Real-Time Adaptive Tractor Ride Comfort Adjustment System Based on Machine Learning Method
Tractors are among the most widely used machinery in agriculture. However, low-frequency vibrations during tractor operations pose significant health risks to drivers, such as musculoskeletal disorders, and negatively impact ride comfort. Current approaches rely on offline comfort prediction models,...
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| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10816406/ |
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| author | Hu Tian Anping Ji |
| author_facet | Hu Tian Anping Ji |
| author_sort | Hu Tian |
| collection | DOAJ |
| description | Tractors are among the most widely used machinery in agriculture. However, low-frequency vibrations during tractor operations pose significant health risks to drivers, such as musculoskeletal disorders, and negatively impact ride comfort. Current approaches rely on offline comfort prediction models, which lack real-time feedback, making them unsuitable for practical field applications. To address this gap, we propose a real-time recommendation system based on Internet of Things (IoT) and Machine Learning (ML) to enhance the driving comfort of agricultural tractors. Our low-cost IoT-enabled solution is compatible with existing tractors, requiring no expensive intelligent upgrades. Using the XGBoost model for ride comfort prediction, we achieved superior performance (R<inline-formula> <tex-math notation="LaTeX">$^{2} =0.96$ </tex-math></inline-formula>, RMSE =0.015) compared to other ML models. Additionally, the Particle Swarm Optimization (PSO) algorithm is employed to recommend optimal operational parameters, reducing the ride comfort value (OVV) by 6.67% in real-time experiments. This study highlights a scalable, data-driven approach for improving tractor comfort and offers a reference for intelligent control strategies in Agriculture 4.0. |
| format | Article |
| id | doaj-art-184b11454a44435cb6482682de7e25aa |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-184b11454a44435cb6482682de7e25aa2025-01-09T00:01:50ZengIEEEIEEE Access2169-35362025-01-01133274328310.1109/ACCESS.2024.352295910816406Real-Time Adaptive Tractor Ride Comfort Adjustment System Based on Machine Learning MethodHu Tian0https://orcid.org/0009-0000-1732-8908Anping Ji1School of Mechanical Engineering, Chongqing Three Gorges University, Chongqing, ChinaSchool of Mechanical Engineering, Chongqing Three Gorges University, Chongqing, ChinaTractors are among the most widely used machinery in agriculture. However, low-frequency vibrations during tractor operations pose significant health risks to drivers, such as musculoskeletal disorders, and negatively impact ride comfort. Current approaches rely on offline comfort prediction models, which lack real-time feedback, making them unsuitable for practical field applications. To address this gap, we propose a real-time recommendation system based on Internet of Things (IoT) and Machine Learning (ML) to enhance the driving comfort of agricultural tractors. Our low-cost IoT-enabled solution is compatible with existing tractors, requiring no expensive intelligent upgrades. Using the XGBoost model for ride comfort prediction, we achieved superior performance (R<inline-formula> <tex-math notation="LaTeX">$^{2} =0.96$ </tex-math></inline-formula>, RMSE =0.015) compared to other ML models. Additionally, the Particle Swarm Optimization (PSO) algorithm is employed to recommend optimal operational parameters, reducing the ride comfort value (OVV) by 6.67% in real-time experiments. This study highlights a scalable, data-driven approach for improving tractor comfort and offers a reference for intelligent control strategies in Agriculture 4.0.https://ieeexplore.ieee.org/document/10816406/Tractormachine learningparticle swarm optimizationinternet of thingsdriving comfortreal-time control |
| spellingShingle | Hu Tian Anping Ji Real-Time Adaptive Tractor Ride Comfort Adjustment System Based on Machine Learning Method IEEE Access Tractor machine learning particle swarm optimization internet of things driving comfort real-time control |
| title | Real-Time Adaptive Tractor Ride Comfort Adjustment System Based on Machine Learning Method |
| title_full | Real-Time Adaptive Tractor Ride Comfort Adjustment System Based on Machine Learning Method |
| title_fullStr | Real-Time Adaptive Tractor Ride Comfort Adjustment System Based on Machine Learning Method |
| title_full_unstemmed | Real-Time Adaptive Tractor Ride Comfort Adjustment System Based on Machine Learning Method |
| title_short | Real-Time Adaptive Tractor Ride Comfort Adjustment System Based on Machine Learning Method |
| title_sort | real time adaptive tractor ride comfort adjustment system based on machine learning method |
| topic | Tractor machine learning particle swarm optimization internet of things driving comfort real-time control |
| url | https://ieeexplore.ieee.org/document/10816406/ |
| work_keys_str_mv | AT hutian realtimeadaptivetractorridecomfortadjustmentsystembasedonmachinelearningmethod AT anpingji realtimeadaptivetractorridecomfortadjustmentsystembasedonmachinelearningmethod |