Seminar

The report presents the results of a study devoted to the development of theoretical and methodological foundations for the intelligent processing and analysis of heterogeneous data in environmental monitoring and agrotechnological systems. The main focus is on the problems of heterogeneity of input information, incomplete observations, spatial sparsity of data, limited training samples, and high variability in data acquisition conditions. The report presents principles for the integration and validation of spatially distributed data, methodological approaches to developing models for the analysis and prediction of environmental indicators based on Earth remote sensing data and ground-based observations, as well as approaches to developing classification and detection models for visual objects under limited training data conditions. Architectural and organizational principles of intelligent visual recognition platforms focused on processing data streams from mobile systems are considered, along with methodological foundations for building distributed intelligent systems for monitoring and production accounting in industrial greenhouse complexes. Information on the testing and implementation of the research results is also provided.

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