Utilising variable sorting for normalisation to correct illumination effects in close-range spectral images of potato plants

Visible and near-infrared spectral imaging is a key non-destructive technique for rapid assessment of biophysical traits of plants. A major challenge with close-range spectral imaging of plants is spectral variation arising from illumination effects, which may mask the signals due to physiochemical differences. In the present work, we describe a new scatter correction technique called variable sorting for normalisation (VSN) and compare its efficiency with that of the commonly used standard normal variate (SNV) technique for the removal of unwanted illumination effects. Spectral images of potato plants were used for testing the correction. The results showed that the VSN outperformed SNV in removing illumination effects from the images of plants. The results show that the VSN approach to illumination correction can support high-throughput plant phenotyping where spectral imaging is used as a continuous monitoring tool.