геоінформаційні системи (ГІС)

Platform Implementation for Monitoring and Detecting Failures in Agriculture Machinery

In the dynamic landscape of modern agriculture, ensuring the reliability and efficiency of machinery is a critical challenge. This article proposes an innovative platform for monitoring and detecting failures in agricultural machinery, harnessing the power of Internet of Things (IoT) technology and cloud computing. The system in AWS cloud receives data from vehicles in real-time and can predict potential failures in engine, transmission, electric and hydraulic systems using machine learning algorithm LSTM.

PRINCIPLES OF SOFTWARE AND INFORMATION FOR MODELING HYDROLOGICAL RIVER BASINS OF THE UKRAINIAN CARPATHIANS

This study addresses the information and software tools used for modeling hydrological basins in the Ukrainian Carpathians, a region distinguished by its complex terrain and diverse ecosystems. The research focuses on a variety of hydrological modeling software, including HEC-HMS, SWAT, and GIS applications, which facilitate the analysis of hydrological processes and the assessment of river runoff dynamics. Furthermore, the integration of meteorological data and land use information is discussed to enhance the accuracy of hydrological models.

Agriculture Vehicles Predictive Maintenance With Telemetry, Maintenance History and Geospatial Data

Timely detection and prevention of agriculture vehicles malfunctions are key approaches to reducing maintenance costs, as well as updating and replacing equipment, and reducing the cost of growing agricultural crops. In this article an approach for Remaining Useful Life (RUL) prediction that utilizes a combination of telemetry, maintenance, and geospatial data (such as weather and terrain information) as input to a Long Short- Term Memory (LSTM) algorithm has been considered.