Two-dimensional multifractal detrended fluctuation analysis for plant identification

Plant Methods

Gui-ping Liao³  Jin-wei Li³  Deng-wen Liao¹  Fang Wang² 

[1] Forestry Department of Hunan Province, Quality Testing and Inspection Centre of Forest Products, Changsha 410007, China;College of Science, Hunan Agricultural University, Changsha 410128, China;Agricultural Information Institute, Hunan Agricultural University, Changsha 410128, China

关键词: Support vector machines and kernel methods;    Multifractal detrended fluctuation analysis;    Plant identification;   

Others  :  1162821
DOI  :  10.1186/s13007-015-0049-7

 received in 2014-10-15, accepted in 2015-01-21,  发布年份 2015

【 摘 要 】

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, I₀, 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.

【 授权许可】

   
2015 Wang et al.; licensee BioMed Central.

【 图 表 】

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