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

Yunchao Tang,Xiangjun Zou,Chenglin Wang,Lufeng Luo,Jinhui Li,Mingyou Chen,Guoping Lian

doi:10.3389/fpls.2020.00510

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 occlusion environments; target tracking in dynamic interference-laden environments, 3D target reconstruction, and fault tolerance of the vision system for agricultural robots. In the end, several open research problems specific to recognition and localization applications for fruit harvesting robots are mentioned, and the latest development and future development trends of machine vision are described.

Highlights

The field of robotics is broad and covers several diverse technological areas (Yang et al, 2018)
Liu and Liu (2007) designed a system based on multi-spectral vision technology and triangulation technology to obtain the spatial position and maturity information of apple fruit through a specific optical path, which was an attempt to have great application value; Lu et al (2011) used multi-spectral imaging techniques to identify branches under different lighting conditions, which ensured the efficiency of path planning and safety of the operation of citrus picking robots in complex natural scenes
The results showed that the recognition rate of apples can reach over 90%

Summary

Introduction

The field of robotics is broad and covers several diverse technological areas (Yang et al, 2018). Liu and Liu (2007) designed a system based on multi-spectral vision technology and triangulation technology to obtain the spatial position and maturity information of apple fruit through a specific optical path, which was an attempt to have great application value; Lu et al (2011) used multi-spectral imaging techniques to identify branches under different lighting conditions, which ensured the efficiency of path planning and safety of the operation of citrus picking robots in complex natural scenes.

Results

Conclusion