Airborne multispectral identification of individual cotton plants using consumer-grade cameras

Although multispectral remote sensing using consumer-grade cameras has successfully identified fields of small cotton plants, improved detection sensitivity is needed to identify individual or small clusters of cotton plants that also can provide habitat for boll weevils. The imaging sensor of consumer-grade cameras is based on a Bayer pattern, which alternates red, green, and blue filters over individual sensor pixels. However, each pixel of the imaging sensor of consumer-grade cameras represents direct measurement of only one of the three spectral bands (red, green, and blue) and interpolation of the remaining two spectral bands. We present an analytical technique in which endmember sets were derived from bimodal histograms of each spectral band for cotton, other vegetation types and soil, and linear spectral unmixing was used to identify individual cotton plants. We achieved significant misclassification rates as low as 0.125 and 0.146 in frequently tilled plots for validation tests of remote sensing identification of volunteer okraleaf cotton plants and volunteer conventional cotton plants, respectively. Results of this study indicate that consumer-grade cameras can acquire multispectral images of sufficient quality to detect individual cotton plants at an early growth stage, which will aid boll weevil eradication programs in identifying and locating volunteer plants.