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

Frontiers in Plant Science
Yunchao TangXiangjun Zou

Abstract

The utilization of machine vision and its associated algorithms improves the efficiency, functionality, intelligence, and remote interactivity of harvesting robots in complex agricultural environments. Machine vision and its associated emerging technology promise huge potential in advanced agricultural applications. However, machine vision and its precise positioning still have many technical difficulties, making it difficult for most harvesting robots to achieve true commercial applications. This article reports the application and research progress of harvesting robots and vision technology in fruit picking. The potential applications of vision and quantitative methods of localization, target recognition, 3D reconstruction, and fault tolerance of complex agricultural environment are focused, and fault-tolerant technology designed for utilization with machine vision and robotic systems are also explored. The two main methods used in fruit recognition and localization are reviewed, including digital image processing technology and deep learning-based algorithms. The future challenges brought about by recognition and localization success rates are identified: target recognition in the presence of illumination changes and occlusi...Continue Reading

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Citations

Aug 6, 2020·Sensors·Jin Liu, Yongjian Gao
Dec 18, 2020·Frontiers in Plant Science·Manya AfonsoRon Wehrens
Feb 7, 2021·Sensors·Joseba GorospeMiguel Ángel Antón
Mar 23, 2021·Expert Systems with Applications·Qiangguo JinRan Su
Apr 27, 2021·Frontiers in Plant Science·Xuewei Wang, Jun Liu

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Methods Mentioned

BETA
light scattering
imaging techniques
feature extraction
feature point extraction

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