title: Two-dimensional multifractal detrended fluctuation analysis for plant identification.
abstract: BACKGROUND
  In this paper, a novel method is proposed to identify plant species by using the two- dimensional multifractal detrended fluctuation analysis (2D MF-DFA). Our method involves calculating a set of multifractal parameters that characterize the texture features of each plant leaf image. An index, I 0, that characterizes the relation of the intra-species variances and inter-species variances is introduced. This index is used to select three multifractal parameters for the identification process. The procedure is applied to the Swedish leaf data set containing leaves from fifteen different tree species.
  
  
  RESULTS
  The chosen three parameters form a three-dimensional space in which the samples from the same species can be clustered together and be separated from other species. Support vector machines and kernel methods are employed to assess the identification accuracy. The resulting averaged discriminant accuracy reaches 98.4% for every two species by the 10 - fold cross validation, while the accuracy reaches 93.96% for all fifteen species.
  
  
  CONCLUSIONS
  Our method, based on the 2D MF-DFA, provides a feasible and efficient procedure to identify plant species.
authors.affiliation: College of Science, Hunan Agricultural University, Changsha, 410128 China.
authors.forename: Fang
authors.initials: F
authors.lastname: Wang
authors.affiliation: Forestry Department of Hunan Province, Quality Testing and Inspection Centre of Forest Products, Changsha, 410007 China.
authors.forename: Deng-Wen
authors.initials: DW
authors.lastname: Liao
authors.affiliation: Agricultural Information Institute, Hunan Agricultural University, Changsha, 410128 China.
authors.forename: Jin-Wei
authors.initials: JW
authors.lastname: Li
authors.affiliation: Agricultural Information Institute, Hunan Agricultural University, Changsha, 410128 China.
authors.forename: Gui-Ping
authors.initials: GP
authors.lastname: Liao
keywords: Multifractal detrended fluctuation analysis; Plant identification; Support vector machines and kernel methods