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Agricultural machinery operation trajectory identification and operation area estimation for cloud-platform
Baozhong Li1,2,3, Yunhe Feng2, Guomin Zhou3,4
1Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China.
Frontiers in Plant Science
|July 16, 2026
Summary
This study presents a new method for accurately identifying agricultural machinery paths and estimating operation areas, crucial for cloud-based farm management. The approach enhances precision and efficiency in agricultural data analysis.
Area of Science:
- Agricultural Engineering
- Geospatial Data Science
- Precision Agriculture
Background:
- Accurate identification of agricultural machinery trajectories and operation areas is vital for cloud-based supervision and service settlement.
- Raw Global Navigation Satellite System (GNSS) data is often degraded by positioning drift, missing points, and transfer paths, hindering reliable analysis.
- Existing methods struggle to balance recognition accuracy and engineering efficiency for complex trajectory data.
Purpose of the Study:
- To propose a staged trajectory identification and area estimation method for cloud-platform deployment.
- To address the challenges of noisy GNSS data in agricultural machinery operations.
- To improve the reliability and efficiency of agricultural machinery management platforms.
Main Methods:
- Standardization of raw trajectory sequences via integrity checking, motion filtering, speed cleaning, and temporal interpolation.
- Organization of candidate trajectories using spatiotemporal constraints and removal of non-operational paths.
- Multi-channel trajectory image representation (speed, acceleration, heading variation) input to an improved CBGAM U-Net for semantic segmentation.
- Reconstruction of operation coverage areas using a cubic-spline-smoothed vector-buffer algorithm with corrections.
Main Results:
- Achieved an average trajectory recognition accuracy of 96.32% on the IAEMP platform dataset.
- Controlled absolute area estimation errors within 3.00% at the parcel level.
- Demonstrated stable processing efficiency for cloud-platform deployment.
- Field validation with Real-Time Kinematic (RTK) showed area accuracies of 99.64% for wheat harvesting and 99.91% for rotary tillage.
Conclusions:
- The proposed staged framework significantly enhances trajectory identification reliability and area estimation consistency.
- The method maintains practical deployability for agricultural machinery management platforms.
- This approach offers a robust solution for precise agricultural machinery operation monitoring.
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