Multi-type Feature Fusion Technique for Weed Identification in Cotton Fields

Abstract

Weed identification is core of precision variable spray technology and weed information management system. Single type features are difficult to identify multi-class weeds in cotton fields. In this paper, multi-type feature fusion technique for weed identification is proposed. Firstly, multi-type features are extracted. In color feature extraction, FMS, SMS and TMS in HSI are extracted by color moment. In shape feature extraction, REC, RWL, CIR and SPH are extracted by geometric parameter method. In texture feature extraction, ASM, CON and COR are extracted by GLCM. Secondly, because feature dimension is too large, principle component analysis is used to reduce dimension to extract new features including COR, ASM, REC and two components. Finally, three comparative experiments including identification of five kinds of weeds, three kinds of weeds and two kinds of weeds are carried out. Experimental results show that method proposed in this paper is superior to state of the art and is suitable for identification of multi-class weeds. This method can also be applied in identifying weeds in other fields.