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Intelligent Surface Recognition for Autonomous Tractors Using Ensemble Learning with BNO055 IMU Sensor Data

Author

Listed:
  • Phummarin Thavitchasri

    (Department of Mechatronics Engineering, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand)

  • Dechrit Maneetham

    (Department of Mechatronics Engineering, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand)

  • Padma Nyoman Crisnapati

    (Department of Mechatronics Engineering, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand)

Abstract

This study aims to enhance the navigation capabilities of autonomous tractors by predicting the surface type they are traversing using data collected from BNO055 Inertial Measurement Units (IMU sensors). IMU sensor data were collected from a small mobile robot driven over seven different floor surfaces within a university environment, including tile, carpet, grass, gravel, asphalt, concrete, and sand. Several machine learning models, including Logistic Regression, K-Neighbors, SVC, Decision Tree, Random Forest, Gradient Boosting, AdaBoost, and XGBoost, were trained and evaluated to predict the surface type based on the sensor data. The results indicate that Random Forest and XGBoost achieved the highest accuracy, with scores of 98.5% and 98.7% in K-Fold Cross-Validation, respectively, and 98.8% and 98.6% in an 80/20 Random State split. These findings demonstrate that ensemble methods are highly effective for this classification task. Accurately identifying surface types can prevent operational errors and improve the overall efficiency of autonomous systems. Integrating these models into autonomous tractor systems can significantly enhance adaptability and reliability across various terrains, ensuring safer and more efficient operations.

Suggested Citation

  • Phummarin Thavitchasri & Dechrit Maneetham & Padma Nyoman Crisnapati, 2024. "Intelligent Surface Recognition for Autonomous Tractors Using Ensemble Learning with BNO055 IMU Sensor Data," Agriculture, MDPI, vol. 14(9), pages 1-21, September.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:9:p:1557-:d:1474065
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    References listed on IDEAS

    as
    1. En Lu & Jialin Xue & Tiaotiao Chen & Song Jiang, 2023. "Robust Trajectory Tracking Control of an Autonomous Tractor-Trailer Considering Model Parameter Uncertainties and Disturbances," Agriculture, MDPI, vol. 13(4), pages 1-17, April.
    2. Yanming Li & Yibo Guo & Liang Gong & Chengliang Liu, 2023. "Harvesting Route Detection and Crop Height Estimation Methods for Lodged Farmland Based on AdaBoost," Agriculture, MDPI, vol. 13(9), pages 1-18, August.
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