This paper considers the development and training of a neural network model for the identification of the species and quantitative composition of pollen, which will subsequently be used to determine the botanical origin of honey and quantitative calculations of pollen grains contained in a certain mass of honey. The main purpose of the study is to create a model that can effectively distinguish the morphology of pollen grains present in honey, determine their quantitative and qualitative composition, which will improve product quality control, as well as identify its botanical and geographical origin. For this purpose, similar works on pollen classification were analyzed. Pwtorch was chosen as the framework for creating a neural network model, which provides the possibility of detailed configuration of the model. The result of the work is a trained model capable of classifying pollen grains.

Keywords: neural networks, classification task, pollen classification, convolutional neural networks, PyTorch

On the basis of the model of non-piston displacement of oil with water, a mathematical model, algorithm and software product are presented, which allows to conduct numerical studies of the indicators of the development of a layered layer, taking into account the method of flooding the field: reservoir water temperature or hot water.

Keywords: continuity equation, heat transfer equation, non-piston displacement of oil by water, finite-difference scheme

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