The article deals with multi-criteria mathematical programming problems aimed at optimizing food production. One of the models of one-parameter programming is associated with solving the problem of combining crop production, animal husbandry and product processing. It is proposed to use the time factor as the main parameter, since some production and economic characteristics can be described by significant trends. The second multi-criteria parametric programming model makes it possible to optimize the production of agricultural products and harvesting of wild plants. in relation to the municipality, which is important for territories with developed agriculture and high potential of food forest resources.

Keywords: parametric programming, agricultural production, two-criteria model

The article is devoted to the review of food production planning models, the analysis of their features. For this purpose, the classification of models was considered, which included models with deterministic and interval parameters, and also took into account the influence of natural and man-made events. The article considers a model for optimizing the functioning of an agro-industrial cluster, presents models for optimizing the production of food products in agro-industrial clusters of the Irkutsk region.

Keywords: optimization methods, agricultural production planning model, deterministic and interval parameters

The article discusses transfer learning methods of convolutional neural network VGG16 for solving the problem of object recognition in images from UAVs (unmanned aerial vehicles). In the absence of the required amount of initial information, it is proposed to work on the augmented dataset. The article presents the architecture of a neural network and considered its action on a specific example. When developing a service, loading the image and displaying the results of the model, was used Flask framework, training of models took place using a cloud service Google Colab based on Jupyter Notebook.

Keywords: deep learning, neural networks, object recognition, data augmentation

Text summarization is the process of creating concise summary of a text document which maintains important meaningful information and general meaning of source text. In this paper we overview automatic text summarization task and text preprocessing methods, then we describe the main approaches to automatic text summarization and finally six methods are compared by their performances and execution times. Results of comparison are presented along with example of text summarization. This overview can help those looking to get a basic understanding of the text summarization task and NLP field.

Keywords: text summarization, machine learning, natural language processing