## 5 Analysis and Discussion of Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Type** & **MLP** & **Linear** & **RBF** & **RBF* & **MLP** \\ \hline Correlation coefficient & 0.98 & 0.99 & 0.98 & 0.95 & 0.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Correlation Coefficient for Individual Types of Generated Neural Networks [Own Source]. Figure 7: The graph of fitting of empirical data of percentage of contaminants in wheat seeds obtained by means of image analysis with empirical data obtained by traditional method (by weight—sieve) [own source]. showed the correct classification of over 90% for all types of grain [18]. [PERSON]; [PERSON]; [PERSON]; [PERSON] (2007) used image analysis to classify rapeseed. The color, which was determined using the _HSV_ model, and the shape of seeds were used for evaluation [13]. [PERSON]; [PERSON]; [PERSON] (1999) used digital image analysis based on the _RGB_ model to evaluate and classify samples of wheat, barley, oats, and rye. The accuracy of the applied method was 98.6% [23]. [PERSON]; [PERSON]; [PERSON]; [PERSON] used digital image analysis for volumetric classification of oil and pulses seeds [24]. [PERSON]; [PERSON] (2006) used image analysis to assess the texture of breadcrumbs. The research showed that the texture of breadcrumbs varies according to the type of flour from which the bread was baked. The results were 93% correct [25]. [PERSON]; [PERSON]; [PERSON]; [PERSON]. (2005) used image analysis and neural networks to classify rice varieties based on morphological characteristics and color [26]. [PERSON] (2005) and [PERSON]; [PERSON]; [PERSON] (2005) used neural networks to estimate the relative humidity of atmospheric air based on its temperature. In earlier studies [PERSON] (2019) conducted the evaluation of selected parameters of pea quality using computer image analysis and the obtained results were verified by artificial neural networks using the geostatic function. The obtained results enabled the creation of logically and experimentally developed and tested neural models for the evaluation of pea contamination distribution and confirmed that it is advisable to use them on the basis of color characteristics obtained from the created image analysis software. The application of K function allowed the observation and statistical identification of regularity of contaminant distribution in pea seed mass. The results of grain quality computer analysis indicate that the adopted direction of research is promising. Similar tests were carried out for maize grain but the biggest problem in examining the contaminated fraction was the relatively low quality of the photos resulting from the adopted methodology according to which the photos for this examination concerned a collective sample placed on a Petri dish. In developing the method another solution should be adopted, in particular one in which the sample to be acquired contains at most one layer of grain placed on a contrasting background (preferably blue R0G0B255). Irrespective of these difficulties, a relatively small scatter of the measured values could be observed for each of the grain types, i.e., maize [14,15]. [PERSON], [PERSON] et al. (2020) basis of the collected data, a model artificial neural network (ANN) MLP 52-6-3 was created, which, with the use of four independent features, allows us to determine changes in the content of water, protein and gluten in stored wheat. The chosen network returned good error values: learning, below 0.001; testing, 0.015; and validation, 0.008. The obtained results and their interpretation are an important element in the warehouse industry [27]. The research allowed improving the research methodology used in the grain warehouse and optimize management, achieving savings resulting from the time necessary for goods receipt and further production process.