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A Novel Method of Automatic Plant Species Identification Using Sparse Representation of Leaf Tooth Features.

Jin T, Hou X, Li P, Zhou F - PLoS ONE (2015)

Bottom Line: Next, the top and bottom leaf tooth edges are discriminated to effectively correspond to the extracted image corners; then, four leaf tooth features (Leaf-num, Leaf-rate, Leaf-sharpness and Leaf-obliqueness) are extracted and concatenated into a feature vector.Finally, a sparse representation-based classifier is used to identify a plant species sample.Tests on a real-world leaf image dataset show that our proposed method is feasible for species identification.

View Article: PubMed Central - PubMed

Affiliation: School of Information Science and Engineering, Xiamen University, Xiamen, 361005, China.

ABSTRACT
Automatic species identification has many advantages over traditional species identification. Currently, most plant automatic identification methods focus on the features of leaf shape, venation and texture, which are promising for the identification of some plant species. However, leaf tooth, a feature commonly used in traditional species identification, is ignored. In this paper, a novel automatic species identification method using sparse representation of leaf tooth features is proposed. In this method, image corners are detected first, and the abnormal image corner is removed by the PauTa criteria. Next, the top and bottom leaf tooth edges are discriminated to effectively correspond to the extracted image corners; then, four leaf tooth features (Leaf-num, Leaf-rate, Leaf-sharpness and Leaf-obliqueness) are extracted and concatenated into a feature vector. Finally, a sparse representation-based classifier is used to identify a plant species sample. Tests on a real-world leaf image dataset show that our proposed method is feasible for species identification.

No MeSH data available.


The example image for Duranta repens Linn.
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pone.0139482.g008: The example image for Duranta repens Linn.

Mentions: Because different species own leaf characteristics with large and small inter-class variations, even if the study focuses on a single genus, it may contain many species, each of which encompasses much variation between constituent populations. Thus, we specifically select the species, which have similar leaf tooth character and observe the classification performance of the proposed method. To demonstrate the performance of our proposed method, we constructed, as an image dataset, a total of 700 leaf images (S1 File) from eight plant species. For each species, there are leaf images with variations in lighting, scale and background. The eight species have images as follows: (1) 54 images of Hibiscus rosa-sinensis Linn; (2) 96 images of Duranta repens Linn; (3) 54 images of Parthenocissus tricuspidata (Sieb. et Zucc.) Planch;(4) 124 images of Hibiscus schizopetalus (Masters) Hook. f; (5) 100 images of Cyclobalanopsis glauca (Thunb.) Oerst; (6) 82 images of Eriobotrya japonica (Thunb.) Lindl; (7) 124 images of Conyza canadensis (L.) Cronq; and (8) 66 images of Amygdalus persica Linn. Example images are shown in Figs 7, 8, 9, 10, 11, 12, 13, 14. A total of 350 images were used as the training dataset, where half of the images were randomly selected for each species. Then, the other 350 images were used as the test dataset.


A Novel Method of Automatic Plant Species Identification Using Sparse Representation of Leaf Tooth Features.

Jin T, Hou X, Li P, Zhou F - PLoS ONE (2015)

The example image for Duranta repens Linn.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4595291&req=5

pone.0139482.g008: The example image for Duranta repens Linn.
Mentions: Because different species own leaf characteristics with large and small inter-class variations, even if the study focuses on a single genus, it may contain many species, each of which encompasses much variation between constituent populations. Thus, we specifically select the species, which have similar leaf tooth character and observe the classification performance of the proposed method. To demonstrate the performance of our proposed method, we constructed, as an image dataset, a total of 700 leaf images (S1 File) from eight plant species. For each species, there are leaf images with variations in lighting, scale and background. The eight species have images as follows: (1) 54 images of Hibiscus rosa-sinensis Linn; (2) 96 images of Duranta repens Linn; (3) 54 images of Parthenocissus tricuspidata (Sieb. et Zucc.) Planch;(4) 124 images of Hibiscus schizopetalus (Masters) Hook. f; (5) 100 images of Cyclobalanopsis glauca (Thunb.) Oerst; (6) 82 images of Eriobotrya japonica (Thunb.) Lindl; (7) 124 images of Conyza canadensis (L.) Cronq; and (8) 66 images of Amygdalus persica Linn. Example images are shown in Figs 7, 8, 9, 10, 11, 12, 13, 14. A total of 350 images were used as the training dataset, where half of the images were randomly selected for each species. Then, the other 350 images were used as the test dataset.

Bottom Line: Next, the top and bottom leaf tooth edges are discriminated to effectively correspond to the extracted image corners; then, four leaf tooth features (Leaf-num, Leaf-rate, Leaf-sharpness and Leaf-obliqueness) are extracted and concatenated into a feature vector.Finally, a sparse representation-based classifier is used to identify a plant species sample.Tests on a real-world leaf image dataset show that our proposed method is feasible for species identification.

View Article: PubMed Central - PubMed

Affiliation: School of Information Science and Engineering, Xiamen University, Xiamen, 361005, China.

ABSTRACT
Automatic species identification has many advantages over traditional species identification. Currently, most plant automatic identification methods focus on the features of leaf shape, venation and texture, which are promising for the identification of some plant species. However, leaf tooth, a feature commonly used in traditional species identification, is ignored. In this paper, a novel automatic species identification method using sparse representation of leaf tooth features is proposed. In this method, image corners are detected first, and the abnormal image corner is removed by the PauTa criteria. Next, the top and bottom leaf tooth edges are discriminated to effectively correspond to the extracted image corners; then, four leaf tooth features (Leaf-num, Leaf-rate, Leaf-sharpness and Leaf-obliqueness) are extracted and concatenated into a feature vector. Finally, a sparse representation-based classifier is used to identify a plant species sample. Tests on a real-world leaf image dataset show that our proposed method is feasible for species identification.

No MeSH data available.