Paper Titles
The Effect of the Green Density on the Properties of Powder Metallurgy Ti Sintered by High Frequency Induction Heating
p.43
Yield of Binary Ti-Cu and Ti-Mn Alloys Produced via Powder Metallurgy
p.49
Physical Properties and Rheological Characteristics of Activated Carbon Nanofluids with Varying Filler Fractions and Surfactants
p.58
Model Building and Simulation of the Suspension System for a Robotic Asparagus Harvester
p.69
Overview of Sensor Technologies Used for 3D Localization of Asparagus Spears for Robotic Harvesting
p.77
Investigation of Vial Position Controls for the Development of a Cave Auto-Sampler
p.86
Comparison of Spherical and Non-Spherical Objects in Cyclonic and Uniaxial Flow Regimes
p.93
Gait Analysis of Pedestrians with the Aim of Detecting Disabled People
p.105
Jitter Extraction in a Noisy Signal by Fast Fourier Transform and Time Lag Correlation
p.113

Overview of Sensor Technologies Used for 3D Localization of Asparagus Spears for Robotic Harvesting

Article Preview

Abstract:

Advances in agricultural automation, coupled with a general decline of available labour hasgenerated interest in automated harvesting of various crops. Paramount to the success of such systemsis the development of accurate, robust detection technologies and localization strategies. This paperpresents an overview of sensor technologies used in the detection and localization of green aspara-gus spears for robotic harvesting. Tactile, photoelectric, machine vision and time-of-flight sensors areinvestigated and their applicability for use in robotic asparagus harvesting is evaluated. Investigationof previous asparagus harvesting devices has revealed that no such device has yet achieved commer-cial viability. It was identified that this is likely due to weaknesses in currently employed detectiontechnologies, namely slow response times, high sensitivity to changes in ambient lighting conditionsand requirement for frequent manual calibration. Of the sensor technologies investigated it was foundthat time-of-flight cameras, such as the Microsoft Kinect V2 are the most feasible for the detectionof asparagus spears for robotic harvesting. It was concluded that further research would be conductedinto the application of such sensors into a commercially viable harvester.

You might also be interested in these eBooks
Advances in Materials Science and Engineering Technology View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 884)

Pages:

77-85

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.884.77

Citation:

Cite this paper

Online since:

August 2018

Authors:

Matthew Peebles*, Shen Hin Lim, Mike Duke, Chi Kit Au

Keywords:

3D Sensors, Asparagus Harvesting, Detection Systems, Machine Vision, Robotic Harvesting

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2018 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

References

[1] LLP, A.R., Agricultural Robots Market Analysis &; Trends - Product, Technology - Forcast to 2025. 2016. p.113.

Google Scholar

[2] Lewis, G., Personal Communication, M. Peebles,(2016).

Google Scholar

[3] Du, C., Z. Qin, and W. Shumao, Current Status and Technical Challenges of Asparagus Mechanical Harvesting. 2010 Pittsburgh, Pennsylvania, June 20 - June 23, (2010).

DOI: 10.13031/2013.29743

Google Scholar

[4] Matteoli, A.J., Asparagus harvester. 1952, Google Patents.

Google Scholar

[5] Turkington, J.O., Asparagus harvesting machine. 1956, Google Patents.

Google Scholar

[6] Haws, S.K., Stalk selective harvesting machine. 1977, Google Patents.

Google Scholar

[7] Arndt, G., R. Rudziejewski, and V.A. Stewart, On the future of automated selective asparagus harvesting technology. Computers and Electronics in Agriculture, 1997. 16(2): pp.137-145.

DOI: 10.1016/s0168-1699(96)00033-6

Google Scholar

[8] Lund, W.J., Asparagus harvester. 1985, Google Patents.

Google Scholar

[9] Haws, S.K.R., WA, US, Selective Harvester. 2010: United States.

