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Neural image analysis is commonly used to solve scientific problems of biosystems and mechanical engineering. The method has been applied, for example, to assess the quality of foodstuffs such as fruit and vegetables, cereal grains, and meat. The method can also be used to analyse composting processes. The scientific problem lets us formulate the research hypothesis: it is possible to identify representative traits of the image of composted material that are necessary to create a neural model supporting the process of assessment of the content of dry matter and dry organic matter in composted material. The effect of the research is the identification of selected features of the composted material and the methods of neural image analysis resulted in a new original method enabling effective assessment of the content of dry matter and dry organic matter. The content of dry matter and dry organic matter can be analysed by means of parameters specifying the colour of compost. The best developed neural models for the assessment of the content of dry matter and dry organic matter in compost are: in visible light RBF 19:19-2-1:1 (test error 0.0922) and MLP 14:14-14-11-1:1 (test error 0.1722), in mixed light RBF 30:30-8-1:1 (test error 0.0764) and MLP 7:7-9-7-1:1 (test error 0.1795). The neural models generated for the compost images taken in mixed light had better qualitative characteristics.

In the last decade, an increasingly common method of maize stover management is to use it for energy generation, including anaerobic digestion for biogas production. Therefore, the aim of this study was to provide a chemical and structural characterization of maize stover fractions and, based on these parameters, to evaluate the potential application of these fractions, including forbiogas production. In the study, maize stover fractions, including cobs, husks, leaves and stalks, were used. The biomass samples were characterized by infrared spectroscopy (FTIR), X-ray diffraction and analysis of elemental composition. Among all maize stover fractions, stalks showed the highest C:N ratio, degree of crystallinity and cellulose and lignin contents. The high crystallinity index of stalks (38%) is associated with their high cellulose content (44.87%). FTIR analysis showed that the spectrum of maize stalks is characterized by the highest intensity of bands at 1512 cm−1 and 1384 cm−1, which are the characteristic bands of lignin and cellulose. Obtained results indicate that the maize stover fraction has an influence on the chemical and structural parameters. Moreover, presented results indicate that stalks are characterized by the most favorable chemical parameters for biogas production.