Combing K-means Clustering and Local Weighted Maximum Discriminant Projections for Weed Species Recognition

Abstract: Weed species identification is the premise to control weeds in smart agriculture. It is a challenging topic to control weeds in field, because the weeds in field are quite various and irregular with complex background. An identification method of weed species in crop field is proposed based on Grabcut and local discriminant projections (LWMDP) algorithm. First, Grabcut is used to remove the most background and K-means clustering (KMC) is utilized to segment weeds from the whole image. Then, LWMDP is employed to extract the low-dimensional discriminant features. Finally, the support vector machine (SVM) classifier is adopted to identify weed species. The characteristics of the method are that (1) Grabcut and KMC utilize the texture (color) information and boundary (contrast) information in the image to remove the most of background and obtain the clean weed image, which can reduce the burden of the subsequent feature extraction; (2) LWMDP aims to seek a transformation by the training samples, such that in the low-dimensional feature subspace, the different-class data points are mapped as far as possible while the within-class data points are projected as close as possible, and the matrix inverse computation is ignored in the generalized eigenvalue problem, thus the small sample size (SSS) problem is avoided naturally. The experimental results on the dataset of the weed species images show that the proposed method is effective for weed identification species, and can preliminarily meet the requirements of multi-row spraying of crop based on machine vision.