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Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review.

Yunchao Tang1, Mingyou Chen2, Chenglin Wang3

  • 1College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou, China.

Frontiers in Plant Science
|June 9, 2020
PubMed
Summary
This summary is machine-generated.

Machine vision enhances agricultural robot efficiency for fruit picking, but precise positioning remains a challenge for commercial use. Research advances in recognition, localization, and fault tolerance are key to overcoming these hurdles.

Keywords:
3D reconstructionagricultural harvesting roboticclassificationfault tolerancerecognitionvision

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Area of Science:

  • Agricultural Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Machine vision and algorithms boost harvesting robot efficiency, intelligence, and remote interaction in agriculture.
  • Despite potential, precise positioning of machine vision presents significant technical challenges for commercial harvesting robot applications.

Purpose of the Study:

  • To review the application and research progress of harvesting robots and vision technology in fruit picking.
  • To focus on vision and quantitative methods for localization, target recognition, 3D reconstruction, and fault tolerance in complex agricultural settings.

Main Methods:

  • Review of digital image processing and deep learning-based algorithms for fruit recognition and localization.
  • Exploration of fault-tolerant technologies for machine vision and robotic systems in agriculture.

Main Results:

  • Identified key challenges: target recognition under illumination changes and occlusion, dynamic environment tracking, 3D reconstruction, and vision system fault tolerance.
  • Discussed open research problems and future trends in machine vision for fruit harvesting robots.

Conclusions:

  • Advancements in machine vision are crucial for the commercial viability of agricultural robots.
  • Further research is needed to address challenges in recognition, localization, and fault tolerance for robust fruit harvesting.