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Optimizing drone-based pollination method by using efficient target detection and path planning for complex durian

Ruipeng Tang1, Jianxun Tang2, Mohamad Sofian Abu Talip3

  • 1School of Electrical, Electronic and Mechanical Engineering, University of Bristol, BS8 1UB, Bristol, UK. tang823662722@gmail.com.

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Summary
This summary is machine-generated.

This study introduces an AI-powered drone for durian pollination, enhancing flower detection and optimizing flight paths. This smart agriculture solution improves pollination efficiency and durian yield.

Keywords:
Agricultural automationAgricultural image analysisArtificial intelligence automates pollinationDrone pollinationIndustry, innovation and infrastructureSmart durian garden management

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Area of Science:

  • Agricultural Engineering
  • Artificial Intelligence
  • Robotics

Background:

  • Durian pollination relies on natural agents like bats and insects, which are declining due to environmental issues and pesticide use.
  • Inefficient natural pollination in durian orchards leads to reduced fruit yield and quality.
  • There is a need for advanced pollination techniques in durian cultivation.

Purpose of the Study:

  • To develop an AI-powered drone-based pollination system for durian orchards.
  • To improve object detection accuracy for durian flowers, especially in challenging conditions.
  • To optimize drone path planning for efficient and reliable pollination operations.

Main Methods:

  • Enhanced YOLO-v8 object detection algorithm integrated with GhostNet for improved precision and reduced computational load.
  • Developed an Enhanced Traveling Salesperson Problem (EN-TSP) algorithm using branch and bound with least-cost optimization for path planning.
  • Implemented an AI-powered drone system for automated pollination in complex durian orchard environments.

Main Results:

  • Achieved a 5.85% improvement in detection accuracy for durian flowers compared to baseline methods.
  • Demonstrated a 26.89% increase in path efficiency through optimized drone route planning.
  • The GhostNet-YOLO-v8 combination showed superior performance in detecting flowers under low-light and occluded conditions.

Conclusions:

  • The AI-powered drone system offers a scalable, efficient, and precise solution for durian pollination.
  • This technology can significantly reduce labor costs and enhance durian yield and fruit quality.
  • The integrated approach represents a significant advancement in smart agriculture for fruit cultivation.