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Robotics in Agriculture and Forestry

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Springer Handbook of Robotics

Abstract

In agriculture and forestry, robotics has made a substantial impact. Farmers are conscious of their need for automatic vehicle guidance to minimize damage to the growing zone of their soil. Automatic sensing, handling, and processing of produce are now commonplace, while there is substantial instrumentation and mechanization of livestock procedures. In forestry, legged harvesters have not yet seen great success in their application, but the automation of trimming and forwarding with simultaneous localization and mapping techniques will change the industry in the future.

Some impressive developments in walking forestry harvesters are presented, including machines targeted towards the difficult terrain of the Scandinavian forests. More-conventional cut-to-length harvesters are also highly automated, while operations such as delimbing must be carried out at speed. Before complete autonomous harvesting becomes possible, some of the localization and mapping techniques that are described must come to fruition.

The combination of machine vision with global positioning by satellite (GPS) allows a tractor to follow a row of crops, performing a headland turn at the end of the row. The history of a series of projects is outlined, leading to the present outcome that is in the process of being commercialized. Another project that is based on machine vision relates to the location of macadamia nuts. To select which trees should be propagated, it is necessary to attribute fallen nuts to the correct tree. Color sorting and grading of produce is not a matter of sensing alone, but involves a measure of produce handling that puts it at the fringe of robotics.

Automated milking parlours have proved their worth. However success has eluded some other projects described here, such as automated sheep-shearing and an automated abattoir. Another project is presented that literally sorts the sheep from the goats, using a swinging gate to separate different species using machine vision so that feral species are excluded from watering holes in the dry Australian outback.

Although robotics is making rapid inroads into these areas, they are still a fruitful source of application projects, some sufficiently demanding to require the development of new theoretical techniques.

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Abbreviations

CAN:

controller area network

CTL:

cut-to-length

GPS:

global positioning system

NASA:

National Aeronautics and Space Agency

NCEA:

National Center for Engineering in Agriculture

PC:

Purkinje cells

PC:

principal contact

RFID:

radiofrequency identification

RGB:

red, green, blue

RTK:

real-time kinematics

SLAM:

simultaneous localization and mapping

SLAMP:

sheep loading animal manipulation platform

UAV:

unmanned aerial vehicles

US:

ultrasound

References

  1. The ZigBee Alliance. Last access 10 Dezember 2007, from www.zigbee.org

  2. J. Billingsley: Low cost GPS for the autonomous robot farmhand. In: Mechatronics and Machine Vision, ed. by J. Billingsley (Research Studies, Baldock 2000) pp. 119–125

    Google Scholar 

  3. A. Halme, K. Hartikainen, K. Kärkkäinen: Terrain adaptive motion and free gait of a six-legged walking machine, Contr. Eng. Pract. 2, 273–279 (1994)

    Article  Google Scholar 

  4. A. Halme, M. Vainio: Forestry robotics – why, what, when. In: Autonomous Robotic Systems, ed. by A.T. de Almeida, O. Khatib (Springer, Surrey 1998)

    Google Scholar 

  5. A. Halme, T. Luksch, S. Ylönen: Biomimicing motion control of the WorkPartner robot, Ind. Robot 31, 209–217 (2004)

    Article  Google Scholar 

  6. M. Manninen, A. Halme, R. Myllylä: An aimable laser time-of-flight range finder for rapid scene description, 7th Annu. Conf. Brit. Robot Assoc., Cambridge (1984)

    Google Scholar 

  7. A. Halme, A. Visala, M. Paakkunainen, J. Joensuu, P. Forsman, T. Torvikoski: Model based on-site description of robotized processings, 3rd IFAC/IFIP/IEA/IFORS Conf. Man-Mach. Syst. Anal. Design Eval., Oulu (1988)

    Google Scholar 

  8. P. Viljamaa, H.N. Koivo, A. Peltomaa: Adaptive feed control of a forest harvester, ICOM 2003 Int. Conf. Mechatron., Loughborough University (2003)

    Google Scholar 

  9. T. Högström, A. Wernersson: On segmentation, shape estimation and navigation using 3D laser range measurements of forest scenes, 3rd IFAC Symp. Intell. Auton. Veh., Madrid (1998)

    Google Scholar 

  10. P. Forsman, A. Halme: 3D mapping of natural environments with trees by means of mobile perception, IEEE Trans. Robot. 21, 462–490 (2005)

