Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-26T22:02:58.694Z Has data issue: false hasContentIssue false
  • English
  • Français

Factors affecting the operation of the weed-sensing Detectspray system

Published online by Cambridge University Press:  12 June 2017

Robert E. Blackshaw ,
Louis J. Molnar ,
Duane F. Chevalier  and
C. Wayne Lindwall
Robert E. Blackshaw*
Affiliation:
Research Centre, Agriculture and Agri-Food Canada, Lethbridge, Alberta, Canada T1J 4B1
Louis J. Molnar
Affiliation:
Research Centre, Agriculture and Agri-Food Canada, Lethbridge, Alberta, Canada T1J 4B1
Duane F. Chevalier
Affiliation:
Research Centre, Agriculture and Agri-Food Canada, Lethbridge, Alberta, Canada T1J 4B1
C. Wayne Lindwall
Affiliation:
Research Centre, Agriculture and Agri-Food Canada, Lethbridge, Alberta, Canada T1J 4B1
*
Corresponding author. [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Field experiments were conducted for 3 yr to determine the effect of various biological and physical factors on the operation of the weed-sensing Detectspray system. Plant detection is achieved by sensors measuring differential reflectance of red and near-infrared wavelengths of light from green plants, crop residues, and soil. Weed detection was greatly reduced 70 to 80 min after sunrise and before sunset when operated at lat 50°N because of reduced solar irradiance. Tall, dense-standing crop stubble limited detection of small weeds at the soil surface. Weed detection varied with plant species. Canola with three to four leaves consistently was detected, but wheat or green foxtail usually required five to six leaves to be detected. Small weeds were detected if present at densities greater than 70 plants m−2. Growers and commercial applicators need to be aware of the limitations of the Detectspray system to use it effectively to control weeds with concurrent reductions in herbicide use.

Keywords

Green foxtail, Setaria viridis (L.) Beauv. SETVIcanola, Brassica napus L.wheat, Triticum aestivum L.Infrared sensorreduced herbicide useselective sprayerweed managementAMAREKCHSCSETVI
Type
Weed Management
Information
Weed Science , Volume 46 , Issue 1 , February 1998 , pp. 127 - 131
Copyright
Copyright © 1998 by the 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

Ahrens, W. H. 1994. Relative costs of a weed-activated versus conventional sprayer in northern Great Plains fallow. Weed Technol. 8: 50–57.Google Scholar
Anonymous. 1993. Detectspray User Manual. Fargo, ND: Concord. 31 p.Google Scholar
Duff, P. 1993. Detectspray system. Weed Sci. Soc. Am. Abstr. 33: 45.Google Scholar
Felton, W. L. 1990. Use of Weed Detection for Fallow Weed Control. Conservation Tillage. Brookings, SD: Great Plains Agricultural Council Bull. 131. pp. 241–244.Google Scholar
Felton, W. L., Doss, A. F., Nash, P. G., and McCloy, K. R. 1991. A microprocessor controlled technology to selectively spot spray weeds. Pages 427–431 in Automated Agriculture for the 21st Century Symposium. American Society of Agricultural Engineering. Fargo, ND: Concord.Google Scholar
Hanson, G. E., Wicks, G. A., and Kappler, B. F. 1994. Successes and failures with the Detectspray. Proc. N. Cent. Weed Sci. Soc. 49: 71–81.Google Scholar
[SAS] Statistical Analysis Systems. 1989. SAS/STAT User's Guide. Version 6, 4th ed., Volume 2. Cary, NC: Statistical Analysis Systems Institute. 846 p.Google Scholar
Steel, R.G.D. and Torrie, J. H. 1980. Pages 336–376 in Principles and Procedures of Statistics: A Biometrical Approach. 2nd ed. New York: McGraw-Hill.Google Scholar