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Assessing the Reflective Characteristics of Palmer Amaranth (Amaranthus palmeri) and Pitted Morningglory (Ipomoea lacunosa) Accessions

Published online by Cambridge University Press:  20 January 2017

Cody J. Gray ,
David R. Shaw ,
Jason A. Bond ,
Daniel O. Stephenson IV  and
Lawrence R. Oliver
Cody J. Gray
Affiliation:
Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762
David R. Shaw*
Affiliation:
Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762
Jason A. Bond
Affiliation:
Mississippi State University, Delta Research and Extension Center, Stoneville, MS 38776
Daniel O. Stephenson IV
Affiliation:
University of Arkansas, Northeast Arkansas Research and Extension Center, Keiser, AR 72351
Lawrence R. Oliver
Affiliation:
Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701
*
Corresponding author's E-mail: [email protected]
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Abstract

A hand-held hyperspectral radiometer was used to measure differences in reflectance characteristics of 24 Palmer amaranth and 15 pitted morningglory accessions collected from the central and southern United States. A hyperspectral reflectance reading was collected from two mature leaves at 24 and 27 d after emergence (DAE) for each accession. Two analysis techniques, linear discriminant analysis and best spectral-band combination (BSBC) analysis, were performed using various vegetation indices, spectral bands, and individual wavelengths. Differentiation of individual accessions was difficult. Palmer amaranth accession classification accuracies were < 50% using both analysis techniques, except one accession collected in South Carolina (63%), when pooled over data acquisition dates. Pitted morningglory accession classification accuracies were also generally < 50%. Classification accuracies were higher using BSBC analysis at 24 DAE; however, at 27 DAE only one accession resulted in classification accuracy > 30%. These results suggest there are only slight reflectance differences within Palmer amaranth and pitted morningglory accessions. These differences may not be predictable based upon accession origin because of the genetic diversity of Palmer amaranth and pitted morningglory. However, differentiation between Palmer amaranth and pitted morningglory was 100%. Thus, spectral sensors used to differentiate between Palmer amaranth and pitted morningglory do not need to be calibrated for a particular region of the United States, and differentiation between these two species can be made using reflectance characteristics.

