Показатели качества работы автоматизированной машины для ухода за растениями картофеля в селекции и семеноводстве

Introduction. According to experts, in the coming years, the world market of organic products will continue to grow at a rate of 15-16% per year. The growth will equal about 20% of the world market of all agricultural products by 2025. In this connection, of a particular interest is the development of elements of agricultural technologies for the use of soil-improving substances and growth regulators of natural origin, including humic preparations, which provide for sufficiently high yields of agricultural products. Materials and Methods. The methods of using humic preparations were investigated on the example of spring barley seeds, treated with the traditional semi-dry method and using hot mist. Germination of seeds in the laboratory was carried out according to the Russian State Standard GOST 12038-84. The laboratory data were subjected to mathematical processing by the method of variance analysis. Results. The treatment of seeds with humic preparations led to an increase in sowing qualities, regardless of the method of seed treatment. However, the comparative efficiency analysis of the pre-sowing treatment of the test crop seeds with the aerosol and using the semi-dry method showed that the use of the developed equipment made it possible to increase the biological activity of humic preparations, which brought about an increase in the growth and biometric parameters of seedlings. Discussion and Conclusion. The treatment of spring barley seeds with humic preparations has some positive effect on their sowing and yield qualities. The strongest positive effect was obtained when treating seeds with reduced sowing qualities, which was quite often observed when harvesting seed crops in years with unfavorable conditions: heavy rainfall, lack of heat, as well as in violation of the technology of harvesting and preparing seeds. Our experimental studies showed that the use of the developed device for treating seeds with hot mist reduces energy costs and increases processing efficiency.

Relevance. The article deals with the relevance of a quantitative assessment of the level of biological risk for alimentary-caused factors according to the epidemiological indicators of infectious morbidity in the Ryazan region over a five-year period. Alimentarycaused biological risk factors are pathogens of infectious and parasitic diseases of various etiologies transmitted with food products, which can be the causative agents of especially dangerous infections, acute intestinal infections, food poisoning or be a source of food poisoning.Methods. In the course of the research, we used statistical methods for analyzing and assessing cyclical trends in morbidity, predicting the dynamics of morbidity, comparing the dynamics of morbidity with the dynamics of alimentary-related biological risk factors.Results. To quantify the level of biological risk for alimentary-related factors according to the epidemiological indicators of infectious morbidity in the Ryazan region, threestage epidemiological diagnostics was carried out, including the stage of collecting and analyzing statistical information, a descriptive and analytical stage. The results of our analysis over a five-year period showed that the greatest danger at present in terms of food safety is posed by risk factors of bacterial etiology: Staphylococcus aureus bacteria (УР_эпид = 365,572), diarrheagenic serovariants of Escherichia and bacteria of the Escherichia coli group (УР_эпид = 367,230), as well as various Salmonella serovariants (УР_эпид = 371,161).

Relevance. In order to obtain high-quality seed material, seed farms should pay great attention to crop cultivation technologies. At the same time, an important role is played by the implementation of such breeding measure as phyto-cleaning of breeding and seed plots, in order to identify and eliminate infected plants. However, it is worth noting the fact that the implementation of such measure requires the presence of highly qualified specialists capable of detecting plant diseases at early stages. However, currently there is a shortage of such employees in agriculture, and therefore the development of innovative digital technologies aimed at detecting infected plants is an urgent task. Currently, machine vision and neural network technologies designed to solve such problems are actively developing.Methods. As part of the research, existing machine vision technologies were analyzed, as well as developed machine learning technologies. Then, based on the analysis, a software package based on a convolutional neural network was developed. During the training and testing of the neural network, framing technologies, affine transformation methods, information and logical analysis of the initial information were used.Results. To determine the quality of the software package for the identification of diseased potato plants, a series of tests was conducted. During the research, the accuracy with which the distribution of plants to a particular group was carried out was evaluated. The analysis of the results showed that the chosen neural network design successfully coped with the experimental task. At the same time, for the further development of this direction, it is necessary to create an extensive information base on potato diseases. That will allow in the future to develop a software and hardware complex for the analysis of potato plantings and the identification of infected plants in real time.

Анализ тенденций развития тукосмесительного оборудования

The article discusses modern approaches to the control of technological processes in the production of humic preparations. The technology of differentiated introduction of alkali in their production is considered. An artificial neural network is presented, aimed at controlling the technological process of extracting humic substances and, as a result, improving the quality of the final product. The developed neural network is part of a multi-level algorithm of the automation system of the technological line. The introduction of the proposed artificial neural network into the technological process will increase the efficiency of extraction of humates by up to 20%.