1. SISN
  2. All Volumes and Issues
  3. Volume 17, 2025
  4. Platform Implementation for Monitoring and Detecting Failures in Agriculture Machinery

Platform Implementation for Monitoring and Detecting Failures in Agriculture Machinery

SISN.
2025;
Volume 17
: pp. 281 - 291
https://doi.org/10.23939/sisn2025.17.281
Authors:
  1. Anton Shykhmat
  2. Zenoviy Veres
1
Lviv Polytechnic National University, Department of Computerized Automatic Systems
2
Lviv Polytechnic National University, Department of Computerized Automatic Systems, Lviv, Ukraine

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. An article provides detailed description of the proposed remote monitoring method, describes the structure of the remote monitoring system and the organization of data transmission, pre- processing, analysis and visualization. Architecturally, the platform adopts a microservices framework, ensuring scalability, high performance, security, and reliability. Algorithms of data processing in the system are described and the main features and benefits of using the monitoring solution are presented. The system’s predictive performance is assessed by processing real telemetry and maintenance data collected over 12 months from farms located in United States. The collected data was sent to platform using Java-based simulator and prediction results were evaluated using the Mean Absolute Percentage Error and Coefficient of Determination metrics, demonstrating the high accuracy of the implemented prediction model.

Agriculture vehicles
GIS
LSTM
Predictive Maintenance
RUL
antibiotic
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