Recognition of Leaf Image Based on Ring Projection Wavelet Fractal Feature

Wang, Qingping; Du, Ji-Xiang; Zhai, Chuan-Min

doi:10.1007/978-3-642-14932-0_30

Cited by 15 publications

(6 citation statements)

References 2 publications

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“…To compare and evaluate the results of the proposed algorithm, a set of prevalent shape and texture features that have been used in many studies and are mentioned in feature extraction section [10, 11, 17, 26].…”

Section: Resultsmentioning

confidence: 99%

Plant leaf classification using GIST texture features

Kheirkhah

Asghari

2019

IET Computer Vision

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The leaves of plants have rich information in recognition of plants. In general, agriculture experts accomplish information extraction from the leaves. Since the leaves contain useful features for recognising various types of plants, so these features can be extracted and applied by automatic image recognition algorithms to classify plant species. In this study, the authors investigate a novel approach for recognition of plant species using GIST texture features. Then, the principal and suitable features are selected by principal component analysis (PCA) algorithm. In the classification step, three different approaches such as Patternnet neural network, support vector machine, and K-nearest neighbour (KNN) algorithms were applied to the extracted features. For evaluation of the authors' approach, they applied their proposed algorithm on three famous datasets. In comparison to some widely used features, the results show that their approach outperforms the other methods in the case of the time and the accuracy. The best results were achieved by applying PCA algorithm to GIST feature vector and using the Cosine KNN classifier.

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Section: Resultsmentioning

confidence: 99%

Plant leaf classification using GIST texture features

Kheirkhah

Asghari

2019

IET Computer Vision

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“…The determination of features such as the characteristic wavelengths, multidimensional embedding sequence similarity, 21 fuzzy logic, 22 and Fourier descriptors, 23 is performed by Bayesian discriminant analysis 24 and Principal Component Analysis (PCA). Wang et al 25 considered the Ring Projection Wavelet Fractal Feature for the recognition of the leaf images, whereas Kadir et al 26 employed Zernike moments in order to construct systems for the purpose of foliage plants identification. In addition to the Zernike moments, subsequent features such as gray‐level co‐occurrence matrix (GLCM) and geometric features, color moments were brought together.…”

Section: Literature Reviewmentioning

confidence: 99%

Particle Swarm Optimization (PSO) with fuzzy c means (PSO‐FCM)–based segmentation and machine learning classifier for leaf diseases prediction

Kumar¹,

Sumithra

Saranya³

2019

Concurrency and Computation

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Summary This paper proposes an automatic classification technique that uses leaf images some medicinal plants. It is primarily the core reason that drives the research presented here, including the introduction of new innovative segmentation and classification techniques that are deployed to facilitate automatic detection. The major aim of the work is to introduce a new leaf disease prediction technique. The study conducted here a unique but effective image segmentation, feature extraction, as well as plant leaf disease classification. The proposed approach initially preprocesses leaf images of plants thereafter which the diseased sections of the plant are segmented by deploying Particle Swarm Optimization (PSO)–based fuzzy c means segmentation (PSO‐FCM), Gaussian Mixture Model (GMM)–based background subtraction. Vein and shape features, edge‐based feature extraction, and texture characteristics or texture features (TF) are computed. This methodology classifies the leaves of medicinal plants by deploying the Multiple Kernel Parallel Support Vector Machine (MK‐PSVM) classifier. The classifier is implemented via the use of MATLAB classifier. The results are measured using the accuracy, sensitivity, specificity, precision, and F‐measure metrics. Experimental results depict that the classifiers that have been proposed here achieve a higher classification accuracy enabling leaf detection.

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“…An accuracy of about 80% is reported. Wavelet and fractal based features have been used in [15] to model the uneven shapes of leaves. Texture features along with shape identifiers have been used in [16] to improve recognition accuracies.…”

Section: Previous Workmentioning

confidence: 99%

Plant Leaf Recognition using Shape based Features and Neural Network classifiers

Chaki¹,

Parekh²

2011

IJACSA

108

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This paper proposes an automated system for recognizing plant species based on leaf images. Plant leaf images corresponding to three plant types, are analyzed using two different shape modeling techniques, the first based on the Moments-Invariant (M-I) model and the second on the Centroid-Radii (C-R) model. For the M-I model the first four normalized central moments have been considered and studied in various combinations viz. individually, in joint 2-D and 3-D feature spaces for producing optimum results. For the C-R model an edge detector has been used to identify the boundary of the leaf shape and 36 radii at 10 degree angular separation have been used to build the feature vector. To further improve the accuracy, a hybrid set of features involving both the M-I and C-R models has been generated and explored to find whether the combination feature vector can lead to better performance. Neural networks are used as classifiers for discrimination. The data set consists of 180 images divided into three classes with 60 images each. Accuracies ranging from 90%-100% are obtained which are comparable to the best figures reported in extant literature.

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Recognition of Leaf Image Based on Ring Projection Wavelet Fractal Feature

Cited by 15 publications

References 2 publications

Plant leaf classification using GIST texture features

Plant leaf classification using GIST texture features

Particle Swarm Optimization (PSO) with fuzzy c means (PSO‐FCM)–based segmentation and machine learning classifier for leaf diseases prediction

Plant Leaf Recognition using Shape based Features and Neural Network classifiers

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