## 3 Results and Discussion  
The information on the neural models developed to determine the total solids content in the composted material is presented in Table 4. The MSE error of the models trained only on the basis of some of the image parameters was in a range from 20.346 to 52.211 for the test dataset. The MLP 25-10-1 TS5 model, considering all colour parameters, was the one with \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Name of** & **Name of** & **Number of** & \\  & **Dataset for TS** & **Dataset for VS** & **Parameters** & **Description** \\ \hline
1 & TS1 & VS1 & 9 & colour parameters for the RGB \\
2 & TS2 & VS2 & 3 & colour parameters for greyscale only \\
3 & TS3 & VS3 & 4 & colour parameters for binary scale only \\
4 & TS4 & VS4 & 9 & colour parameters for HSV model only \\
5 & TS5 & VS5 & 25 & all colour parameters \\
6 & TS6 & VS6 & 5 & texture parameters only \\
7 & TS7 & VS7 & 30 & all image parameters \\
8 & TS8 & VS8 & 31 & all image parameters + SLUD\_PER a  \\
9 & TS9 & VS9 & 32 & all image parameters + SLUD\_PER a  \\
10 & TS10 & VS10 & 33 & all image parameters + SLUD\_PER a  \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Input parameters used in the datasets. the smallest error. The MSE error of the MLP 5-10-1 TS6 model, which was based on the texture parameters, was equal to 42.737. In the case of the MLP 30-10-1 TS7 model trained with the dataset containing all image parameters, the MSE error was 19.359. Additional information on the composition of the composted material (percentage of sludge and type of straw) and the maturity stage of the compost at the input of the network had a significant effect on improving the prediction of the total solids content. This is probably due to the differences in the appearance of the composted material with different proportions of substrates used, both during intensive composite and after reaching the early maturity stage. The best accuracy was observed for the MLP 33-10-1 TS10 network, developed based on all of the image parameters and three additional features of the material analysed. The MSE error of this network was 2.470. Detailed information on the models developed for the prediction of volatile solids content in the composted material is presented in Table 5. In the case of the models trained with the use of the selected image parameters, the MSE error was in a range of 8.582-14.418. The smallest MSE error in this group of models was observed for MLP 25-10-1 VS5, trained based on all colour features. The MSE error of the model developed on the basis of texture parameters (MLP 5-10-1 VS6) was equal to 13.308. The accuracy of the MLP 30-10-1 VS7 model trained based on all image parameters was lower than the accuracy of the model developed with the use of only colour features, which was unexpected. The use of parameters describing the composition of the composted material and the maturity stage of the compost as additional inputs to the model significantly improved the prediction of the volatile solids content. A similar phenomenon was observed for the models of the total solids content in the composted material. The lowest error of 0.494 was calculated for the MLP 33-10-1 VS10 model, developed taking into account all image parameters and three additional features describing the material. MLP 33-10-1 TS10 and MLP 33-10-1 VS10 proved to be the best models for determining the total solids and volatile solids content in the composted mixture of sewage sludge and maize straw, respectively. The high accuracy of both neural models as predictive tools is confirmed by the regression issue statistics and error values (Tables 6 and 7). The linear regression between the values of the total solids content predicted by the MLP 33-10-1 TS10 model and the experimental values of this parameter for the test dataset is depicted in Figure 3. The information on the error in the determination of the analysed parameter is presented in the form of a histogram.