Agriculture vehicles

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.

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.