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Utility of Hyperspectral Reflectance for Differentiating Soybean (Glycine max) and Six Weed Species
- Cody J. Gray, David R. Shaw, Lori M. Bruce
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- Journal:
- Weed Technology / Volume 23 / Issue 1 / March 2009
- Published online by Cambridge University Press:
- 20 January 2017, pp. 108-119
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- Article
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Reflectance data were subjected to a variety of analysis methods to determine the utility of hyperspectral reflectance for differentiating soybean, soil, and six weed species commonly found in Mississippi agricultural fields. Weed species evaluated were hemp sesbania, palmleaf morningglory, pitted morningglory, prickly sida, sicklepod, and smallflower morningglory. Hyperspectral reflectance data were collected from mature plant leaves three times in 2002 and two times in 2003. Vegetation indices were calculated and subjected to principal component analysis (PCA) and linear discriminant analysis (LDA). The PCA, using vegetation indices, produced the poorest classification accuracies for the plant species studied, generally less than 50%, whereas LDA resulted in classification accuracies greater than those from PCA. Best spectral band combination (BSBC) provided the greatest classification accuracies, with all better than 80% for all data sets. The BSBC indicated three wavelength bands of interest for species discrimination in the short wavelength infrared portion of the electromagnetic spectrum, which are not commonly used in current vegetation indices for species differentiation. These areas of interest were located from 1,445 to 1,475 nm, 2,030 to 2,090 nm, and 2,115 to 2,135 nm. The top 10 wavelengths determined by BSBC were then added to the vegetation indices and reanalyzed using PCA and LDA. Classification accuracies increased for all species when these wavelengths were added rather than using vegetation indices alone, suggesting greater crop and weed species differentiation can be obtained when using sensors that include these wavelength regions of the short wavelength infrared portion of the electromagnetic spectrum.
Utility of Multispectral Imagery for Soybean and Weed Species Differentiation
- Cody J. Gray, David R. Shaw, Patrick D. Gerard, Lori M. Bruce
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- Journal:
- Weed Technology / Volume 22 / Issue 4 / December 2008
- Published online by Cambridge University Press:
- 20 January 2017, pp. 713-718
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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.