journal article Apr 28, 2017

Design, integration, and field evaluation of a robotic apple harvester

Journal of Field Robotics Vol. 34 No. 6 pp. 1140-1159 · Wiley
View at Publisher Save 10.1002/rob.21715
Abstract
AbstractEvery apple destined for the fresh market is picked by the human hand. Despite extensive research over the past four decades, there are no mechanical apple harvesters for the fresh market commercially available, which is a significant concern because of increasing uncertainty about the availability of manual labor and rising production costs. The highly unstructured orchard environment has been a major challenge to the development of commercially viable robotic harvesting systems. This paper reports the design and field evaluation of a robotic apple harvester. The approach adopted was to use a low‐cost system to assess required sensing, planning, and manipulation functionality in a modern orchard system with a planar canopy. The system was tested in a commercial apple orchard in Washington State. Workspace modifications and performance criteria are thoroughly defined and reported to help evaluate the approach and guide future enhancements. The machine vision system was accurate and had an average localization time of 1.5 s per fruit. The seven degree of freedom harvesting system successfully picked 127 of the 150 fruit attempted for an overall success rate of 84% with an average picking time of 6.0 s per fruit. Future work will include integration of additional sensing and obstacle detection for improved system robustness.
Topics

No keywords indexed for this article. Browse by subject →

References
75
[1]
United States Department of Agriculture (USDA).Definition of specialty crops. Retrieved fromhttp://www.ams.usda.gov/AMSv1.0/scbgpdefinitions.2008.
[2]
Washington State Employment Security Department.Agriculture workforce report. Retrieved fromhttps://fortress.wa.gov/esd/employmentdata/docs/industry-reports/agricultural-workforce-report-2013.pdf(accessed April 27 2016).2013.
[3]
GallardoRK TaylorM HinmanH.2009 cost estimates of establishing and producing ‘Gala’ apples in Washington. Washington State University Extension Factsheet FS005E. Pullman WA: Washington State University;2010.
[5]
Calvin L (2010)
[6]
Gonzalez‐BarreraA.More Mexicans leaving than coming to the U.S. Pew Research Center. Retrieved fromhttp://www.pewhispanic.org/files/2015/11/2015-11-19_mexican-immigration__FINAL.pdf(accessed November 24 2015).2015.
[8]
Robinson T "A vision for apple orchard systems of the future" New York Fruit Q. (2013)
[17]
Grand d'Esnon A (1987)
[18]
Harrell RC (1988)
[19]
Harvesting Robots for High‐value Crops: State‐of‐the‐art Review and Challenges Ahead

C. Wouter Bac, Eldert J. van Henten, Jochen Hemming et al.

Journal of Field Robotics 10.1002/rob.21525
[24]
Sensors and systems for fruit detection and localization: A review

A. Gongal, S. Amatya, M. Karkee et al.

Computers and Electronics in Agriculture 10.1016/j.compag.2015.05.021
[26]
Qiang L "Identification of fruit and branch in natural scenes for citrus harvesting robot using machine vision and support vector machine" Int J Agric Biol Eng (2014)
[29]
Silwal A "Identification of red apples in field environment with over‐the‐row machine vision system" Agric Eng Intl. (2014)
[30]
Silwal A "A hierarchical approach of apple identification for robotic harvesting" Trans ASABE (2015)
[31]
Tabb AL (2006)
[33]
Stajnko D "Modelling apple fruit yield using image analysis for fruit colour, shape and texture" Eur J Horticultural Sci (2009) 10.1079/ejhs.2009/1226350
[35]
Hannan MW (2004)
[48]
Bulanon DM "Fruit detection system and an end effector for robotic harvesting of Fuji apples" Agric Eng Int. (2010)
[50]
Washington State Department of Agriculture. Washington tree fruit acreage report 2011. Retrieved fromwww.nass.usda.gov/Statistics_by_State/Washington/Publications/Fruit/FruitTreeInventory2011.pdf(accessed April 27 2015).2011.

Showing 50 of 75 references

Related

You May Also Like

Stanley: The robot that won the DARPA Grand Challenge

Sebastian Thrun, Mike Montemerlo · 2006

1,655 citations

A survey of deep learning techniques for autonomous driving

Sorin Grigorescu, Bogdan Trasnea · 2019

1,443 citations

Autonomous driving in urban environments: Boss and the Urban Challenge

Chris Urmson, Joshua Anhalt · 2008

1,339 citations

Scan registration for autonomous mining vehicles using 3D‐NDT

Martin Magnusson, Achim Lilienthal · 2007

651 citations