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Utility of Multispectral Imagery for Soybean and Weed Species Differentiation

Published online by Cambridge University Press:  20 January 2017

Cody J. Gray ,
David R. Shaw ,
Patrick D. Gerard  and
Lori M. Bruce
Cody J. Gray
Affiliation:
Department of Plant and Soil Science, Box 9555, Mississippi State University, Mississippi State, MS 39762
David R. Shaw*
Affiliation:
Department of Plant and Soil Science, Box 9555, Mississippi State University, Mississippi State, MS 39762
Patrick D. Gerard
Affiliation:
Experimental Statistics Unit, Box 9653, Mississippi State University, Mississippi State, MS 39762
Lori M. Bruce
Affiliation:
Department of Electrical and Computer Engineering, Box 9571, Mississippi State University, Mississippi State, MS 39762
*
Corresponding author's E-mail: [email protected].
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Abstract

An experiment was conducted to determine the utility of multispectral imagery for identifying soybean, bare soil, and six weed species commonly found in Mississippi. Weed species evaluated were hemp sesbania, palmleaf morningglory, pitted morningglory, prickly sida, sicklepod, and smallflower morningglory. Multispectral imagery was analyzed using supervised classification techniques based upon 2-class, 3-class, and 8-class systems. The 2-class system was designed to differentiate bare soil and vegetation. The 3-class system was used to differentiate bare soil, soybean, and weed species. Finally, the 8-class system was designed to differentiate bare soil, soybean, and all weed species independently. Soybean classification accuracies classified as vegetation for the 2-class system were greater than 95%, and bare soil classification accuracies were greater than 90%. In the 3-class system, soybean classification accuracies were 70% or greater. Classification of soybean decreased slightly in the 3-class system when compared to the 2-class system because of the 3-class system separating soybean plots from the weed plots, which was not done in the 2-class system. Weed classification accuracies increased as weed density or weeks after emergence (WAE) increased. The greatest weed classification accuracies were obtained once weed species were allowed to grow for 10 wk. Palmleaf morningglory and pitted morningglory classification accuracies were greater than 90% for 10 WAE using the 3-class system. Palmleaf morningglory and pitted morningglory at the highest densities of 6 plants/m2 produced the highest classification accuracies for the 8-class system once allowed to grow for 10 wk. All other weed species generally produced classification accuracies less than 50%, regardless of planting density. Thus, multispectral imagery has the potential for weed detection, especially when being used in a management system when individual weed species differentiation is not essential, as in the 2-class or 3-class system. However, weed detection was not obtained until 8 to 10 WAE, which is unacceptable in production agriculture. Therefore, more refined imagery acquisition with higher spatial and/or spectral resolution and more sophisticated analyses need to be further explored for this technology to be used early-season when it would be most valuable.

