Predicting equipment utilization agricultural tractors is a B.Tech project topic for Agricultural Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Predicting equipment utilization agricultural tractors Project Details
| Abstract |
This study tackles the problem of fully autonomous equipment recognition for tractors using onboard data alone without considering additional sensors or manual entries. The study focuses on the use of standard tractor CAN-Bus signals for the purpose of the study. This study focused on the field testing of a 105 HP agricultural tractor performing one of three operations: ploughing, rotary tilling, and beet harvesting in the field. A set of data was put together from SAE J1939 parameters: wheel-based vehicle speed; engine torque percentage; hitch position; traction load; engine speed; at 10 Hz samples on IoT-based edge to cloud telemetry from a real-world situation.
Machine learning classifiers studied classified performances of Random Forest and XGBoost perfectly in stratified hold-out test sets. This was followed by a SHAP-based sensitivity analysis, in order to measure the contribution of parameters to classification and to confirm the interpretive models. Additional class-level examinations exhibit that each agricultural operation has its own unique topological structure with speed being the primary for ploughing, torque for rotary tillage and given patterns for beet harvesting. The study provides a great means for monitoring the utilization of equipment. |
| Reference Paper |
Predicting equipment utilization in agricultural tractors using field data and machine learning |
| Domain |
Agricultural Engineering |
| Sub-Domain |
Agricultural Machinery |
| PDF Download |
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| Get Help |
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