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  7. Agriculture, Land And Farm Management
  9. Agricultural Production Systems Simulation
  11. Low-cost Plant-protection Unmanned Ground Vehicle System For Variable Weeding Using Machine Vision.
  1. Home
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  5. Agricultural, Veterinary And Food Sciences
  7. Agriculture, Land And Farm Management
  9. Agricultural Production Systems Simulation
  11. Low-cost Plant-protection Unmanned Ground Vehicle System For Variable Weeding Using Machine Vision.

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Low-Cost Plant-Protection Unmanned Ground Vehicle System for Variable Weeding Using Machine Vision.

Huangtao Dong1, Jianxun Shen2, Zhe Yu1

  • 1College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China.

Sensors (Basel, Switzerland)
|February 24, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a cost-effective machine vision system for unmanned ground vehicles (UGVs) to reduce pesticide waste. The variable weeding system precisely controls spraying based on real-time vegetation detection, enhancing agricultural sustainability.

Keywords:
PID controlPWM controlUGVfuzzy rules

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Area of Science:

  • Agricultural Engineering
  • Robotics
  • Computer Vision

Background:

  • Traditional agricultural spraying machinery contributes to pesticide waste and environmental pollution.
  • There is a need for precision agriculture technologies to optimize resource use and minimize environmental impact.

Purpose of the Study:

  • To develop and evaluate a machine vision-based variable weeding system for unmanned ground vehicles (UGVs).
  • To reduce pesticide waste and environmental pollution by enabling adaptive spraying based on vegetation distribution.

Main Methods:

  • Utilized a machine vision system with the normalized super green (2G-R-B) algorithm and fast iterative threshold segmentation for vegetation detection.
  • Implemented a PID control algorithm with fuzzy rule-based adaptive parameter adjustment (Kp, Ki, Kd) and an interleaved period PWM controller.
machine vision
variable spray
  • Conducted orthogonal testing with varying spraying duty cycles (25%, 50%, 75%, 100%) to assess system performance.

    • Main Results:

    • The chosen image processing combination effectively distinguished vegetation from the background, improving pixel extraction accuracy.
    • Orthogonal testing demonstrated minimal pressure variation (<0.05 MPa) and low spraying errors (average <2%, max <5%).
    • Field trials confirmed the system's ability to adjust spraying volume in real-time according to vegetation distribution.

    Conclusions:

    • The developed machine vision-based variable weeding system is a low-cost and effective solution for precision agriculture.
    • The system enhances control accuracy and stability, significantly reducing pesticide waste and environmental impact.