Keywords

Hemp sesbania, Sesbania exaltata (Raf.) Rydb. ex A. W. Hill SEBEXpalmleaf morningglory, Ipomoea wrightii Gray IPOWRpitted morningglory, Ipomoea lacunosa L. IPOLAprickly sida, Sida spinosa L. SIDSPsicklepod, Senna obtusifolia (L.) H. S. Irwin & Barnaby CASOBsmallflower morningglory, Jacquemontia tamnifolia (L.) Griseb. IAQTAsoybean, Glycine max (L.) Merr. ‘Asgrow 4403’Remote sensingspecies differentiationsupervised classificationweed-crop discrimination
Type
Weed Management—Techniques
Information
Weed Technology , Volume 22 , Issue 4 , December 2008 , pp. 713 - 718
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Anonymous 2001. Weed survey-southern states, broadleaf crops subsection. Proc. South. Weed Sci. Soc 54:244–259.Google Scholar
Barber, L. T. 2004. Utilizing hyperspectral and multispectral remote sensing and geographic information systems to identify and differentiate weed and crop species. Ph.D. Dissertation. Mississippi State, MS: Department of Plant and Soil Sciences, Mississippi State University. 97.Google Scholar
Cardina, J., Johnson, G. A., and Sparrow, D. H. 1997. The nature and consequences of weed spatial distribution. Weed Sci 45:364–373.Google Scholar
Carson, H. W., Lass, L. W., and Callihan, R. H. 1995. Detection of yellow hawkweed (Hieracium pratense) with high resolution multispectral digital imagery. Weed Technol 9:477–483.Google Scholar
Cordes, R. C. and Bauman, T. T. 1984. Morningglory competition in soybeans. Weed Sci 32:364–370.Google Scholar
Duda, R. O., Hart, P. E., and Stork, D. G. 2000. Pattern Classification. 2nd ed. New York: Wiley Interscience. 289.Google Scholar
Everitt, J. H. and DeLoach, C. J. 1990. Remote sensing of Chinese tamarisk (Tamarix chinensis) and associated vegetation. Weed Sci 38:273–278.CrossRefGoogle Scholar
Everitt, J. H., Escobar, D. E., Villarreal, R., Alaniz, M. A., and Davis, M. R. 1993. Integration of airborne video, global positioning system, and geographic information system technologies for detecting and mapping two woody legumes on rangelands. Weed Technol 7:981–987.CrossRefGoogle Scholar
Gibson, K. D., Dirks, R., Medlin, C. R., and Johnston, L. 2004. Detection of weed species in soybean using multispectral digital images. Weed Technol 18:742–749.Google Scholar
Goudy, H. J., Bennet, K. A., Brown, R. B., and Tardif, F. J. 2001. Evaluation of site-specific weed management using a direct-injection sprayer. Weed Sci 49:359–366.CrossRefGoogle Scholar
Hughes, J. S., Evans, D. L., Burns, P. Y., and Hill, J. M. 1986. Identification of two southern pine species in high-resolution aerial MSS data. Photogramm. Eng. Remote Sens 52:1175–1180.Google Scholar
Koger, C. H., Shaw, D. R., Watson, C. E., and Reddy, K. N. 2003. Detecting late-season weed infestations in soybean (Glycine max). Weed Technol 17:696–704.Google Scholar
Lamb, D. W. and Brown, R. B. 2001. Remote-sensing and mapping of weeds in crops. J. Agric. Eng. Res 78:117–125.CrossRefGoogle Scholar
Lass, L. W. and Prather, T. S. 2004. Detecting the locations of Brazilian pepper trees in the Everglades with a hyperspectral sensor. Weed Technol 18:437–442.Google Scholar
Lass, L. W., Shafii, B., Price, W. J., and Thill, D. C. 2000. Assessing agreement in multispectral images of yellow starthistle (Centaurea solstitialis) with ground truth data using a Bayesian methodology. Weed Technol 14:539–544.Google Scholar
Lillesand, T. M. and Kiefer, R. W. 2000. Remote Sensing and Image Interpretation. 4th ed. New York: John Wiley and Sons. 750.Google Scholar
Medlin, C. R., Shaw, D. R., Gerard, P. D., and LaMastus, F. E. 2000. Using remote sensing to detect weed infestations in Glycine max . Weed Sci 48:393–398.CrossRefGoogle Scholar
Rankins, A. Jr., Shaw, D. R., and Byrd, J. D. Jr. 1998. HERB and MSU-HERB field validation for soybean (Glycine max) weed control in Mississippi. Weed Technol 12:88–96.Google Scholar
Richardson, A. J., Menges, R. M., and Nixon, P. R. 1985. Distinguishing weed from crop plants using video remote sensing. Photogramm. Eng. Remote Sens 51:1785–1790.Google Scholar
Shurtleff, J. L. and Coble, H. D. 1993. Interference of certain broadleaf weed species in soybeans (Glycine max). Weed Sci 33:654–657.CrossRefGoogle Scholar
Smith, A. M. and Blackshaw, R. E. 2003. Weed-crop discrimination using remote sensing: A detached leaf experiment. Weed Technol 17:811–820.CrossRefGoogle Scholar
[SWSS] Southern Weed Science Society, 1998. Weed Identification Guide. Champaign, IL: SWSS. 600.Google Scholar
Thornton, P. K., Fawcett, R. H., Dent, J. B., and Perkins, T. J. 1990. Spatial weed distribution and economic thresholds for weed control. Crop Prot 9:337–342.Google Scholar