+ Site Statistics
References:
54,258,434
Abstracts:
29,560,870
PMIDs:
28,072,757
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

Automated Detection of Branch Shaking Locations for Robotic Cherry Harvesting Using Machine Vision



Automated Detection of Branch Shaking Locations for Robotic Cherry Harvesting Using Machine Vision



Robotics 6(4): 31



Automation in cherry harvesting is essential to reduce the demand for seasonal labor for cherry picking and reduce the cost of production. The mechanical shaking of tree branches is one of the widely studied and used techniques for harvesting small tree fruit crops like cherries. To automate the branch shaking operation, different methods of detecting branches and cherries in full foliage canopies of the cherry tree have been developed previously. The next step in this process is the localization of shaking positions in the detected tree branches for mechanical shaking. In this study, a method of locating shaking positions for automated cherry harvesting was developed based on branch and cherry pixel locations determined using RGB images and 3D camera images. First, branch and cherry regions were located in 2D RGB images. Depth information provided by a 3D camera was then mapped on to the RGB images using a standard stereo calibration method. The overall root mean square error in estimating the distance to desired shaking points was 0.064 m. Cherry trees trained in two different canopy architectures, Y-trellis and vertical trellis systems, were used in this study. Harvesting testing was carried out by shaking tree branches at the locations selected by the algorithm. For the Y-trellis system, the maximum fruit removal efficiency of 92.9% was achieved using up to five shaking events per branch. However, maximum fruit removal efficiency for the vertical trellis system was 86.6% with up to four shakings per branch. However, it was found that only three shakings per branch would achieve a fruit removal percentage of 92.3% and 86.4% in Y and vertical trellis systems respectively.

(PDF emailed within 0-6 h: $19.90)

Accession: 066350829

Download citation: RISBibTeXText

DOI: 10.3390/robotics6040031


Related references

Machine vision for three-dimensional modelling of Gerbera jamesonii for automated robotic harvesting. Acta Horticulturae (691(Vol 2)): 757-764, 2005

Identification of fruit and branch in natural scenes for citrus harvesting robot using machine vision and support vector machine. International Journal of Agricultural and Biological Engineering 7(2): 115-121, 2014

Machine vision algorithm of eggplant recognition for robotic harvesting. Journal of Society of High Technology in Agriculture 12(1): 38-46, 2000

Machine vision to locate melons and guide robotic harvesting. Paper American Society of Agricultural Engineers ( 91-7006): 21pp., 1991

Detection of cherry tree branches with full foliage in planar architecture for automated sweet-cherry harvesting. Biosystems Engineering 146: 3-15, 2016

Robotics and machine vision Engineering and horticultural aspects of robotic fruit harvesting technology Opportunities and constraints. Hortscience 38(5): 800-801, 2003

Automated Debris Mass Estimation for Citrus Mechanical Harvesting SYSTEMS USING MACHINE VISION. Applied Engineering in Agriculture 27(5): 673-685, 2011

Machine vision and r.f. absorption detection techniques for robotic production of sugarcane seedpieces. Agri Mation 1 Proceedings of the conference, Chicago, February 25-28, 1985: 312-320, 1985

Harvesting olives with a shaking machine: The results of a continuous field trial and analyses of the limitations of the machine. Studi Sassaresi, III 1977; 24: 100-139, 1976

Automated detection of pistachio defects by machine vision. Applied engineering in agriculture 17(5): 729-732, 2001

The Schaumann cherry shaking machine. Mitteilungen des Obstbauversuchsringes des Alten Landes, 26: 7, 252-255, 1971

Machine vision system for automated detection of aflatoxin-contaminated pistachios. Journal of Agricultural & Food Chemistry 46(6): 2248-2252, 1998

Machine vision system for automated detection of stained pistachio nuts. Lebensmittel-Wissenschaft & Technologie 29(3): 203-209, 1996

Mechanical harvesting of olives. The branch shaking solution. Arboriculture Fruitiere ( 413): 33...40, 1989

The cause of differences in suitability for harvesting by shaking of some sweet and sour cherry varieties. Erwerbsobstbau, 13: 1, 8-10, 1971