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Related Experiment Videos

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
PubMed
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.

Keywords:
agricultural machinerybuffer algorithmoperation areaoperation trajectorysemantic segmentation

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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.