Use of airborne multi-spectral imagery for weed detection in field crops

In this article, the potential of multi-spectral airborne remote sensing is evaluated for the detection of weed infestation in corn (Zea mays L) and soybean (Glycine max.) crops. Afield plot experiment was laid out at the Lods Agronomy Research Center of Macdonald Campus, McGill University,, Ste-Anne-de-Bellevue, Quebec, Canada. A multi-spectral image in 24 wavebands (475.12 nm to 910.01 nm wavelength range) was obtained using all airborne platform. Three weed treatments were selected to represent different weed conditions in corn and soybean, namely velvetleaf (Abutilon theophrasti Medic.), grasses, and mixed weeds. For the purpose of comparison, a treatment without weeds was also planted of each type of crop. Statistical analysis of radiance values recorded in different wavebands was performed to find the wavelength regions that were most useful for detecting different weed infestations. The results indicate that wavebands centered at 675.98 and 685.17 nm in the red region, and from 743.93 nm to 830.43 nm in the near-infrared, have good potential or distinguishing weeds in corn. For soybean, however, only one waveband (811.40 nm) was found to be useful. Efforts were also made to evaluate various ratios of radiance values recorded in red and near infrared (NIR) wavebands for the detection of weeds. Much better results were obtained when ratios were used than with single wavebands. The results of this study will be helpful in selecting the most useful parts of the electromagnetic spectrum for the detection of weeds in corn and soybean fields.