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Unstructured road extraction and roadside fruit recognition in grape orchards based on a synchronous detection

Xinzhao Zhou1,2, Xiangjun Zou2,3, Wei Tang2

  • 1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.

Frontiers in Plant Science
|June 19, 2023
PubMed
Summary
This summary is machine-generated.

A new algorithm accurately extracts roads and recognizes roadside grapes in complex orchards. This enhances robot perception for fruit picking and navigation, improving detection by 23.84% and speed by 14.33%.

Keywords:
deep learningfruit harvesting robotmachine visionnon-structural environmentroadside fruits detection

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Area of Science:

  • Agricultural Robotics
  • Computer Vision
  • Machine Learning

Background:

  • Accurate road extraction and roadside fruit recognition are crucial for autonomous agricultural robots.
  • Complex orchard environments present significant challenges due to unstructured terrain and visual interference.
  • Existing methods often struggle with simultaneous road detection and fruit identification in real-world conditions.

Purpose of the Study:

  • To develop a novel algorithm for simultaneous unstructured road extraction and roadside fruit recognition in orchards.
  • To improve the perception capabilities of agricultural robots for tasks like fruit picking and navigation.
  • To address the limitations of current methods in complex and unstructured field environments.

Main Methods:

  • A preprocessing method involving region of interest interception, bilateral filtering, logarithmic space transformation, and MSRCR-based image enhancement.
  • A dual-space fusion road extraction method utilizing color channel enhancement and optimized gray factor analysis.
  • An optimized YOLOv7 model for enhanced recognition of randomly distributed grape clusters, integrated into a fusion framework with road extraction results.

Main Results:

  • The proposed preprocessing method effectively reduced interference from adverse environmental factors, enhancing road extraction quality.
  • The optimized YOLOv7 model achieved high performance in roadside fruit cluster detection (precision: 88.9%, recall: 89.7%, mAP: 93.4%, F1-score: 89.3%), outperforming YOLOv5.
  • The synchronous algorithm increased fruit identification by 23.84% and detection speed by 14.33% compared to standalone detection algorithms.

Conclusions:

  • The developed algorithm successfully enables synchronous road extraction and roadside fruit detection in complex orchard environments.
  • The optimized YOLOv7 model demonstrates superior performance for grape recognition in wild conditions.
  • This research provides a robust foundation for enhancing agricultural robot perception and decision-making systems.