Google Scholar

[10] Clary, C.D., et al., Performance and economic analysis of a selective asparagus harvester. Applied Engineering in Agriculture, 2007. 23(5): pp.571-577.

DOI: 10.13031/2013.23665

Google Scholar

[11] Humburg, D.S. and J.F. Reid, A machine vision algorithm for identification of harvestable spears of asparagus. 1990. p. 13pp.

Google Scholar

[12] Humburg, D.S. and J.F. Reid. Field performance of machine vision for the selective harvest of asparagus. in International Off-Highway and Powerplant Congress and Exposition, September 9, 1991 - September 12, 1991. 1991. Milwaukee, WI, United states: SAE International.

DOI: 10.4271/911751

Google Scholar

[13] Irie, N., et al., Asparagus Harvesting Robot Coordinated with 3-D Vision Sensor. 2009 Ieee International Conference on Industrial Technology, Vols 1-3, 2009: pp.408-413.

DOI: 10.1109/icit.2009.4939556

Google Scholar

[14] Mehta, S.S. and T.F. Burks, Multi-camera Fruit Localization in Robotic Harvesting. IFACPapersOnLine, 2016. 49(16): pp.90-95.

DOI: 10.1016/j.ifacol.2016.10.017

Google Scholar

[15] Jones, M., Personal Communication, M. Peebles,(2016).

Google Scholar

[16] Baylou, P., et al., Detection and three-dimensional localization by stereoscopic visual sensor and its application to a robot for picking asparagus. Pattern Recognition, 1984. 17(4): pp.377-384.

DOI: 10.1016/0031-3203(84)90067-0

Google Scholar

[17] Bousseau, G., et al., Automatic asparagus picking machine. 1984, Google Patents.

Google Scholar

[18] Grattoni, P., et al. Automatic harvesting of asparagus: an application of robot vision to agriculture. (1994).

Google Scholar

[19] Strauß, J., Development of an Automatic harvesting system for green asparagus with stalk detection in Ambient Light. 2014. pp.1-14.

Google Scholar

[20] Sakai, H., et al., Accurate position detecting during asparagus spear harvesting using a laser sensor. Engineering in Agriculture, Environment and Food, 2013. 6(3): pp.105-110.

DOI: 10.1016/s1881-8366(13)80019-5

Google Scholar

[21] Irie, N. and N. Taguchi, Asparagus harvesting robot. Journal of Robotics and Mechatronics, 2014. 26(2): pp.267-268.

Google Scholar

[22] Luna, C.A., et al., Robust people detection using depth information from an overhead Time-ofFlight camera. Expert Systems with Applications, 2017. 71: pp.240-256.

DOI: 10.1016/j.eswa.2016.11.019

Google Scholar

[23] Stahlschmidt, C., et al., Applications for a people detection and tracking algorithm using a timeof- flight camera. Multimedia Tools and Applications, 2016. 75(17): pp.10769-86.

DOI: 10.1007/s11042-014-2260-3

Google Scholar

[24] Kapuciski, T., M. Oszust, and M. Wysocki. Hand gesture recognition using time-of- flight camera and viewpoint feature histogram. in 11th International Conference on Diagnostics of Processes and Systems, DPS 2013, September 8, 2013 - September 11, 2013. 2014. Lubuski, Poland: Springer Verlag.

DOI: 10.1007/978-3-642-39881-0_34

Google Scholar

[25] Oprisescu, S., C. Burlacu, and V. Buzuloiu. Action Recognition using Time of Flight Cameras. in 2010 8th International Conference on Communications (COMM), 10-12 June 2010. 2010. Piscataway, NJ, USA: IEEE.

DOI: 10.1109/iccomm.2010.5509074

Google Scholar

[26] Sun, L., J.-R. Cai, and J.-W. Zhao, Vision System Based on TOF 3D Imaging Technology Applied to Robotic Citrus Harvesting. Intelligent Automation &; Soft Computing, 2015: pp.1-10.

DOI: 10.1080/10798587.2015.1015767

Google Scholar