Advertisement

Precision Agriculture

, Volume 7, Issue 1, pp 21–32 | Cite as

Automated Crop and Weed Monitoring in Widely Spaced Cereals

  • T. HagueEmail author
  • N. D. Tillett
  • H. Wheeler
Article

Abstract

An approach is described for automatic assessment of crop and weed area in images of widely spaced (0.25 m) cereal crops, captured from a tractor mounted camera. A form of vegetative index, which is invariant over the range of natural daylight illumination, was computed from the red, green and blue channels of a conventional CCD camera. The transformed image can be segmented into soil and vegetative components using a single fixed threshold. A previously reported algorithm was applied to robustly locate the crop rows. Assessment zones were automatically positioned; for crop growth directly over the crop rows, and for weed growth between the rows. The proportion of crop and weed pixels counted was compared with a manual assessment of area density on the basis of high resolution plan view photographs of the same area; this was performed for views with a range of crop and weed levels. The correlation of the manual and automatic measures was examined, and used to obtain a calibration for the automatic approach. The results of mapping of a small field, at two times, are presented. The results of the automated mapping appear to be consistent with manual assessment.

Keywords

Field mapping Image analysis Cereals 

Notes

Acknowledgements

This work was supported by the Biotechnology and Biological Sciences Research Council.

References

  1. Blair AM, Jones P, Orson JH (1997) Integration of row widths, chemical and mechanical weed control and the effect on winter wheat yield. Asp Appl Biol 50:385–392Google Scholar
  2. Blum H, Nagel RN (1978) Shape description using weighted symmetric axis features. Pattern Recogn 10:167–180CrossRefGoogle Scholar
  3. Blum H (1973) Biological shape and visual science. J Theor Biol 38:205–287CrossRefGoogle Scholar
  4. Gerhards R, Christensen S (2003) Real-time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley. Weed Res 43(6):385–392CrossRefGoogle Scholar
  5. Hague T, Tillett ND (2001) A bandpass filter based approach to crop row location and tracking. Mechatronics 11(1):1–12CrossRefGoogle Scholar
  6. Jacquemoud S, Ustin SL, Verdebout J, Schmuck G, Andreoli G, Hosgood B (1996) Estimating leaf biochemistry using the PROSPECT leaf optical properties model. Remote Sensing Environ 56(3):194–202Google Scholar
  7. Manh A-G, Rabatel G, Assémat L, Aldon M-J (2001) Weed leaf image segmentation by deformable templates. J Agric Eng Res 80(2):139–146CrossRefGoogle Scholar
  8. Marchant JA, Onyango CM (2002) Shadow-invariant classification for scenes illuminated by daylight. J Opt Soc Am A 17(11):1952–1961Google Scholar
  9. Marchant JA, Tillett ND, Onyango CM (2004) Dealing with colour changes caused by natural illumination in outdoor machine vision. Cybernetics Syst 35(1)Google Scholar
  10. Paice MER, Miller PCH, Bodle J (1995) An experimental sprayer for the spatially selective application of herbicides. J Agri Eng Res 60(2):107–116CrossRefGoogle Scholar
  11. Perez AJ, Lopez F, Benlloch JV, Christensen S (2000) Colour and shape analysis techniques for weed detection in cereal fields. Comp Electron Agric 25(3):197–212Google Scholar
  12. Philipp I, Rath T (2002) Improving plant descrimination in image processing by use of different colour space transformations. Comp Electron Agric 35(1):1–15Google Scholar
  13. Robert PC (1999) Precision agriculture: research needs and status in the USA. In: Stafford JV (ed) Proceedings of the 2nd European conference on Precision Agriculture, Sheffield Academic Press, Sheffield, UK, pp 19–33Google Scholar
  14. Slaughter DC, Chen P, Curley RG (1997) Computer vision guidance system for precision cultivation. Paper no. 97-1079, ASAE, St Joseph, MI, USAGoogle Scholar
  15. Sogaard HT, Olsen HJ (2003) Determination of crop rows by image analysis without segmentation. Comp Electron Agric 38(2):141–158Google Scholar
  16. Tian LF, Slaughter DC (1998) Environmentally adaptive segmentation algorithm for outdoor image segmentation. Comp Electron Agric 21(3):153–168Google Scholar
  17. Tottman DR (1987) The decimal code for the growth stages of cereals, with illustrations. Ann Appl Biol 110(2):441–454Google Scholar
  18. Vioix J-B, Douzals J-P, Truchetet F, Assémat L, Guillemin J-P (2002) Spatial and Spectral methods for weed detection and localization. EURASIP J Appl Signal Procassing 7:679–685Google Scholar
  19. Vrindts E, de Baerdemaeker J (1997) Optical discrimination of crop, weeds and soil for on-line weed detection. In: Stafford JV (ed) Proceedings of the 1st Conference on Precision Agriculture, Bios Scientific Publishers Ltd, Oxford, UK, pp 537–544Google Scholar
  20. Woebbecke DM, Meyer GE, von Bargen K, Mortensen D (1992) Plant species identification, size, and enumeration using machine vision techniques on near-binary images. SPIE Opt Agric Forest 1836:208–219Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Tillett and Hague Technology LtdSilsoeUK

Personalised recommendations