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Two-dimensional multifractal detrended fluctuation analysis for plant identification.

Wang F, Liao DW, Li JW, Liao GP - Plant Methods (2015)

Bottom Line: 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.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.Our method, based on the 2D MF-DFA, provides a feasible and efficient procedure to identify plant species.

View Article: PubMed Central - PubMed

Affiliation: College of Science, Hunan Agricultural University, Changsha, 410128 China.

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.

No MeSH data available.


Related in: MedlinePlus

Visualization of two tree species in the{h(−3), αmin, Δα} space. (a): Ulmuscarpinifolia versus Alnusincana; (b): Salixaurita versus SalixalbaSericea; (c): Salixsinerea versus Tilia; (d): Sorbusaucuparia versus Fagussilvatica.
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Fig8: Visualization of two tree species in the{h(−3), αmin, Δα} space. (a): Ulmuscarpinifolia versus Alnusincana; (b): Salixaurita versus SalixalbaSericea; (c): Salixsinerea versus Tilia; (d): Sorbusaucuparia versus Fagussilvatica.

Mentions: In our first identification experiment, we test the proposed method through examining the distinguishing effect for every two species. To this end, we form a three-dimensional parameter space with components given by the above chosen feature descriptors {h(−3), αmin, Δα}. In this space, one point represents a leaf sample image. In Figure 8(a)-(d), we plot the corresponding points for Ulmus carpinifolia versus Alnus incana, Salix aurita versus Salix alba Sericea, Salix sinerea versus Tilia and Sorbus aucuparia versus Fagus silvatica, respectively. As shown in these plots, the samples from the same tree species are clustered together reasonably well.Figure 8


Two-dimensional multifractal detrended fluctuation analysis for plant identification.

Wang F, Liao DW, Li JW, Liao GP - Plant Methods (2015)

Visualization of two tree species in the{h(−3), αmin, Δα} space. (a): Ulmuscarpinifolia versus Alnusincana; (b): Salixaurita versus SalixalbaSericea; (c): Salixsinerea versus Tilia; (d): Sorbusaucuparia versus Fagussilvatica.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4358846&req=5

Fig8: Visualization of two tree species in the{h(−3), αmin, Δα} space. (a): Ulmuscarpinifolia versus Alnusincana; (b): Salixaurita versus SalixalbaSericea; (c): Salixsinerea versus Tilia; (d): Sorbusaucuparia versus Fagussilvatica.
Mentions: In our first identification experiment, we test the proposed method through examining the distinguishing effect for every two species. To this end, we form a three-dimensional parameter space with components given by the above chosen feature descriptors {h(−3), αmin, Δα}. In this space, one point represents a leaf sample image. In Figure 8(a)-(d), we plot the corresponding points for Ulmus carpinifolia versus Alnus incana, Salix aurita versus Salix alba Sericea, Salix sinerea versus Tilia and Sorbus aucuparia versus Fagus silvatica, respectively. As shown in these plots, the samples from the same tree species are clustered together reasonably well.Figure 8

Bottom Line: 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.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.Our method, based on the 2D MF-DFA, provides a feasible and efficient procedure to identify plant species.

View Article: PubMed Central - PubMed

Affiliation: College of Science, Hunan Agricultural University, Changsha, 410128 China.

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.

No MeSH data available.


Related in: MedlinePlus