Real-Time Decision Support System for Tractor Load Management

This research focuses on creating a real-time monitoring system for agricultural tractors that improves load management by continuously tracking essential parameters such as PTO RPM and fuel consumption. By accurately predicting engine load, the system allows operators to take timely actions to prevent overloading and enhance fuel efficiency. A detailed regression model was developed, yielding an R-squared value of 0.9368, indicating a strong correlation with engine load. The model also achieved a Root Mean Square Error (RMSE) of 448.26 and a Coefficient of Variation (CV) of 16.98%, showcasing its reliability and accuracy. These results demonstrate the system’s effectiveness in real-world agricultural applications, offering a cost-efficient approach to optimize tractor performance and promote sustainable farming practices. This advancement not only improves operational efficiency but also extends the lifespan of agricultural equipment, addressing the need for enhanced load management in contemporary farming.