Tracking Accuracy Evaluation of Autonomous Agricultural Tractors via Rear Three-Point Hitch Estimation Using a Hybrid Model of EKF Transformer

Eun-Kuk Kim, Tae-Ho Han, Jun-Ho Lee, Cheol-Woo Han () and Ryu-Gap Lim ()
Additional contact information
Eun-Kuk Kim: Agriculture Machinery Test Team, Korea Agriculture Promotion Agency, Iksan 54667, Republic of Korea
Tae-Ho Han: Agriculture Machinery Test Team, Korea Agriculture Promotion Agency, Iksan 54667, Republic of Korea
Jun-Ho Lee: Agriculture Machinery Test Team, Korea Agriculture Promotion Agency, Iksan 54667, Republic of Korea
Cheol-Woo Han: Department of Agriculture Life Bio System, Korea Polytechnic College, Kimje 54352, Republic of Korea
Ryu-Gap Lim: Department of Convergent Biosystems Engineering, Sunchon National University, Suncheon 57922, Republic of Korea

Agriculture, 2025, vol. 15, issue 14, 1-22

Abstract: The objective of this study was to improve measurement accuracy in the evaluation of autonomous agricultural tractor performance by addressing external disturbances, such as sensor installation errors, vibrations, and heading-induced bias that occur during the measurement of the conventional rear three-point hitch (Rear 3-Point) system. To mitigate these disturbances, the measurement point was relocated to the cab, where external interference is comparatively minimal. However, in compliance with the ISO 12188 standard, the Rear 3-Point system must be used as the reference measurement point. Therefore, its coordinates were indirectly estimated using an extended Kalman filter (EKF) and artificial intelligence (AI)-based techniques. A hybrid model was developed in which a transformer-based AI model was trained using the Rear 3-Point coordinates predicted by EKF as the ground truth. While traditional time-series models, such as LSTM and GRU, show limitations in predicting nonlinear data, the application of an attention mechanism was found to enhance prediction performance by effectively learning temporal dependencies and vibration patterns. The experimental results show that the EKF-based estimation achieved a precision of RMSE 1.6 mm, a maximum error of 12.6 mm, and a maximum standard deviation of 3.9 mm compared to actual measurements. From the perspective of experimental design, the proposed hybrid model was able to predict the trajectory of the autonomous agricultural tractor with significantly reduced external disturbances when compared to the actual measured Rear 3-Point coordinates, while also complying with the ISO 12188 standard. These findings suggest that the proposed approach provides an effective and integrated solution for developing high-precision autonomous agricultural systems.

Keywords: autonomous agricultural tractor; extended Kalman filter; transformer model; attention mechanism; artificial intelligence; rear 3-point hitch (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/14/1475/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/14/1475/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:14:p:1475-:d:1698292

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().