Identification of malting barley varieties using computer image analysis and artificial neural networks

2012 ◽  
Author(s):  
K. Nowakowski ◽  
P. Boniecki ◽  
R. J. Tomczak ◽  
S. Kujawa ◽  
B. Raba
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Artificial Neural Networks ◽  
Computer Image ◽  
Malting Barley ◽  
Computer Image Analysis ◽  
Artificial Neural

scholarly journals Artificial neural networks and computer image analysis in the evaluation of selected quality parameters of pea seeds

2019 ◽  
Vol 132 ◽  
pp. 01027 ◽  
Author(s):  
Katarzyna Szwedziak
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Artificial Neural Networks ◽  
Computer Image ◽  
Good Method ◽  
Quality Parameters ◽  
Computer Image Analysis ◽  
Pea Seeds ◽  
Artificial Neural

The aim of the study was to develop an innovative method of modelling the process of evaluating the quality of agricultural crops on the basis of computer image analysis and artificial neural networks (ANN). It was therefore assumed that on the basis of the prepared application for processing and analysing the acquired digital images, based on the RGB colour recognition model, a quick and good method of assessing the quality of products would be obtained. An experiment was conducted on the evaluation of selected parameters of pea seeds quality using computer image analysis and the obtained results were verified by artificial neural networks using the geostatic function.

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

2020 ◽  
Vol 10 (16) ◽  
pp. 5721
Author(s):  
Katarzyna Szwedziak ◽  
Żaneta Grzywacz ◽  
Ewa Polańczyk ◽  
Piotr Bębenek ◽  
Marian Olejnik
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Artificial Neural Networks ◽  
Computer Image ◽  
Investment Project ◽  
Evaluation Process ◽  
Computer Application ◽  
Stored Grain ◽  
Computer Image Analysis ◽  
Artificial Neural

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.

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