Development of a Machine Learning–Driven Predictive Maintenance System for Agricultural Equipments

This study presents the development of a machine learning–driven predictive maintenance system for agricultural equipment to reduce unexpected downtime and improve equipment reliability. Traditional maintenance practices in agriculture are largely reactive or schedule-based, leading to high operational costs and inefficient fault detection. To address these challenges, the research integrates Internet of Things (IoT) sensor data, an Artificial Neural Network model for fault prediction, and a web-based monitoring platform to enable real-time equipment supervision and intelligent maintenance decision-making. The system was developed using Agile methodology with Python for machine learning processing, a MySQL database for data management, and web technologies for interface development. The findings show that the system achieved high prediction accuracy, effective real-time monitoring, and improved maintenance planning through automated alerts and performance reporting. Overall, the proposed solution demonstrates that combining machine learning with digital monitoring tools enhances equipment lifespan, reduces downtime, and provides a scalable and cost-effective approach for modern agricultural maintenance management.