Identification of weeds based on fractal dimension analysis of time series of weed leaf chlorophyll

A new application of fractal dimension analysis was initiated for identification of different kinds of broadleaf weeds. The distribution of chlorophyll time series of weed leaves exhibited self-similar geometrical characteristics. The fractal dimension analysis was conducted using GrassbergerProcaccia (G-P) phase space reconstruction algorithm. A total of 300 leaves of three weeds of Oxalis corniculata L. (OC), Ixeris chinensis (Thunb.) Nakai. (IC) and Herba glechomae L. (HG) (species) were sampled and analyzed. The correlation dimensions of time series of O. corniculata , I. chinensis and H. glechomae estimated by G-P algorithm are 8.050, 10.094 and 11.730, respectively. The distribution of chlorophyll time series was restricted in chaos environment and governed by strange attractors. The self-similar distribution property includes the information about weed varieties, which can be used for classification.