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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. (a): the average accuracies of every two species using different values of K; (b): The accuracies of identifying species Ulmus carpinifolia versus the other 14 species using K = 10.
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Fig9: Average identification accuracies. (a): the average accuracies of every two species using different values of K; (b): The accuracies of identifying species Ulmus carpinifolia versus the other 14 species using K = 10.

Mentions: In addition, we calculate the discriminant accuracies of every two tree species by SVMKM using the K − fold cross validation with different K values. The average accuracies of 10 trials are shown in Figure 9(a). To display the applicability of identifying different tree species by our proposed method, as an example, we plot the accuracy of identifying species MI (Ulmus carpinifolia) versus other 14 species with K = 10 in Figure 9(b). As expected, the average accuracy of every two species is increasing with respect to K. The obtained best accuracy is 98.40%, higher than 96.82% reported in [35], which requires a very complex pre-processing process for leaf images. It is seen from Figure 9(b) that there are accuracy variations between species Ulmus carpinifolia and the other 14 species. Five species, namely, Salix aurita, Betula pubescens, Ulmus glabra, Salix sinerea and Fagus silvatica, have accuracies below the average accuracy. This suggests that species Ulmus carpinifolia has high similarity with the above mentioned five species, which agrees with the observation from Figure 1.Figure 9


Two-dimensional multifractal detrended fluctuation analysis for plant identification.

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

Average identification accuracies. (a): the average accuracies of every two species using different values of K; (b): The accuracies of identifying species Ulmus carpinifolia versus the other 14 species using K = 10.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig9: Average identification accuracies. (a): the average accuracies of every two species using different values of K; (b): The accuracies of identifying species Ulmus carpinifolia versus the other 14 species using K = 10.
Mentions: In addition, we calculate the discriminant accuracies of every two tree species by SVMKM using the K − fold cross validation with different K values. The average accuracies of 10 trials are shown in Figure 9(a). To display the applicability of identifying different tree species by our proposed method, as an example, we plot the accuracy of identifying species MI (Ulmus carpinifolia) versus other 14 species with K = 10 in Figure 9(b). As expected, the average accuracy of every two species is increasing with respect to K. The obtained best accuracy is 98.40%, higher than 96.82% reported in [35], which requires a very complex pre-processing process for leaf images. It is seen from Figure 9(b) that there are accuracy variations between species Ulmus carpinifolia and the other 14 species. Five species, namely, Salix aurita, Betula pubescens, Ulmus glabra, Salix sinerea and Fagus silvatica, have accuracies below the average accuracy. This suggests that species Ulmus carpinifolia has high similarity with the above mentioned five species, which agrees with the observation from Figure 1.Figure 9

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