Skip to main content
SpringerLink
Log in

Development of an Image Processing System and a Fuzzy Algorithm for Site-Specific Herbicide Applications

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

In precision farming, image analysis techniques can aid farmers in the site-specific application of herbicides, and thus lower the risk of soil and water pollution by reducing the amount of chemicals applied. Using weed maps built with image analysis techniques, farmers can learn about the weed distribution within the crop. In this study, a digital camera was used to take a series of grid-based images covering the soil between rows of corn in a field in southwestern Quebec in May of 1999. Weed coverage was determined from each image using a “greenness method” in which the red, green, and blue intensities of each pixel were compared. Weed coverage and weed patchiness were estimated based on the percent of greenness area in the images. This information was used to create a weed map. Using weed coverage and weed patchiness as inputs, a fuzzy logic model was developed for use in determining site-specific herbicide application rates. A herbicide application map was then created for further evaluation of herbicide application strategy. Simulations indicated that significant amounts of herbicide could be saved using this approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Ambuel, J. R., Steenhoek, L., Smith, R. and Colvin, T. 1993. Control of hydrostatic transmission output speed: development and comparison of PI and hybrid fuzzy-PI controllers. Transactions of the ASAE 36, 1057-1064.

    Google Scholar 

  • Andreasen, C., Rudemo, M. and Sevestre, S. 1997. Assessment of weed density at an early stage by use of image processing. Weed Research 37, 5-18.

    Google Scholar 

  • Ascheman, R. E. 1993. Some practical field applications. In: Soil Specific Crop Management, edited by P. C. Robert, R. H. Rust, and W. E. Larson (American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, Madison, WI, USA), p. 79-86.

    Google Scholar 

  • Blackmer, T. M. and Schepers, J. S. 1996. Using DGPS to improve corn production and water quality. GPS World 7, 44-52.

    Google Scholar 

  • Blackmore, S. 1994. Precision farming: an introduction. Outlook on Agriculture 23, 275-280.

    Google Scholar 

  • Cardina, J., Johnson, G. A. and Sparrow, D. H. 1997. The nature and consequence of weed spatial distribution. Weed Science 45, 364-373.

    Google Scholar 

  • Donald, W. W. 2000. Alternative ways to control weeds between rows in weeded check plots in corn (Zea mays) and Soybean (Glycine max). Weed Technology 14, 36-44.

    Google Scholar 

  • Franz, E., Gebhardt, M. R. and Unklesbay, K. B. 1991. The use of local spectral properties of leaves as an aid for identifying weed seedlings in digital images. Transactions of the ASAE 34, 682-687.

    Google Scholar 

  • Hartzler, R. G. 1997. Velvetleaf (Abutilon theophrasti) interference in soybean (Glycine max): a survey of yield loss estimates and management recommendations. Crop Protection 16, 483-485.

    Google Scholar 

  • Heske, T. and Heske, J. N. 1996. Fuzzy Logic for Real World Design (Annabooks, San Diego, CA, USA).

    Google Scholar 

  • Hemming, J. and Rath, T. 2001. Computer-vision-based weed identification under field conditions using controlled lighting. Journal of Agricultural Engineering Research 78, 233-243.

    Google Scholar 

  • Kasabov, N. K. 1996. Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering (The MIT Press, Cambridge, MA, USA).

    Google Scholar 

  • Lacroix, R., Huijbers, J., Tiemessen, R., Lefebvre, D., Marchand, D. and Wade, K. M. 1998. Fuzzy set-based analytical tools for dairy herd improvement. Applied Engineering in Agriculture 14, 79-85.

    Google Scholar 

  • Lindquist, J. L., Mortensen, D. A., Westra, P., Lambert, W. J., Bauman, T. T., Fausey, J. C., Kells, J. J., Langton, S. J., Harvey, R. G., Bussler, B. H., Banken, K., Clay, S. and Forcella, F. 1999. Stability of corn (Zea mays)-foxtail (Setaria spp.) interference relationships. Weed Science 47, 195-200.

    Google Scholar 

  • Lussier, S. 1999. Personal communication (Assistant Director of Farm Management Technology Program, Macdonald Campus, McGill University, Canada).

  • Mannion, A. M. 1995. Agriculture and Environmental Change: Temporal and Spatial Dimensions (Wiley & Sons, Inc., New York, NY, USA).

    Google Scholar 

  • MathWorks. 1998a. Fuzzy Logic Toolbox User's Guide, for Use with MATLAB (The MathWorks, Inc., Natick, MA, USA).

    Google Scholar 

  • MathWorks. 1998b. Image Processing Toolbox User's Guide, for Use with MATLAB (The MathWorks, Inc., Natick, MA, USA).

