Combing modified Grabcut, K-means clustering and sparse representation classification for weed recognition in wheat field

Abstract

Weeding is beneficial to the growth of the crops in field. At present, weeding in China mainly relies on chemical herbicide spraying on a large area, which leads to environmental pollution. Combined with digital image processing and pattern recognition technology, weed species identification in wheat seedling stage in field is of great significance to realize the variable spraying of herbicide, reduce the cost and protect the ecological environment. Weed species identification in field by machine vision is one of the challenging and hard topics because of the diversity and changeability of the weed in field. A weed species recognition approach is proposed combining modified Grabcut, adaptive fuzzy dynamic K-means algorithms and sparse representation classification (SRC). First, the original weed images are enhanced and noise is suppressed using filtering technique, and in the segmentation phase, each weed image is coarsely segmented by the modified GrabCut algorithm to remove most of background of the original image captured in the field, which can reduce the computing cost and recognition time. The original weed image is segmented by adaptive fuzzy dynamic K-means. Finally the weed species is recognized by SRC. Compared with the other weed recognition methods, the proposed method integrated the advantages of three approaches, (1) the improved Grabcut method does not require human interaction and can automatically segment the background, (2) the dynamic K-means algorithm introduces fitness function to evaluate clustering, which reduces the dependence of traditional K-means clustering algorithm on the initial value of clustering center to a certain extent, and avoids the problems such as dead zone center and center redundancy caused by local extremum, (3) SRC is utilized to classify the weed species. To test the proposed method, a lot of experiments are carried on the wheat weed image dataset. The results validate that the proposed method is effective for the weed species recognition, which can be used as a preliminary step for precision applying pesticide.