Keywords

Palmer amaranth,Amaranthus palmeri S. Wats. AMAPApitted morninggloryIpomoea lacunosa L. IPOLAClassification accuracieshyperspectral reflectanceremote sensingweed differentiation
Type
Weed Biology and Ecology
Information
Weed Science , Volume 55 , Issue 4 , August 2007 , pp. 293 - 298
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Agati, G., Mazzinghi, P., Fusi, F., and Ambrosini, I. 1995. The F 685/F 730 chlorophyll fluorescence ration as a tool in plant physiology: Response to physiological and environmental factors. J. Plant Physiol. 14:228–238.Google Scholar
Aitkenhead, M. J., Dalgetty, I. A., Mullins, C. E., McDonald, A. J. S., and Strachan, N. J. C. 2003. Weed and crop discrimination using image analysis and artificial intelligence methods. Comput. Electron. Agr\ic. 39:157–171.Google Scholar
Barber, L. T. 2004. Utilizing hyperspectral and multispectral remote sensing and geographic information systems to identify and differentiate weed and crop species. . Mississippi State, MS Mississippi State University.Google Scholar
Bond, J. A. and Oliver, L. R. 2006. Comparative growth of Palmer amaranth (Amaranthus palmeri) accessions. Weed Sci. 54:121–126.Google Scholar
Crippen, R. E. 1990. Calculating the vegetation index faster. Remote Sens. Environ. 34:71–73.CrossRefGoogle Scholar
Gitelson, A. A., Buschmann, C., and Lichtenthaler, H. K. 1999. The chlorophyll fluorescence ratio F 735/F 700 as an accurate measure of the chlorophyll content in plants. Remote Sens. Environ. 69:296–302.CrossRefGoogle Scholar
Gitelson, A. A., Kaufman, Y. J., and Merzlyak, M. N. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58:289–298.Google Scholar
Gitelson, A. A., Kaufman, Y. J., Stark, R., and Rundquist, D. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 80:76–87.Google Scholar
Gitelson, A. A., Merzlyak, M. N., and Chivkunova, O. B. 2001. Optical properties and nondestructive estimation of anthocyanin content in plant leaves. J. Photochem. Photobiol. B Biol. 74:38–45.2.0.CO;2>CrossRefGoogle ScholarPubMed
Hemming, J. and Rath, T. 2001. Computer-vision-based weed identification under field conditions using controlled lighting. J. Agric. Eng. Res. 78:233–243.Google Scholar
Henry, W. B., Shaw, D. R., Reddy, K. R., Bruce, L. M., and Tamhankar, H. D. 2004a. Remote sensing to detect herbicide drift on crops. Weed Technol. 18:358–368.Google Scholar
Henry, W. B., Shaw, D. R., Reddy, K. R., Bruce, L. M., and Tamhankar, H. D. 2004b. Spectral reflectance curves to distinguish soybean from common cocklebur (Xanthium strumarium) and sicklepod (Cassia obtusifolia) grown with varying soil moisture. Weed Sci. 52:788–796.Google Scholar
Henry, W. B., Shaw, D. R., Reddy, K. R., Bruce, L. M., and Tamhankar, H. D. 2004c. Remote sensing to distinguish soybean from weeds after herbicide application. Weed Technol. 18:594–604.Google Scholar
Hoagland, D. H. and Arnon, D. I. 1950. The water-culture method for growing plants without soil. 347:1–32. Berkeley, CA: California Agricultural Experiment Station Circular.Google Scholar
Horak, M. J. and Loughin, T. M. 2000. Growth analysis of four Amaranthus species. Weed Sci. 48:347–355.Google Scholar
Hunt, E. R. Jr. and Rock, B. N. 1989. Detection of changes in leaf water content using near- and middle-infrared reflectances. Remote Sens. Environ. 30:43–54.Google Scholar
Jordan, C. F. 1969. Derivation of leaf-area index from quality of light on the forest floor. Ecology. 50:663–666.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
Lillesand, T. M. and Keifer, R. W. 2000. Remote Sensing and Image Interpretation, 4th ed. New York J. Wiley. 724.Google Scholar
Mathur, A., Bruce, L. M., Cheriyadat, A. M., and Lin, H. H. 2003. Hyperspec—Analysis of handheld spectroradiometer data. Pages 342–344. in. Proceedings of the IEEE Geoscience and Remote Sensing Symposium, Volume 1. Los Alamitos, CA IEEE.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
Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B., and Rakitin, V. Y. 1999. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol. Plant. 106:135–141.Google Scholar
Rawlings, J. O., Pantula, S. G., and Dickey, D. A. 1998. Applied Regression Analysis: A Research Tool, 2nd ed. New York Springer-Verlag. 657.CrossRefGoogle 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
SAS 1988. SAS/STAT User's Guide. Release. 6.03 ed. Cary, NC SAS Institute Inc. 1028.Google Scholar
Senseman, S. A. and Oliver, L. R. 1993. Flowering patterns, seed production, and somatic polymorphism of three weed species. Weed Sci. 41:418–425.CrossRefGoogle Scholar
Stephenson, D. O. 4th 2004. Identification and characterization of pitted morningglory (Ipomoea lacunosa) ecotypes and biotypes. . Fayetteville, AR University of Arkansas.Google Scholar
Stephenson, D. O. 4th, Oliver, L. R., Burgos, N. R., and Gbur, E. E. Jr. 2006. Identification and characterization of pitted morningglory (Ipomoea lacunosa) ecotypes. Weed Sci. 54:78–86.Google Scholar
[SWSS] Southern Weed Science Society 1998. Weed Identification Guide. Champaign, IL SWSS.Google Scholar
Tucker, C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8:127–150.Google Scholar
VanGessel, M. J., Schroeder, J., and Westra, P. 1998. Comparative growth and development of four spurred anoda (Anoda cristata) accessions. Weed Sci. 46:91–98.Google Scholar
Vaughan, L. K., Ottis, B. V., Prazak-Havey, A. M., Bormans, C. A., Sneller, C., Chandler, J. M., and Park, W. D. 2001. Is all red rice found in commercial rice really Oryza sativa? Weed Sci. 49:468–476.Google Scholar
Wassom, J. J., Tranel, P. J., and Wax, L. M. 2002. Variation among U.S. accessions of common cocklebur (Xanthium strumarium). Weed Technol. 16:171–179.Google Scholar
Webster, T. M. 2000. Weed survey-southern states, grass crops subsection. Proc. South. Weed Sci. Soc. 53:247–274.Google Scholar
Webster, T. M. 2001. Weed survey-southern states, broadleaf crops subsection. Proc. South. Weed Sci. Soc. 54:244–259.Google Scholar