The article describes the main stages of the development of an intelligent system for analysis and segmentation of remote sensing data using deep machine learning and neural network modeling. In particular, the environment for creating an experimental software module was chosen, an experiment on training a neural network was implemented and the results of the training sample were evaluated.

The article deals the technique of application of neuro-fuzzy systems in problems of management of melioration of agricultural crops is considered. In particular, an adaptive network based on the fuzzy inference system is developed, a method for predicting data flows based on the neuro-fuzzy network is proposed, which is implemented in the MATLAB ANFIS module.

The integration process of information technologies and medicine has led to the formation of cyberspace. As a result of the research, the factors of positive and negative socio-technological impact of information technologies on the discourse of health care actors were identified. Positive impact factors include the possibility of continuing medical education, reduced time spent on virtual consultations, the possibility of creating social networks, communication, and the availability of specialized medical information to patients. Negative impact factors: the problem of self-diagnosis and self-treatment, the possibility of patients receiving incorrect information on the Internet, ineffective diagnostics in virtual consultation, and patients’ distrust of the health care system.

The article deals with the design and software implementation of neural network modules for solving problems of image recognition. In particular, it describes the development of a module for network training and recognition of input pulses, which made it possible to recognize, process and analyse aerial photographs of agricultural crops as objects of identification based on the use of a multi-layer deep learning neural network. The practical use of the software tool is possible for the study and study of the peculiarities of cultivation in conditions of irrigation and differentiated placement.

The article discusses the possibilities of using the mathematical and instrumental enhancement of the multilayer Hamming network in the tasks of recognizing aerial photographs, in particular, the advantages of the neuromodel for implementing further research to solve the problems of increasing crop yield based on monitoring and segmentation of dry crop areas are highlighted.