    Article  Google Scholar 

  11. T. Bailey: Mobile robot localisation and mapping in extensive outdoor environments, ed. by Australian Centre for Field Robotics (University of Sydney, Sydney 2002)

    Google Scholar 

  12. C. Brenneke, O. Wulf, B. Wagner: Using 3D laser range data for SLAM in outdoor environments, 2003 IEEE/RSJ Int. Conf. Intell. Robot. Syst., Las Vegas (2003)

    Google Scholar 

  13. J. Guivant, E. Nebot, S. Baiker: Autonomous navigation and map building using laser range sensors in outdoor, J. Robot. Syst. 17, 565–583 (2000)

    Article  MATH  Google Scholar 

  14. S. Thrun, W. Burgard, D. Fox: Probabilistic Robotics (MIT Press, Cambridge 2005)

    MATH  Google Scholar 

  15. C. Brack: Forest Measurement and Modeling (Australian National University, Canberra 2006)

    Google Scholar 

  16. T. Hellström, T. Johansson, O. Ringdahl, F. Georgsson, K. Prorok, U. Sandström: Development of an autonomous path tracking forest machine, Int. Conf. Field Serv. Robot., Port Douglas (2005)

    Google Scholar 

  17. B. Löfgren: Automation – way to increase productivity in logging, NSR Conf. For. Op., Hyytiälä (2004)

    Google Scholar 

  18. P. Kourtz, M. Strome, F. Gougeon: Autonomous forest land-tending robots, Meet. Stat. Methods Math. Comput., Birmensdorf (1992)

    Google Scholar 

  19. J. Billingsley, M. Schoenfisch: Vision-guidance of agricultural vehicles, Auton. Robot. 2, 65–76 (1995)

    Article  Google Scholar 

  20. NDSU Extension: Center pivot is agricultural production robot. Last access 10 December 2007, from http://www.farmandranchguide.com/articles/2006/02/02/ag_news/production_news/prod23.txt (2006)

  21. D. Elstein, E. Peabody: Fields with a mind of their own?, Agricult. Res. Mag. 53, 14–16 (2005)

    Google Scholar 

  22. C. Perry: Variable-rate irrigation takes guess work out. Last access 10 December 2007 http://georgiafaces.caes.uga.edu/storypage.cfm?storyid=2930 (2006)

  23. Y. Edan, V. Rogozin: Robotic melon harvesting: Prototype and field tests, IEEE Trans. Robot. Autom. 16, 831–834 (2000)

    Google Scholar 

  24. M. Kanemitsu, K. Yamamoto, Y. Shibano, Y. Goto, M. Suzuki: Development of a Chinese cabbage harvester (Part 1), Jap. Soc. Agricult. Mach. 55, 133–140 (1993)

    Google Scholar 

  25. A. Milella, G. Reina., M. Foglia, A. Gentile: Computer vision applications in agricultural robotics, 11th Conf. Mechatron. Mach. Vis. Pract., Macao (2005)

    Google Scholar 

  26. N. Murakami, K. Inoue, K. Otsuka: Selective harvesting robot for cabbages, Int. Symp. Autom. Robot. Bioproduct. Process. (1995)

    Google Scholar 

  27. M. Slaughter, R. Harre: Color vision in robotic fruit harvesting, Trans. ASAE 30, 1144–1146 (1987)

    Google Scholar 

  28. M. Dunn, J. Billingsley: Machine vision system for counting macadamia nuts, Australasian Conf. Robot. Autom., Brisbane (2003)

    Google Scholar 

  29. J. Billingsley, M. Schoenfisch: Vision and mechatronics applications at the NCEA, Fourth IARP Workshop Robot. Agricult. Food Ind., Toulouse (1995)

    Google Scholar 

  30. R. Leach: The Measurement of Surface Texture Using Stylus Instruments (National Physics Laboratory, London 2001)

    Google Scholar 

  31. M. Dunn, J. Billingsley: A machine vision system for surface texture measurements of citrus, Proc. 11th IEEE Conf. Mechatron. Mach. Vis. Pract., Macau (2004)

    Google Scholar 

  32. R.E. Graves: A primer on robotic milking, ASAE/CSAE Annu. Int. Meet., Ottawa (2004)

    Google Scholar 

  33. J. Trevelyan: Welcome to the Robot Sheep Shearing Project! Last access 10 December 2007 from http://www.mech.uwa.edu.au/jpt/shearmagic/Default.html (2004)

