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 for biogas 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.

Over the last decade, there has been increased interest in applying biomass as a raw material for producing biofuels used for thermochemical conversions. Extensive use of biomass could lead to controversial competition for arable land, water, and food; therefore, only waste materials and agricultural by-products and residues should be used to produce biofuels. One suitable by-product of agricultural production is crop residue from the harvest of maize for grain (corn stover). The harvest residues of corn stover consist of four fractions, i.e., husks, leaves, cobs, and stalks, which are structurally and morphologically distinct. The aim of the study was to determine the effect of selected maize cultivars with distinct FAO (Food and Agriculture Organization of the United Nations) earliness classifications on the chemical and energetic properties of their corn cob cores. We determined the chemical properties based on elemental analysis, and the energy properties based on the heat of combustion and calorific values. The content of ash and volatile compounds in the corn cobs were also determined. The results indicated that the heat of combustion of fresh and seasoned corn cob cores ranged from 7.62–10.79 MJ/kg and 16.19–16.53 MJ/kg, respectively. The heat of combustion and calorific value of corn cob cores in the fresh state differed significantly and were strongly correlated with maize cultivars with distinct FAO earliness.

In relation to the situation caused by the pandemic, which may also take place in the future, there is a need to find effective solutions to improve the economic situation of the floristry industry. The production and sale of flowers is time-consuming and long-term. Therefore, any information that causes the impossibility of selling the plants will result in a reduction of profitability or bankruptcy of such companies. Research on rationally utilizing biowaste from plant cultivation as well as unsold flowers for environmental protection and effective use of their potential as a raw material for bioenergy production were examined in this article. The aim of this study was to analyze the energetic potential of the biodegradable fraction of waste from floriculture. The trials included floricultural waste containing the stems, leaves and flowers of different species and hybrid tulips (Tulipa L.), roses (Rosa L.), sunflowers (Helianthus L.) and chrysanthemums (Dendranthema Des Moul.). Their biogas and methane production as well as heat of combustion were determined experimentally. The calorific value was calculated on the basis of results from selected floricultural waste and its chemical composition. The biogas production was tested on different levels of plant material fragmentation (chaff, macerate) in fermentation processes with two ranges of temperature (meso- and thermophilic fermentation). The presented results show that the highest calorific values were determined for dry stems of roses (18,520 kJ/kg) and sunflowers (18,030 kJ/kg). In turn, the lowest were obtained for dried chrysanthemums and tulips, for which the heating value reached 15,560 kJ/kg and 15,210 kJ/kg. In addition, based on one ton of the fresh mass of biowaste from floriculture, the largest biogas production including the control was obtained from the chrysanthemum chaff by mesophilic anaerobic digestion. Moreover, the largest volume of methane was received by thermophilic anaerobic digestion of roses. The highest content of biomethane (56.68%) was reached by thermophilic fermentation of roses. The energy production of the analyzed substrates was also calculated, based on the amount of biogas produced in the containers for anaerobic digestion. Additionally, a deep neural network model, which predicted the production of methane gas, was created. Owing to the properties of the network, the level of significance of variables used for modelling and prediction of biogas production was determined. The neural modelling process was carried out with the use of the H₂O program.

A significant part of cereal production is intended for agri-food processing, which implies a necessity to search for and implement modern storage systems for this product. Stored grain is exposed to many unfavorable factors, particularly caryopsis macro-damage caused mainly by grain weevil (Sitophilus granarius L.). This triggers a substantial decrease in the value of the stored material, thus resulting in serious economic losses. Due to this fact, it is necessary to take steps to effectively detect this pest’s presence when grain is delivered to storage facilities. The purpose of this work was to identify the representative physical characteristics of wheat caryopsis affected by grain weevil. An automated visual system was developed to ease the detection of damaged kernels and adult weevils. In order to obtain the empirical data, a decision was made to take advance of SKCS 4100 (the Perten Single Kernel Characterization System). The measurements obtained were used to build the training sets necessary in the process of ANN (artificial neural network) learning with digital neural classifiers. Next, a set of identifying neural models was created and verified, and then the optimal topology was selected. The utilitarian goal of the research was to support the decision-making process taking place during grain storage.

The study concentrates on researching possibilities of using computer image analysis and neural modeling in order to assess selected quality discriminants of spray-dried chokeberry powder. The aim of the paper is the quality identification of chokeberry powders on account of their highest dying power, the highest bioactivity, as well as technologically satisfying looseness of the powder. The article presents neural models with vision techniques backed up by devices such as digital cameras, as well as an electron microscope. The reduction in size of input variables with PCA has an influence on improving the processes of learning data sets, thus increasing the effectiveness of identifying chokeberry fruit powders included in digital pictures, which is shown in the results of the conducted research. The effectiveness of image recognition is presented by classifying abilities, as well as low Root Mean Square Error (RMSE), for which the best results are achieved with a typology of network type Multi-Layer Perceptron (MLP). The selected networks type MLP are characterized by the highest degree of classification at 0.99 and RMSE at 0.11 at most at the same time.

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.