Frame-based reflectance estimation from multispectral images for weed identification in varying illumination conditions

To prevent the growth of weeds in precision farming, multispectral imaging has gained much interest for its ability to provide vegetation images with a high spectral resolution. However, spectral reflectance computation is an issue when the image is assembled from successive frames acquired under varying illumination conditions. In this study, we present a method to estimate reflectance from images acquired by a linescan camera in such conditions. Because rows in a given channel are associated to different illumination conditions, we process the image row-wise to improve reflectance estimation. Experimental segmentation results show that our method is a good candidate to effectively identify crops from weeds.