The article is attempted to use the mathematical apparatus of fuzzy logic for processing the initial data of the model of an intellectual system for assessing the environmental safety of irrigated agricultural land and assessing the risks arising in the course of economic activity in the conditions of differentiated placement of crops. There is a proposed scheme for formalizing indicators set at a qualitative level which is based on membership functions. To determine the form of the membership function, limit, critical and high values of the level of environmental safety are identified. When describing model elements, we use a set of fuzzy situations that characterize the space of possible states of these elements, as well as a set of relationships between them. The assignment of communications that fuzzily is characterized the degree of influence between the typical states of pairs of elements allows the formation of production models in the form of a variety of fuzzy rules. The quantitative result of the interaction of elements is determined on the basis of fuzzy inference. The representation of the proposed model allows us to obtain an integrated assessment of the state of environmental safety of land and comparative characteristics of the magnitude of security threats are based on their automated assessment. The implementation of the proposed model allows us to obtain an integrated assessment of the state of environmental safety of land and comparative characteristics of the magnitude of security threats based on their automated assessment. Zoning environmental security threats are based on model. The developed model allows us to formulate recommendations on the set of necessary measures to prevent damage and minimize losses, as well as to evaluate the effectiveness of their execution in each specific case.

The article is described a new approach that allows to find the optimal solution to the problem of hybrid plant cost calculations of low and medium power facilities based on renewable energy sources. The calculation is performed using a multivariate analysis of the obtained results. The need to search for the system optimal configuration with maximum capacity at minimum cost is determined by the major setback to use of renewable energy sources, such as the high cost of equipment. Initial data for math model as an example of the Hybrid Plant cost calculation was from nature conditions in Volgograd and cost equipment in the demonstration Zone of Volgograd State Agricultural University. The demo zone was created in the educational research and production center of University for conducting experiments on the self-generated power supply of the facility management.

The article is devoted to the research of the inter-urban library role as a center for individual and mass library-information services for specialists in the agro-industrial complex. The work experience of the Volgograd region with agricultural literature is presented and the ways to overcome the problematic field of documentary funds replenishment, the information technologies usage in the technological processes of inter-urban libraries, and the service system management in the region are outlined.

The article analyzes the approaches to the construction of predictive models based on the apparatus of artificial neural networks, in particular, the method of back propagation of errors by iterative adjustment of weight coefficients.

The article deals with the issues of theoretical and applied modeling based on convolutional neural network in the field of agricultural production. In particular, the specificity of convolutional neural network is considered, its model, architecture and layers are presented. Possibilities of its use for the solution of problems of recognition and classification of images are revealed. In addition, it was found that the convolutional neural network differs from the usual perceptron in that each neural layer is not connected to all the neurons of the previous layer, but only to a part of it. In networks of this type, a small matrix is used for the convolution operation, which moves along the processed layer with a certain offset step. For the first layer, this matrix moves across the input image. After each shift, a signal is generated to activate the neuron of the next layer and from the corresponding position. As a result of studying the problems of image recognition using electronic computers, the possibilities of machine learning as a method of recognizing an object in an image that characterizes each object with a set of features are revealed. In addition, the author reveals the specifics of using the convolutional neural network mathematical apparatus as a model for image recognition, which consists in using an array of data describing the image and applying the method of error back propagation.