Wavelet analysis of hyperspectral reflectance data for detecting pitted morningglory (Ipomoea lacunosa) in soybean (Glycine max)

This research determined the potential for wavelet-based analysis of hyperspectral reflectance signals for detecting the presence of early season pitted morningglory when intermixed with soybean and soil. Ground-level hyperspectral reflectance signals were collected in a field experiment containing plots of soybean and plots containing soybean intermixed with pitted morningglory in a conventional tillage system. The collected hyperspectral signals contained mixed reflectances for vegetation and background soil in each plot. Pure reflectance signals were also collected for pitted morningglory, soybean, and bare soil so that synthetically mixed reflectance curves could be generated, evaluated, and the mixing proportions controlled. Wavelet detail coefficients were used as features in linear discriminant analysis for automated discrimination between the soil + soybean and the soil + soybean + pitted morningglory classes. A total of 3 6 different mother wavelets were investigated to determine the effect of mother wavelet selection on the ability to detect the presence of pitted morningglory. When the growth stage was two to four leaves, which is still controllable with herbicide, the weed could be detected with at least 87% accuracy, regardless of mother wavelet selection. Moreover, the Daubechies 3, Daubechies 5, and Coiflet 5 mother wavelets resulted in 100% classification accuracy. Most of the best wavelet coefficients, in terms of discriminating ability, were derived from the red-edge and the near-infrared regions of the spectrum. For comparison purposes, the raw spectral bands and principal components were evaluated as possible discriminating features. For the two-leaf to four-leaf weed growth stage, the two methods resulted in classification accuracies of 83% and 81%, respectively. The wavelet-based method was shown to be very promising in detecting the presence of early season pitted morningglory in mixed hyperspectral reflectances. (C) 2003 Elsevier Science Inc. All rights reserved.