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Sowing Depth Monitoring System for High-Speed Precision Planters Based on Multi-Sensor Data Fusion.

Song Wang1, Shujuan Yi1, Bin Zhao1,2

  • 1College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.

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Summary
This summary is machine-generated.

This study introduces an improved sparrow search algorithm-extended Kalman filter (ISSA-EKF) for precise sowing depth monitoring in high-speed planters. The ISSA-EKF significantly reduces errors caused by vibration and sensor inaccuracies, enhancing agricultural machinery performance.

Keywords:
data fusionextended Kalman filterhigh-speed no-till seederimproved sparrow search algorithmsowing depth monitoring

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Area of Science:

  • Agricultural Engineering
  • Precision Agriculture
  • Sensor Fusion Technology

Background:

  • High-speed precision planters (12-16 km/h) face challenges with terrain undulation, mechanical vibration, and sensor errors.
  • These issues reduce the accuracy of sowing depth monitoring systems, impacting crop uniformity and yield.
  • Existing monitoring systems struggle to compensate for dynamic operational conditions and inherent sensor limitations.

Purpose of the Study:

  • To investigate multi-sensor data fusion technology for enhancing sowing depth monitoring in high-speed precision planters.
  • To develop and validate an optimized extended Kalman filter (EKF) using an improved sparrow search algorithm (ISSA).
  • To address mechanical vibration interference and sensor measurement errors for improved monitoring accuracy and reliability.

Main Methods:

  • Established a multi-sensor monitoring unit integrating laser, ultrasonic, and angle sensors.
  • Applied Kalman filtering to individual sensor data for noise reduction.
  • Developed a multi-sensor data fusion algorithm using an improved sparrow search algorithm (ISSA) to optimize Extended Kalman Filter (EKF) parameters.

Main Results:

  • The ISSA-EKF algorithm achieved high-precision monitoring with a Mean Absolute Error (MAE) of 0.083 cm and Root Mean Square Error (RMSE) of 0.103 cm.
  • Simulation tests showed significant accuracy improvements compared to original sensor values, filtered values, and the standard Sparrow Search Algorithm-EKF (SSA-EKF).
  • Field tests confirmed enhanced precision and reliability, with average MAE and RMSE reduced by 0.071 cm and 0.075 cm, respectively, and average R improved by 0.036.

Conclusions:

  • The proposed ISSA-EKF-based multi-sensor data fusion approach effectively mitigates vibration and sensor errors in high-speed precision planters.
  • This method significantly improves the accuracy and reliability of sowing depth monitoring systems.
  • The study provides a theoretical basis for advancing sowing depth monitoring technologies in modern agricultural machinery.