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Detection of pitted morningglory (Ipomoea lacunosa) by hyperspectral remote sensing. I. Effects of tillage and cover crop residue

Published online by Cambridge University Press:  20 January 2017

Clifford H. Koger ,
David R. Shaw ,
Krishna N. Reddy  and
Lori M. Bruce
David R. Shaw
Affiliation:
Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762
Krishna N. Reddy
Affiliation:
Southern Weed Science Research Unit, USDA-ARS, Stoneville, MS 38776
Lori M. Bruce
Affiliation:
Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762
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Abstract

Field experiments were conducted to evaluate the potential of hyperspectral reflectance data collected with a hand-held spectroradiometer to discriminate soybean intermixed with pitted morningglory and weed-free soybean in conventional till and no-till plots containing rye, hairy vetch, or no cover crop residue. Pitted morningglory was in the cotyledon to six-leaf growth stage. Seven 50-nm spectral bands (one ultraviolet, two visible, four near-infrared) derived from each hyperspectral reflectance measurement were used as discrimination variables. Pitted morningglory plant size had more influence on discriminant capabilities than tillage or cover crop residue systems. Across all tillage and residue systems, discrimination accuracy was 71 to 95%, depending on the size of pitted morningglory plants at the time of data acquisition. The versatility of the seven 50-nm bands was tested by using a discriminant model developed for one experiment location to test discriminant capabilities for the other experiment, with discrimination accuracy across all tillage and residue systems of 55 to 73%, depending on pitted morningglory plant size.

Keywords

Hairy vetch, Trifolium incarnatum L.pitted morningglory, Ipomoea lacunosa L. IPOLArye, Secale cereale L.soybean, Glycine max (L.) Merr. ‘Asgrow 4702RR’Conventional tillagediscriminant analysisno tillageremote sensing
Type
Weed Biology and Ecology
Information
Weed Science , Volume 52 , Issue 2 , April 2004 , pp. 222 - 229
Copyright
Copyright © Weed Science Society of America 

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Footnotes

Present address: Southern Weed Science Research Unit, USDA-ARS, Stoneville, MS 38776; [email protected]

References

Literature Cited

Aase, J. K. and Tanaka, D. L. 1984. Effects of tillage practices on soil and wheat spectral reflectances. Agron. J 76:814–818.Google Scholar
Barrentine, W. L. 1974. Common cocklebur competition in soybeans. Weed Sci 22:600–603.Google Scholar
Bloomberg, J. R., Kirkpatrick, B. L., and Wax, L. M. 1982. Competition of common cocklebur (Xanthium pensylvanicum) with soybean (Glycine max). Weed Sci 30:507–513.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
Carter, G. A. 1998. Reflectance bands and indices for remote estimation of photosynthesis and stomatal conductance in pine canopies. Remote Sens. Environ 63:61–72.Google Scholar
Cousens, R. D. and Woolcock, J. L. 1987. Spatial dynamics of weeds. Pages 613–618 in Proceedings of the Brighton Crop Protection Conference—Weeds. Farham, UK: British Crop Protection Council.Google Scholar
Daughtry, C. S. T., McMurtrey, J. E. III, Chappelle, E. W., Dulaney, W. P., Irons, J. R., and Satterwhite, M. B. 1995. Potential for discriminating crop residues from soil by reflectance and fluorescence. Agron. J 87:165–171.Google Scholar
Elvidge, C. D. and Lyon, R. J. P. 1985. Influence of rock-soil spectral variation on the assessment of green biomass. Remote Sens. Environ 17:265–269.Google Scholar
Franz, E. M., Gebhardt, R., and Unklesbay, K. B. 1991. The use of local spectral properties of leaves as an aid for identifying weed seedlings in digital images. Trans. Am. Soc. Agric. Eng 34:682–687.Google Scholar
Gates, D. M., Keegan, H. J., Schleter, J. C., and Weidner, V. R. 1965. Spectral properties of plants. Appl. Optics 4:11–20.Google Scholar
Goetz, A. F., Rock, B. N., and Rowan, L. C. 1983. Remote sensing for exploration: an overview. Econ. Geol. Bull. Soc. Econ. Geol 78:573–590.Google Scholar
Haertel, V. and Landgrebe, D. 1999. On the classification of classes with nearly equal spectral response in remote sensing hyperspectral image data. IEEE Trans. Geosci. Remote Sens 37:2374–2386.Google Scholar
Hatfield, J. L. and Pinter, P. J. 1993. Remote sensing for crop protection. Crop Prot 12:403–413.Google Scholar
Huete, A. R., Jackson, R. D., and Post, D. F. 1985. Spectral response of a plant canopy with different soil backgrounds. Remote Sens. Environ 17:37–53.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.CrossRefGoogle Scholar
Medlin, C. R., Shaw, D. R., Cox, M. S., Gerard, P. D., Abshire, M. J., and Wardlaw, M. C. 2001. Using soil parameters to predict weed infestations in soybean. Weed Sci 49:367–374.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.Google Scholar
Menges, R. M., Nixon, P. R., and Richardson, A. J. 1985. Light reflectance and remote sensing of weeds in agronomic and horticulture crops. Weed Sci 33:569–581.Google Scholar
Moran, S. M., Clarke, T. R., Inoue, Y., and Vidal, A. 1994. Estimating crop water deficit using the relationship between surface-air temperature and spectral vegetation index. Remote Sens. Environ 49:246–263.Google Scholar
Nelson, R. F., Latty, R. S., and Mott, G. 1984. Classifying northern forests using thermatic mapper simulator data. Photogram. Eng. Remote Sens 50:607–617.Google Scholar
Penuelas, J., Filella, I., Biel, C., Serrano, L., and Save, R. 1993. The reflectance at the 950–970 region as an indicator of plant water stress. Int. J. Remote Sens 14:1887–1905.Google 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. Photogram. Eng. Remote Sens 51:1785–1790.Google Scholar
Shibayama, M. and Akiyama, T. 1991. Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements. Remote Sens. Environ 36:45–53.Google Scholar
Thenkabail, P. S., Smith, R. B., and Pauw, E. D. 2000. Hyperspectral vegetation indices and their relationship with agricultural crop characteristics. Remote Sens. Environ 71:158–182.CrossRefGoogle Scholar
Van Groenendael, J. M. 1988. Patchy distribution of weeds and some implications for modeling population dynamics: a short literature review. Weed Res 28:437–441.Google Scholar
Wiles, L. J., Wilkerson, G. G., and Gold, H. J. 1992. Value of information about weed distributions for improving postemergence control decisions. Crop Prot 11:547–553.Google Scholar