This article explores various architectures of neural networks in order to create models in the field of agriculture, with an emphasis on their use in livestock farms. The paper describes the architecture of Kolmogorov-Arnold networks, considers the stages of data collection and preliminary preparation, the learning process of neural networks, as well as their implementation. As a result, models were developed using Kolmogorov-Arnold networks and a multilayer perceptron. The study compared the effectiveness of the proposed architectures. The experiment demonstrates that Kolmogorov-Arnold networks have higher accuracy in predictions, which makes them a promising tool for forecasting. The developed model has been integrated into the livestock information system being developed to predict the growth, health and other indicators of animals, allowing for more accurate management of the growing process.

Keywords: precision animal husbandry, Kolmogorov-Arnold network, modeling, neural network, monitoring, cultivation, data modeling, forecasting

This article explores the introduction and implementation of neural network models in the field of agriculture, with an emphasis on their use in smart greenhouses. Smart greenhouses are innovative systems for controlling the microclimate and other factors affecting plant growth. Using neural networks trained on data on soil moisture, temperature, illumination and other parameters, it is possible to predict future indicators with high accuracy. The article discusses the stages of data collection and preparation, the learning process of neural networks, as well as the practical implementation of this approach. The results of the study highlight the prospects for the introduction of neural networks in the agricultural sector and their important role in optimizing plant growth processes and increasing the productivity of agricultural enterprises.

Keywords: neural network, predicting indicators, smart greenhouse, artificial intelligence, data modeling, microclimate

The article thoroughly explores cloud, fog, and edge computing, highlighting the distinctive features of each technology. Cloud computing provides flexibility and reliability with remote access capabilities, but encounters delays and high costs. Fog computing focuses on data processing at a low level of infrastructure, ensuring high speed and minimal delays. Edge computing shifts computations to the data source itself, eliminating delays and enhancing security. Applications of these technologies in various fields are analyzed, and their future development is predicted in the rapidly evolving world of information systems.

Keywords: cloud computing, fog computing, edge computing, cloud technologies, data processing infrastructure, scope of application, hybrid computing, Internet of Things, artificial intelligence, information systems development