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

Jin, Taisong; Hou, Xueliang; Li, Pifan; Zhou, Feifei

doi:10.1371/journal.pone.0139482

Cited by 58 publications

(51 citation statements)

References 21 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Solving the constrained LASSO problem [17], we obtain the sparse coefficients and calculate the residuals for each candidate class. Finally, the test sample is classified into the class with the smallest residual.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

Jaccard distance based weighted sparse representation for coarse-to-fine plant species recognition

Zhang

You

2017

PLoS ONE

View full text Add to dashboard Cite

Leaf based plant species recognition plays an important role in ecological protection, however its application to large and modern leaf databases has been a long-standing obstacle due to the computational cost and feasibility. Recognizing such limitations, we propose a Jaccard distance based sparse representation (JDSR) method which adopts a two-stage, coarse to fine strategy for plant species recognition. In the first stage, we use the Jaccard distance between the test sample and each training sample to coarsely determine the candidate classes of the test sample. The second stage includes a Jaccard distance based weighted sparse representation based classification(WSRC), which aims to approximately represent the test sample in the training space, and classify it by the approximation residuals. Since the training model of our JDSR method involves much fewer but more informative representatives, this method is expected to overcome the limitation of high computational and memory costs in traditional sparse representation based classification. Comparative experimental results on a public leaf image database demonstrate that the proposed method outperforms other existing feature extraction and SRC based plant recognition methods in terms of both accuracy and computational speed.

show abstract

Section: Experiments and Results Analysismentioning

confidence: 99%

Jaccard distance based weighted sparse representation for coarse-to-fine plant species recognition

Zhang

You

2017

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Other methods [21][22] [23] use a combination of leaf tooth features and non-leaf tooth features; the focus of this study is only on the leaf teeth features, so the comparison method is based on the selection proposed in [20]. A leaf identification method identifies features such as total area of teeth, internal angles, number of teeth, and total length of outer edges.…”

Section: Methodsmentioning

confidence: 99%

“…In a study on leaf shape, Xiaofeng et al used Hu geometric moment and Zernike vertical distance to measure the shape of an input leaf image for automatic leaf classification [17]; Du et al extracted eight characteristics such as the aspect ratio, rectangular and convex area of the leaf, convex perimeter ratio, sphericity, roundness, eccentricity, and shape factor [18]. In a study on leaf edge, Zheng et al extracted the leaf edge by using the Harris and SUSAN algorithm and calculated three characteristic parameters: leaf edge sawtooth number, sharpness, and skewness [19]; Corney automatically acquired the shape and size of the leaf tooth by identifying the tooth on the leaf edge [20]; Jin proposed a method for classifying plant leaves using the sparse matrix of leaf tooth features [21]; Chen used the ratio between the internal distance of the leaf and the Euclidean distance to represent the local concavity and convexity of leaves [22], and classified the whole edge, tooth edge, wave edge, and leaf crack edge; Li used data dimensionality and character weighted semi-supervised clustering algorithm to identify the leaf by synthesizing leaf shape, leaf edge, texture, and other characteristics, which could be used for several applications [23].…”

Section: Introductionmentioning

confidence: 99%

Plant leaf tooth feature extraction

Tian²,

Chu

et al. 2018

Preprint

View full text Add to dashboard Cite

Leaf tooth can indicate several systematically informative features and is extremely useful for circumscribing fossil leaf taxa. Moreover, it can help discriminate species or even higher taxa accurately. Previous studies extract features that are not strictly defined in botany; therefore, a uniform standard to compare the accuracies of various feature extraction methods cannot be used. For efficient and automatic retrieval of plant leaves from a leaf database, in this study, we propose an image-based description and measurement of leaf teeth by referring to the leaf structure classification system in botany. First, image preprocessing is carried out to obtain a binary map of plant leaves. Then, corner detection based on the curvature scale-space (CSS) algorithm is used to extract the inflection point from the edges; next, the leaf tooth apex is extracted by screening the convex points; then, according to the definition of the leaf structure, the characteristics of the leaf teeth are described and measured in terms of number of orders of teeth, tooth spacing, number of teeth, sinus shape, and tooth shape. In this manner, data extracted from the algorithm can not only be used to classify plants, but also provide scientific and standardized data to understand the history of plant evolution. Finally, to verify the effectiveness of the extraction method, we use leaf tooth features and simple linear discriminant analysis to classify leaves; the results show that the proposed method achieves high accuracy as compared to other methods.

show abstract

“…A total of 102 images were taken from four species’ and the best 80 were used in this study. According to previous studies, [30, 31], we decided to use half of our digital images for training and the other half for testing the system. 10 images of each species were selected as training set and 10 were used as testing set.…”

Section: Methodsmentioning

confidence: 99%

Automated identification of Monogeneans using digital image processing and K-nearest neighbour approaches

et al. 2016

View full text Add to dashboard Cite

BackgroundMonogeneans are flatworms (Platyhelminthes) that are primarily found on gills and skin of fishes. Monogenean parasites have attachment appendages at their haptoral regions that help them to move about the body surface and feed on skin and gill debris. Haptoral attachment organs consist of sclerotized hard parts such as hooks, anchors and marginal hooks. Monogenean species are differentiated based on their haptoral bars, anchors, marginal hooks, reproductive parts’ (male and female copulatory organs) morphological characters and soft anatomical parts. The complex structure of these diagnostic organs and also their overlapping in microscopic digital images are impediments for developing fully automated identification system for monogeneans (LNCS 7666:256-263, 2012), (ISDA; 457–462, 2011), (J Zoolog Syst Evol Res 52(2): 95–99. 2013;). In this study images of hard parts of the haptoral organs such as bars and anchors are used to develop a fully automated identification technique for monogenean species identification by implementing image processing techniques and machine learning methods.ResultImages of four monogenean species namely Sinodiplectanotrema malayanus, Trianchoratus pahangensis, Metahaliotrema mizellei and Metahaliotrema sp. (undescribed) were used to develop an automated technique for identification. K-nearest neighbour (KNN) was applied to classify the monogenean specimens based on the extracted features. 50% of the dataset was used for training and the other 50% was used as testing for system evaluation. Our approach demonstrated overall classification accuracy of 90%. In this study Leave One Out (LOO) cross validation is used for validation of our system and the accuracy is 91.25%.ConclusionsThe methods presented in this study facilitate fast and accurate fully automated classification of monogeneans at the species level. In future studies more classes will be included in the model, the time to capture the monogenean images will be reduced and improvements in extraction and selection of features will be implemented.

show abstract

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

Cited by 58 publications

References 21 publications

Jaccard distance based weighted sparse representation for coarse-to-fine plant species recognition

Jaccard distance based weighted sparse representation for coarse-to-fine plant species recognition

Plant leaf tooth feature extraction

Automated identification of Monogeneans using digital image processing and K-nearest neighbour approaches

Contact Info

Product

Resources

About