Sugar beet (Beta vulgaris L.) and thistle (Cirsium arvensis L.) discrimination based on field spectral data. (November 2015) - Garcia-Ruiz, Francisco J.; Wulfsohn, Dvoralai; Rasmussen, Jesper
Abstract : Creeping thistle ( Cirsium arvensis (L.) Scop.) is a perennial weed that causes yield loss in sugar beet ( Beta vulgaris L.) crops. The weeds are usually mapped for site specific weed management because they tend to grow in patches. Remote sensing techniques have shown promising results in species discrimination and therefore provide potential for weed mapping. In this study we examined the feasibility of high-resolution imaging for sugar beet and thistle discrimination and proposed a protocol to select multispectral camera filters. Spectral samples from sugar beet and thistle were acquired with a field portable spectroradiometer under field conditions and Partial Least Squares Discriminant Analysis (PLS-DA) classification models were developed with 211 and 36 spectral features of 1.56 and 10 nm bandwidths, respectively. The classification rates obtained using these models were regarded as the maximum obtainable. Then, spectral responses of a multi-band camera equipped with the filter configuration proposed by the PLS-DA models were simulated. Finally, a simulation of crop-weed discrimination was made using small unmanned aerial vehicles (UAV)-based multispectral images. More than 95% of the thistles and 89% of the sugar beets were correctly classified when continuous spectral data were used with 1.56 and 10 nm bandwidths. Accuracy dropped to 93% of thistles identified and 84% of sugar beets correctly classified when only the four best bands were used. The validation based on aerial images showed that sugar beets and thistle plants could be discriminated in images if sufficient pure pixels containing leaf spectra were available, that is with spatial resolutions of 6 mm pixel −1 or finer. Highlights: PLS-DA analysis of plant spectra used to select multispectral camera filters. 'Smooth fractionator' provided calibration/validation sets spanning sample variability. Identified spectral and spatial resolutions to discriminate thistles and sugar beet. 10 nm-bands in VIS range offered high discrimination performance for both species. UAV-based images provide spatial resolution needed for classification (≤6 mm px −1 ).