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Robotics 2017, 6(4), 31; doi:10.3390/robotics6040031

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

1
Biological Systems Engineering Department, Washington State University, Prosser, WA 99350, USA
2
Center for Precision & Automated Agricultural Systems, Washington State University, Prosser, WA 99350, USA
3
Department of Horticulture, Washington State University, Prosser, WA 99350, USA
*
Author to whom correspondence should be addressed.
Received: 31 August 2017 / Revised: 9 October 2017 / Accepted: 26 October 2017 / Published: 28 October 2017
(This article belongs to the Special Issue Agriculture Robotics)
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Abstract

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. View Full-Text
Keywords: cherry harvesting; branch detection; mechanical branch shaking; robotic harvesting; shaking locations; 3D reconstruction cherry harvesting; branch detection; mechanical branch shaking; robotic harvesting; shaking locations; 3D reconstruction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Amatya, S.; Karkee, M.; Zhang, Q.; Whiting, M.D. Automated Detection of Branch Shaking Locations for Robotic Cherry Harvesting Using Machine Vision. Robotics 2017, 6, 31.

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