Weed Density Estimation Using Semantic Segmentation

Use of herbicides is rising globally to enhance crop yield and meet the ever increasing food demand. It adversely impacts environment and biosphere. To rationalize its use, variable rate herbicide based on weed densities mapping is a promising technique. Estimation of weed densities depends upon precise detection and mapping of weeds in the field. Recently, semantic segmentation is studied in precision agriculture due to its power to detect and segment objects in images. However, due to extremely difficult and time consuming job of labelling the pixels in agriculture images, its application is limited. To accelerate labelling process for semantic segmentation, a two step manual labelling procedure is proposed in this paper. The proposed method is tested on oat field imagery. It has shown improved intersection over union values as semantic models are trained on a comparatively bigger labelled real dataset. The method demonstrates intersection over union value of 81.28% for weeds and mean intersection over union value of 90.445%.