Orthogonal locally discriminant spline embedding for plant leaf recognition

Lei, Yingke; Zou, Ji-Wei; Dong, Tianbao; You, Zhu-Hong; Yuan, Yuan; Hu, Yihua

doi:10.1016/j.cviu.2013.12.001

Cited by 22 publications

(9 citation statements)

References 22 publications

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“…To demonstrate our method of identifying plant species by using the leaf texture, we use the Swedish leaf data set [ 31 ] for our experiment, which is widely employed in computer vision and pattern recognition fields [ 4 , 33 , 34 ], plant taxon fields [ 1 ] and image processing fields [ 6 , 35 ]. This leaf data set has images of 15 species of leaves with 75 sample images per species.…”

Section: Methodsmentioning

confidence: 99%

Two-dimensional multifractal detrended fluctuation analysis for plant identification

Wang

Liao

et al. 2015

Plant Methods

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BackgroundIn 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.ResultsThe 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.ConclusionsOur method, based on the 2D MF-DFA, provides a feasible and efficient procedure to identify plant species.

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Section: Methodsmentioning

confidence: 99%

Two-dimensional multifractal detrended fluctuation analysis for plant identification

Wang

Liao

et al. 2015

Plant Methods

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“…The authors of [137] used the SVM classifier with the texton features and achieved higher classification accuracy of 97.73%, compared to the classification accuracy of 95.87% achieved in [75] with entropy feature. When the leaf margin was used as a feature, with orthogonally locally linear embedding of a manifold learning classifier, the authors of the paper [164] achieved an accuracy of 94.11%. Wang et al [147] used labelled features and supervised locality projection to achieve a classification accuracy of 97.54%.…”

Section: The Flavia Datasetmentioning

confidence: 99%

A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification

Thyagharajan¹,

Raji²

2018

Arch Computat Methods Eng

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Plants are fundamentally important to life. Key research areas in plant science include plant species identification, weed classification using hyper spectral images, monitoring plant health and tracing leaf growth, and the semantic interpretation of leaf information. Botanists easily identify plant species by discriminating between the shape of the leaf, tip, base, leaf margin and leaf vein, as well as the texture of the leaf and the arrangement of leaflets of compound leaves. Because of the increasing demand for experts and calls for biodiversity, there is a need for intelligent systems that recognize and characterize leaves so as to scrutinize a particular species, the diseases that affect them, the pattern of leaf growth, and so on. We review several image processing methods in the feature extraction of leaves, given that feature extraction is a crucial technique in computer vision. As computers cannot comprehend images, they are required to be converted into features by individually analysing image shapes, colours, textures and moments. Images that look the same may deviate in terms of geometric and photometric variations. In our study, we also discuss certain machine learning classifiers for an analysis of different species of leaves. Table 2 Inferences of data sources Visual descriptors of leaf and classification No. of papers selected Geometrical descriptors 30 Leaf shape/tip/base/venation 40 Texture/texton 25

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“…The proposed method achieved a classification rate of 95% on the Flavia dataset and 70% on the Leafsnap dataset with the Local Discriminant Classifier (LDC) as a classifier. Lei et al (2014), derived two orthogonal locally discriminant spline embedding techniques (OLDSE-I and OLDSE-II) from a Local Spline embedding (LSE) and a Maximum Margin Criterion (MMC). The plant leaves are mapped into a leaf subspace in order to identify the essential leaf manifold.…”

Section: Related Workmentioning

confidence: 99%

Plant Specie Classification Using Sinuosity Coefficients of Leaves

Kala

Viriri

2018

Image Anal Stereol

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Forests are the lungs of our planet. Conserving the plants may require the development of an automated system that will identify plants using leaf features such as shape, color, and texture. In this paper, a leaf shape descriptor based on sinuosity coefficients is proposed. The sinuosity coefficients are defined using the sinuosity measure, which is a measure expressing the degree of meandering of a curve. The initial empirical experiments performed on the LeafSnap dataset on the usage of four sinuosity coefficients to characterize the leaf images using the Radial Basis Function Neural Network (RBF) and Multilayer Perceptron (MLP) classifiers achieved accurate classification rates of 88% and 65%, respectively. The proposed feature extraction technique is further enhanced through the addition of leaf geometrical features, and the accurate classification rates of 93% and 82% were achieved using RBF and MLP, respectively. The overall results achieved showed that the proposed feature extraction technique based on the sinuosity coefficients of leaves, complemented with geometrical features improve the accuracy rate of plant classification using leaf recognition.

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Orthogonal locally discriminant spline embedding for plant leaf recognition

Cited by 22 publications

References 22 publications

Two-dimensional multifractal detrended fluctuation analysis for plant identification

Two-dimensional multifractal detrended fluctuation analysis for plant identification

A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification

Plant Specie Classification Using Sinuosity Coefficients of Leaves

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