## 3 Results and Discussion
The authors stated that the neural models (MAPE < 2%) were of better accuracy than the RSM model (MAPE > 10%). [PERSON] et al. [47] employed Fourier transform infrared spectroscopy and the partial least squares regression (PLS) method to predict the stability and maturity of compost-based substrates. The accuracy of the prediction depended on the maturity index. The coefficient of determination (R\({}^{2}\)) between the experimental and predicted values for the test dataset varied from 0.55 for the NH\({}_{4}\)\({}^{+}\)/NO\({}_{3}\)\({}^{-}\) ratio to 0.92 for the degree of polymerisation. The same techniques were used by [PERSON] et al. [48] to determine the humic acids content in composts. For all of the PLS models developed in this research, the R\({}^{2}\) between the experimental and predicted values exceeded 0.8. Figure 4: Results of the linear regression (**a**) and error histogram (**b**) for the test dataset and the MLP 33-10-1 VS10 model for the prediction of the volatile solids content. Figure 3: Results of the linear regression (**a**) and error histogram (**b**) for the test dataset and the MLP 33-10-1 TS10 model for the prediction of the total solids content. The topic of determining the total solids and volatile solids content in compost material using computer image analysis and neural modelling methods is very rare and, therefore, innovative. It has only been analysed in a few publications. Such research was conducted by [PERSON] et al. [38] and [PERSON] et al. [39]. They developed neural networks to predict the values of these parameters for composted mixtures of sewage sludge with maize, rape, and wheat straw. The model for determining the total solids content from compost images acquired under visible light presented in their report had an RMSE error of 0.0922. The model for the prediction of the volatile solids content was less accurate, with an RMSE error of 0.1722. The neural models presented in this work are described by a higher RMSE error. However, they were developed on the basis of a significantly larger dataset (1536 independent learning cases vs. 84 learning cases). Consequently, the predictive models presented in this work are characterised by a much higher generalisability and lower recall. The uncertainties of these models, expressed by the MAPE error, were 2.88% and 0.59%, respectively, for the prediction of the total solids and volatile solids content. These results may be considered very satisfactory. Changes in the total solids and volatile solids content are crucial for monitoring a composting process [49]. The total solids content is a parameter that is subject to change during the process. This is due, among other things, to the evaporation of significant quantities of water contained in the initial mixture of substrates prepared for the composting. The analysis of changes in the volatile solids content provides information about the correct course of the composting process. This is due to the fact that some of the volatile solids contained in the substrate mixture decompose over time. Classical methods for the determination of total solids and volatile solids content are based on sampling from a reactor or pile and determining these parameters using drying and combustion processes. The disadvantage of these methods is that more than one day is needed to produce the results. The method proposed in this article is less invasive than the classic sampling of the composted mixture from the chambers. The parameters are determined on the basis of the photographs taken, and there is no need to irretrievably remove a portion of the compost from the reactor that will no longer be returned to it.