  34. W. A. Scholes: Two-armed sheep-shearing robot on wool firmsʼ threshold Last access 10 December 2007 from http://www.encyclopedia.com/doc/1G1-3886584.html (1985)

  35. Shear Express: Shear Express Wool Harvesting offers the key to a new future for the wool industry Last access 10 December 2007 from www.shearexpress.com.au (2006)

  36. J. Trevelyan: Robots for Shearing Sheep: Shear Magic (Oxford Univ. Press, Oxford 1992)

    Google Scholar 

  37. Commonwealth of Australia Senate: Official Hansard, Tuesday 7 May 1996. Last access 10 December 2007 from http://parlinfoweb.aph.gov.au/piweb/view_document.aspx?ID=713406&TABLE=HANSARDS (1996)

  38. Danish Crown Pork Processing Plant, Horsens Last access 10 December 2007 from www.danishcrown.dk (2006)

  39. SFK Systems: SFK Robots. Last access 10 December 2007 from www.sfk.com (2006)

  40. Y.-R. Chen, K. Chao, M.S. Kim: Machine vision technology for agricultural applications, Comput. Electron. Agricult. 36, 173–191 (2002)

    Article  Google Scholar 

  41. Big Dutchman: Callmatic 2. Last access 10 December 2007 from www.bigdutchman.de/eng (2005)

  42. M. Dunn, J. Billingsley, N. Finch: Machine vision classification of animals. In: Mechatronics and Machine Vision 2003: Future Trends (Research Studies, Baldock 2003) pp. 157–163.

    Google Scholar 

  43. R. Vaughan, N. Sumpter, A. Frost, S. Cameron: Robot sheepdog project achieves automatic flock control, Fifth Int. Conf. Simulation Adapt. Behav. (1998)

    Google Scholar 

  44. J. Billingsley, J. Stone, J. Hilton: A mobile robot for training horses, Conf. Field Serv. Robot., Canberra (1997)

    Google Scholar 

  45. Yamaha: Unmanned Helicopters. Last access 10 December 2007 from http://www.yamaha-motor.co.jp/global/about/business/sky/index.html (2006)

  46. UAV Collaborative: Unmanned Aerial Vehicles. Last access 10 December 2007 from www.uav-applications.org/projects.html (2006)

  47. T. Grift: U of I creates robot farmers, ACES News. Last access 10 December 2007 from www.aces.uiuc.edu/news/stories/news2813.html (2004)

  48. M. Miettinen, M. Öhman, A. Visala, P. Forsman: Simultaneous localization and mapping for forest harvesters, ICRAʼ07 2007 IEEE Int. Conf. Robot. Autom., Roma (2007)

    Google Scholar 

  49. M. Öhman, M. Miettinen, K. Kannas, J. Jutila, A. Visala, P. Forsman: Tree measurement and simultaneous localization and mapping system for forest harvesters, 6th Int. Conf. Field Serv. Robot., Chamonix (2007)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Engineering and Surveying, University of Southern Queensland, 4350, Toowoomba, QLD, Australia

    John Billingsley

  2. Department of Automation and Systems Technology, Helsinki University of Technology (TKK), 02015, Helsinki, Finland

    Arto Visala Prof

  3. National Centre for Engineering in Agriculture, University of Southern Queensland, 4350, Toowoomba, QLD, Australia

    Mark Dunn

Authors
  1. John Billingsley
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  2. Arto Visala Prof
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  3. Mark Dunn
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Corresponding authors

Correspondence to John Billingsley , Arto Visala Prof or Mark Dunn .

Editor information

Editors and Affiliations

  1. PRISMA Lab, Dipartimento di Informatica e Sistemistica, Universitá degli Studi di Napoli Federico II, 80125, Napoli, Italy, Via Claudio 21

    Bruno Siciliano Prof.

  2. Artificial Intelligence Laboratory, Department of Computer Science, Stanford University, 94305-9010, Stanford, CA, USA

    Oussama Khatib Prof.

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© 2008 Springer-Verlag

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Billingsley, J., Visala, A., Dunn, M. (2008). Robotics in Agriculture and Forestry. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30301-5_47

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  • DOI: https://doi.org/10.1007/978-3-540-30301-5_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23957-4

  • Online ISBN: 978-3-540-30301-5

  • eBook Packages: EngineeringEngineering (R0)

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