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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

Average identification accuracies of the 15 species calculated with K = 10 with different species sample sizes.
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Fig12: Average identification accuracies of the 15 species calculated with K = 10 with different species sample sizes.

Mentions: We remark that the sample size of each species has little effect on the average discriminant accuracy. To justify this, we randomly choose n (n ≤ 75) leaf samples for each species and run the procedure. Then repeat the process 10 times and take the average accuracy, which is reported in Figure 12. It can be seen from Figure 12 that as the number of samples changes from 40 to 75, the accuracy changes only 0.73%.Figure 12


Two-dimensional multifractal detrended fluctuation analysis for plant identification.

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

Average identification accuracies of the 15 species calculated with K = 10 with different species sample sizes.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig12: Average identification accuracies of the 15 species calculated with K = 10 with different species sample sizes.
Mentions: We remark that the sample size of each species has little effect on the average discriminant accuracy. To justify this, we randomly choose n (n ≤ 75) leaf samples for each species and run the procedure. Then repeat the process 10 times and take the average accuracy, which is reported in Figure 12. It can be seen from Figure 12 that as the number of samples changes from 40 to 75, the accuracy changes only 0.73%.Figure 12

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