Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-23T10:15:06.242Z Has data issue: false hasContentIssue false
  • English
  • Français

Use of Optical Remote Sensing for Detecting Herbicide Injury in Soybean

Published online by Cambridge University Press:  20 January 2017

Kurt D. Thelen ,
A. N. Kravchenko  and
Chad D. Lee
Kurt D. Thelen*
Affiliation:
Department of Crop and Soil Science, Michigan State University, East Lansing, MI 48824-1325
A. N. Kravchenko
Affiliation:
Department of Crop and Soil Science, Michigan State University, East Lansing, MI 48824-1325
Chad D. Lee
Affiliation:
Department of Crop and Soil Science, Michigan State University, East Lansing, MI 48824-1325
*
Corresponding author's E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Experiments were conducted from 2000 to 2002 at two locations each year to determine if lactofen and imazethapyr injury to soybean could be detected using digital aerial imagery and ground-based optical remote sensing. Lactofen and imazethapyr were applied at base rates of 105 and 71 g/ha, respectively, and at 0, 2X, and 4X rates. Treated plots were evaluated between 7 and 21 d after treatment for crop injury using a ground-based radiometer and a system using computer analysis of digital aerial imagery. Both the ground-based radiometer and the digital aerial imagery were effective in detecting herbicide injury under most conditions. The digital aerial imagery system was found to be more sensitive in detecting herbicide injury than the ground-based radiometer system. Herbicide or herbicide rate had a significant effect on normalized differential vegetation indices (NDVI) derived from digital aerial imagery in four of four site-years. NDVI values derived from a multispectral ground-based radiometer were significant for herbicide or herbicide rate in four of six site-years. NDVI values from treated plots were subtracted from the NDVI value of the untreated check to generate a ΔNDVI. The resulting ΔNDVI values from the ground-based radiometer system were significant for herbicide or herbicide rate in six of six site-years. Neither optical remote-sensing system was effective at estimating actual application rates of lactofen and imazethapyr across a broad range of field and weather conditions due to temporal and spatial variability in crop response to the herbicides.

Keywords

Imazethapyrlactofensoybean, Glycine max (L.) Merr. ‘Asgrow 2001’, ‘Becks 243’, ‘Becks 323’, ‘Mycogen 5251’, ‘Pioneer 19B91’, ‘Pioneer 92B38’Aerial imagerylight reflectancevegetation indexFc, fractional coverLAI, leaf area indexNDVI, normalized differential vegetation indexNIR, near-infraredSVI, spectral vegetation index
Type
Research
Information
Weed Technology , Volume 18 , Issue 2 , June 2004 , pp. 292 - 297
Copyright
Copyright © 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

Adams, M. L., Norvell, W. A., Philpot, W. D., and Peverly, J. 2000. Spectral detection of micronutrient deficiency in ‘Bragg’ soybean. Agron. J. 92:261–268.Google Scholar
Adcock, T. E., Nutter, F. W., and Banks, P. A. 1990. Measuring herbicide injury to soybeans (Glycine max) using a radiometer. Weed Sci. 38:625–627.Google Scholar
Blackmer, A. M., Schepers, J. S., Varvel, G. E., and Walter-Shea, E. A. 1996. Nitrogen deficiency detection using reflected shortwave radiation from irrigated corn canopies. Agron. J. 88:1–5.Google Scholar
Donald, W. W. 1998. Estimated soybean (Glycine max) yield loss from herbicide damage using ground cover or rated stunting. Weed Sci. 46:454–458.Google Scholar
Felton, W. L., Alston, C. L., Haigh, B. M., Nash, P. G., Wicks, G. A., and Hanson, G. E. 2002. Using reflectance sensors in agronomy and weed science. Weed Technol. 16:520–527.Google Scholar
Jackson, R. D. and Printer, P. J. Jr. 1986. Spectral response of architecturally different wheat canopies. Remote Sens. Environ. 20:43–56.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
Nelson, K. A. and Renner, K. A. 2001. Effect of glyphosate and postemergence herbicides on soybean. Agron. J. 93:428–434.Google Scholar
Nelson, K. A., Renner, K. A., and Penner, D. 1998. Weed control in soybean (Glycine max) with imazamox and imazethapyr. Weed Sci. 46:587–594.CrossRefGoogle Scholar
Qi, J., Kerr, Y. H., Moran, M. S., Weltz, M., Huete, A. R., Sorooshian, S., and Bryant, R. 2000. Leaf area index estimates using remotely sensed data and BRDF Models in a semiarid region. Remote Sens. Environ. 73:18–30.Google Scholar
Scharf, P. C. and Lory, J. A. 2002. Calibrating corn color from aerial photographs to predict sidedress nitrogen need. Agron. J. 94:397–404.CrossRefGoogle Scholar
Shanahan, J. F., Schepers, J. S., Francis, D. D., Varvel, G. E., Willhelm, W. W., Tringe, J. M., Schlemmer, M. R., and Major, D. J. 2001. Use of remote-sensing imagery to estimate corn grain yield. Agron. J. 93:583–589.Google Scholar
Todd, L. A. and Kommedahl, T. 1994. Image analysis and visual estimates for evaluating disease reactions of corn to Fusarium stalk rot. Plant Dis. 78:876–878.Google Scholar
Tucker, C. T. 1979. Red and photographic infrared linear combination for monitoring vegetation. Remote Sens. Environ. 8:127–150.CrossRefGoogle Scholar
Wichert, R. A. and Talbert, R. E. 1993. Soybean [Glycine max (L.)] response to lactofen. Weed Sci. 41:23–27.Google Scholar
Wiegand, C. L., Richardson, A. J., Escobar, D. E., and Gerbermann, A. H. 1991. Vegetation indices in crop assessments. Remote Sens. Environ. 35:105–119.Google Scholar