vol. 10; no. 16; p. 5721
• Main Authors: Szwedziak, Katarzyna, Grzywacz, Żaneta, Polańczyk, Ewa, Bębenek, Piotr, Olejnik, Marian
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-08-2020
• Subjects: artificial neural networks; Barley; Cereals; Contaminants; Contamination; Economics; Food; Grain; grain contamination; Grain storage; Humidity; Image analysis; Image processing; Laboratories; management optimization; Methods; Microorganisms; Neural networks; Optimization; Process controls; Quality assessment; Quality control; Seeds; Sensors; Vision; vision techniques; Warehouses; Europe
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10165721

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.