Effect of using deflector in the distributor head of a pneumatic seed drill on the oat seed sowing unevenness.

This paper presents an innovative solution for a distributor head equipped with a deflector (controlled plate)-intended to change the tilt angle (realignment) of the pneumatic seed drill distributor head cover. We compared two qualitative parameters of seed sowing, coefficient of variation and coefficient of lateral unevenness of seed sowing (δ). Values were obtained on the test stand with an innovative deflector built into the distributor head at three angles of inclination (0°, 5° and 10°). Statistical analyses revealed a significant effect of airflow velocity and deflector angle, which corrects the deviation from the vertical plane of the distributor head, on the uniformity of seed sowing. In addition, regression equations were determined to predict the quality of the seed sowing process. The developed and manufactured innovative distributor head with a deflector that tilts in two planes, designed to improve the distribution evenness of the air stream transporting seed to individual coulters in pneumatic seed drills, received a positive review. The use of a deflector with automatic control of its position angle, correcting the deviation of the distributor head from a vertical plane in pneumatic seed drills improves the uniformity of seeding. Therefore, it is reasonable to use this solution for new pneumatic seed drills and those in use on soils with different relief (undulating surface). Moreover, the solution fits in with modern agricultural manufacturing in accordance with the ideas of precision agriculture.

Effects of Harvest Maturity on the Chemical and Energetic Properties of Corn Stover Biomass Combustion.

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.

Neural Reduction of Image Data in Order to Determine the Quality of Malting Barley.

Image analysis using neural modeling is one of the most dynamically developing methods employing artificial intelligence. The feature that caused such widespread use of this technique is mostly the ability of automatic generalization of scientific knowledge as well as the possibility of parallel analysis of the empirical data. A properly conducted learning process of artificial neural network (ANN) allows the classification of new, unknown data, which helps to increase the efficiency of the generated models in practice. Neural image analysis is a method that allows extracting information carried in the form of digital images. The paper focuses on the determination of imperfections such as contaminations and damages in the malting barley grains on the basis of information encoded in the graphic form represented by the digital photographs of kernels. This choice was dictated by the current state of knowledge regarding the classification of contamination that uses undesirable features of kernels to exclude them from use in the malting industry. Currently, a qualitative assessment of kernels is carried by malthouse-certified employees acting as experts. Contaminants are separated from a sample of malting barley manually, and the percentages of previously defined groups of contaminations are calculated. The analysis of the problem indicates a lack of effective methods of identifying the quality of barley kernels, such as the use of information technology. There are new possibilities of using modern methods of artificial intelligence (such as neural image analysis) for the determination of impurities in malting barley. However, there is the problem of effective compression of graphic data to a form acceptable for ANN simulators. The aim of the work is to develop an effective procedure of graphical data compression supporting the qualitative assessment of malting barley with the use of modern information technologies. Image analysis can be implemented into dedicated software.

Chemical and Structural Characterization of Maize Stover Fractions in Aspect of Its Possible Applications.

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.