A deep semantic segmentation-based algorithm to segment crops and weeds in agronomic color images

Abstract In precision agriculture, the accurate segmentation of crops and weeds in agronomic images has always been the center of attention. Many methods have been proposed but still the clean and sharp segmentation of crops and weeds is a challenging issue for the images with a high presence of weeds. This work proposes a segmentation method based on the combination of semantic segmentation and K-means algorithms for the segmentation of crops and weeds in color images. Agronomic images of two different databases were used for the segmentation algorithms. Using the thresholding technique, everything except plants was removed from the images. Afterward, semantic segmentation was applied usin…