Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-18T09:00:26.819Z Has data issue: false hasContentIssue false
  • English
  • Français

A Mobile Field Robot with Vision-Based Detection of Volunteer Potato Plants in a Corn Crop

Published online by Cambridge University Press:  20 January 2017

Frits K. Van Evert ,
Gerie W.A.M. Van Der Heijden ,
Lambertus A.P. Lotz ,
Gerrit Polder ,
Arjan Lamaker ,
Arjan De Jong ,
Marjolijn C. Kuyper ,
Eltje J.K. Groendijk ,
Jacques J. Neeteson  and
Ton Van Der Zalm
Frits K. Van Evert*
Affiliation:
Plant Research International, P.O. Box 16, 6700 AA Wageningen, The Netherlands
Gerie W.A.M. Van Der Heijden
Affiliation:
Plant Research International, P.O. Box 16, 6700 AA Wageningen, The Netherlands
Lambertus A.P. Lotz
Affiliation:
Plant Research International, P.O. Box 16, 6700 AA Wageningen, The Netherlands
Gerrit Polder
Affiliation:
Plant Research International, P.O. Box 16, 6700 AA Wageningen, The Netherlands
Arjan Lamaker
Affiliation:
Wageningen UR, Wageningen, The Netherlands
Arjan De Jong
Affiliation:
Center for Geo-Information, P.O. Box 47, 6700 AA Wageningen, The Netherlands
Marjolijn C. Kuyper
Affiliation:
Center for Geo-Information, P.O. Box 47, 6700 AA Wageningen, The Netherlands
Eltje J.K. Groendijk
Affiliation:
Plant Research International, P.O. Box 16, 6700 AA Wageningen, The Netherlands
Jacques J. Neeteson
Affiliation:
Plant Research International, P.O. Box 16, 6700 AA Wageningen, The Netherlands
Ton Van Der Zalm
Affiliation:
Plant Research International, P.O. Box 16, 6700 AA Wageningen, The Netherlands
*
Corresponding author's E-mail: frits.vanevert@wur.nl.
Get access Rights & Permissions [Opens in a new window]

Abstract

Volunteer potato is a perennial weed that is difficult to control in crop rotations. It was our objective to build a small, low-cost robot capable of detecting volunteer potato plants in a cornfield and thus demonstrate the potential for automatic control of this weed. We used an electric toy truck as the basis for our robot. We developed a fast row-recognition algorithm based on the Hough transform and implemented it using a webcam. We developed an algorithm that detects the presence of a potato plant based on a combination of size, shape, and color of the green elements in an image and implemented it using a second webcam. The robot was able to detect potatoes while navigating autonomously through experimental and commercial cornfields. In a first experiment, 319 out of 324 images were correctly classified (98.5%) as showing, or not showing, a potato plant. In a second experiment, 126 out of 141 images were correctly classified (89.4%). Detection of a potato plant resulted in an acoustic signal, but future robots may be fitted with weed control equipment, or they may use a global positioning system to map the presence of weed plants so that regular equipment can be used for control.

Keywords

Corn, Zea mays L, Potato, Solanum tuberosum L.Autonomous navigationautonomous weedingglyphosatemachine-visionsite-specific weed controlDIPlib, Delft image-processing libraryDSP, digital signal processorGPS, global positioning systemJPEG, Joint Photographic Experts GroupNiMh, nickle metal hydridePC, personal computer
Type
Research
Information
Weed Technology , Volume 20 , Issue 4 , December 2006 , pp. 853 - 861
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Anonymous 2004. Proceedings of the 2nd Field Robot Event. Farm Technology Group. Wageningen, The Netherlands Wageningen University.Google Scholar
Åstrand, B. and Baerveldt, A. J. 2002. An agricultural mobile robot with vision-based perception for mechanical weed control. Auton. Robot. 13:21–35.Google Scholar
Åstrand, B. and Baerveldt, A. J. 2003. A mobile robot for mechanical weed control. Int. Sugar J. 105:89–95.Google Scholar
Blackmore, B. S., Stout, W., Wang, M., and Runov, B. 2005. Robotic agriculture—the future of agricultural mechanisation? 5th European Conference on Precision Agriculture. Wageningen, The Netherlands Wageningen Academic.Google Scholar
Boydston, R. A. 2001. Volunteer potato (Solanum tuberosum) control with herbicides and cultivation in field corn (Zea mays). Weed Technol. 15:461–466.Google Scholar
Canny, A. 1986. A computational approach to edge detection. IEEE Trans. Pattern Anal. 8:769–698.Google Scholar
Gerhards, R., Nabout, A., Sokefeld, M., Kuhbauch, W., and Eldin, H. A. N. 1993. Automatic identification of 10 weed species in digital images using Fourier descriptors and shape-parameters. J. Agron. Crop Sci. 171:321–328.Google Scholar
Gerhards, R., Sökefeld, M., Kühbauch, W., and Nabout, A. 1995. Using an expert system and digital image processing for site-specific weed control. 9th International Symposium on Challenges for Weed Science in a Changing Europe. Doorwerth, The Netherlands European Weed Research Society. [In German.].Google Scholar
Hastie, T., Tibshirani, R., and Friedman, J. 2001. The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York Springer.Google Scholar
Hough, P. V. C. 1962. Method and means for recognizing complex patterns. USA Patent 3069654.Google Scholar
Reid, J. F., Zhang, Q., Noguchi, N., and Dickson, M. 2000. Agricultural automatic guidance research in North America. Comput. Electron. Agric. 25:155–167.Google Scholar
Schut, A. G. T. 2003. Imaging spectroscopy for characterisation of grass swards. Ph.D. thesis, Wageningen University Wageningen, The Netherlands.Google Scholar
Scotford, I. M. and Miller, P. C. H. 2005. Applications of spectral reflectance techniques in Northern European cereal production: a review. Biosys. Eng. 90:235–250.Google Scholar
Tillett, N. D., Hague, T., and Marchant, J. A. 1998. A robotic system for plant-scale husbandry. J. Agric. Eng. Res. 69:169–178.Google Scholar
Van der Heijden, F., Duin, R. P. W., De Ridder, D., and Tax, D. M. J. 2004. Classification, parameter estimation and state estimation—an engineering approach using MATLAB. New York J. Wiley.Google Scholar
Van Straten, G. 2004. Field Robot Event, Wageningen, 5–6 June 2003. Comput. Electron. Agric. 42:51–58.CrossRefGoogle Scholar
Zwiggelaar, R. 1998. A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops. Crop Prot. 17:189–206.Google Scholar