DocumentCode
2779204
Title
A fruit recognition method for automatic harvesting
Author
Yang, L. ; Dickinson, J. ; Wu, Q.M.J. ; Lang, S.
Author_Institution
Windsor Univ., Windsor
fYear
2007
fDate
4-6 Dec. 2007
Firstpage
152
Lastpage
157
Abstract
This paper presents a method to detect and recognize mature tomato fruit clusters on a complex-structured tomato plant containing clutter and occlusion in a tomato greenhouse for automatic harvesting purpose. A color stereo vision camera (PGR BumbleBee2) is applied as the vision sensor. The proposed method performs a 3D reconstruction with the data collected by the stereo camera to create a 3D environment for further processing. The color layer growing (CLG) method is introduced to segment the mature fruits from the leaves, stalks, background and noise. Target fruit clusters can then be located by depth segmentation. The experimental data was collected from a tomato greenhouse and the method is justified by the experimental results. Our experiments included severe self and stereo occlusion, which wasn´t both included in the previous work.
Keywords
agricultural engineering; agricultural products; greenhouses; image colour analysis; image recognition; image segmentation; object detection; stereo image processing; PGR BumbleBee2; automatic harvesting; clutter; color layer growing; color stereo vision camera; fruit detection; fruit recognition; fruit segmentation; mature tomato fruit clusters; stereo occlusion; tomato greenhouse; tomato plant; vision sensor; Automation; Cameras; Colored noise; Costs; Filters; Image edge detection; Image reconstruction; Robot vision systems; Shape; Smoothing methods; 3D Reconstruction; Color Segmentation; Fruit Recognition; Object Localization;
fLanguage
English
Publisher
ieee
Conference_Titel
Mechatronics and Machine Vision in Practice, 2007. M2VIP 2007. 14th International Conference on
Conference_Location
Xiamen
Print_ISBN
978-1-4244-1358-4
Electronic_ISBN
978-1-4244-1358-4
Type
conf
DOI
10.1109/MMVIP.2007.4430734
Filename
4430734
Link To Document