    Google Scholar 

  • MathWorks. 1998c. Using MATLAB (The MathWorks, Inc., Natick, MA, USA).

    Google Scholar 

  • Medlin, C. R. and Shaw, D. R. 2000. Economic comparison of broadcast and site-specific herbicide applications in nontransgenic and glyphosate-tolerant-Glycine max. Weed Science 48, 653-661.

    Google Scholar 

  • Meyer, G. E., Mehta, T., Kocher, M. F., Mortensen, D. A. and Samal, A. 1998. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Transactions of the ASAE 41, 1189-1197.

    Google Scholar 

  • Perez, A. J., Lopez, F., Benlloch, J. V. and Christensen, S. 2000. Color and shape analysis techniques for weed detection in cereal fields. Computers and Electronics in Agriculture 25, 197-212.

    Google Scholar 

  • Perret, J. S. and Prasher, S. O. 1998. Applications of fuzzy logic in the design of vegetated waterways under uncertainty. Journal of the American Water Resources Association 34, 1355-1367.

    Google Scholar 

  • Scholes, C., Clay, S. A. and Brix-Davis, K. 1995. Velvetleaf (Abutilon theophrasti) effect on corn (Zea mays) growth and yield in South Dakota. Weed Technology 9, 665-668.

    Google Scholar 

  • Shaw, D. R., Rankins, Jr., A., Ruscoe, J. T. and Byrd, Jr., J. D. 1998. Field validation of weed control recommendations from HERB and SWC herbicide recommendation models. Weed Technology 12, 78-87.

    Google Scholar 

  • Smith, Jr., S., Schreiber, J. D. and Cullum, R. F. 1995. Upland soybean production: surface and shallow groundwater quality as affected by tillage and herbicide use. Transactions of the ASAE 38, 1061-1068.

    Google Scholar 

  • Stafford, J. V. and Benlloch, J. V. 1997. Machine-assisted detection of weeds and weed patches. In: Precision Agriculture '97: Proceedings of the 1 st European Conference on Precision Agriculture, Volume II. Technology, IT and Management, edited by J. V. Stafford, (SCI Bios Scientific Publishers), p. 511-518.

  • Stoller, E. W., Wax, L. M. and Slife, F. W. 1979. Yellow nutsedge (Cyperus esculentus) competition and control in corn (Zea mays). Weed Science 27, 32-37.

    Google Scholar 

  • Tan, J. and Chang, Z. 1994. Linearity and a tuning procedure for fuzzy logic controllers. Transactions of the ASAE 37, 973-979.

    Google Scholar 

  • Tian, L., Reid, J. F. and Hummel, J. W. 1999. Development of a precision sprayer for site-specific weed management. Transactions of the ASAE 42, 893-900.

    Google Scholar 

  • Tyler, D. A. 1993. Positioning technology (GPS). In: Soil Specific Crop Management, edited by P. C. Robert, R. H. Rust, and W. E. Larson, (American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, Madison, WI, USA), p. 159-165.

    Google Scholar 

  • Vangessel, M. J., Schweizer, E. E., Garrett, K. A. and Westra, P. 1995. Influence of weed density and distribution on corn (Zea mays) yield. Weed Science 43, 215-218.

    Google Scholar 

  • Yen, J., Langari, R. and Zadeh, L. A. 1995. Industrial Applications of Fuzzy Logic and Intelligent Systems (The Institute of Electrical and Electronics Engineers, Inc., New York, NY, USA).

    Google Scholar 

  • Zadeh, L. A. 1965. Fuzzy sets. Information and Control 8, 338-353.

    Google Scholar 

  • Zanin, G., Berti, A. and Riello, L. 1998. Incorporation of weed spatial variability into the weed control decision-making process. Weed Research 38, 107-118.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Postdoctoral Fellow, Department of Agricultural and Biosystems Engineering, McGill University, Ste-Anne-de-Bellevue, QC, Canada

    Chun-Chieh Yang

  2. Department of Agricultural and Biosystems Engineering, McGill University, Ste-Anne-de-Bellevue, QC, Canada

    Shiv O. Prasher (Professor)

  3. Department of Agricultural and Biosystems Engineering, McGill University, Ste-Anne-de-Bellevue, QC, Canada

    Jacques-André Landry (Assistant Professor)

  4. Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada

    Hosahalli S. Ramaswamy

Authors
  1. Chun-Chieh Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shiv O. Prasher
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jacques-André Landry
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hosahalli S. Ramaswamy
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yang, CC., Prasher, S.O., Landry, JA. et al. Development of an Image Processing System and a Fuzzy Algorithm for Site-Specific Herbicide Applications. Precision Agriculture 4, 5–18 (2003). https://doi.org/10.1023/A:1021847103560

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1021847103560

Search

Navigation