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  • 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 5, September – October (2013), pp. 204-216 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET ©IAEME A REVIEW OF PLANT LEAF CLASSIFICATION FEATURES AND TECHNIQUES Garima Agarwal1, Rekha Nair2, Pravin Shrinath3 1 M.Tech (Computer Science), Mukesh Patel School of Technology Management & Engineering, JVPD Vile Parle (West) Mumbai 400056, India. 2 Centre for Development of Advanced Computing, Gulmohar Cross Road No.9, Juhu, Mumbai – 49, India. 3 Associate Professor, Department of Computer Engineering, MPSTME, SVKM’s NMIMS University, Mumbai, India. ABSTRACT Plant Leaf classification has a broad application in agriculture and medicine, and it is mainly significant to the biology diversity research. As plants play a very important role for environmental protection, it is more important to identify and classify them accurately. Leaf classification is a technique where leaf is classified based on its different morphological features like vein structure, shape, color, texture etc. This paper provides an overview of different aspects of plant leaf classification and various existing techniques used for classification. Keywords: Leaf Classification, Leaf Recognition, Vein Extraction, GLCM, LVQ. 1. INTRODUCTION Plant is one of the most important forms of life on earth. Plants maintain the balance of oxygen and carbon dioxide of earth’s atmosphere. The relations between plants and human beings are also very close. In addition, plants are important means of livelihood and production of human beings. Unfortunately, the overwhelming development of human civilization has disrupted this balance to a greater extent than we realize. It is one of our biggest responsibilities to save the plants from various threats, restore the diverseness of the plant community and put everything back to balance. The main step of protecting plants is to automatically recognize or classify them means understand what they are and where they come from. There are a huge number of plant species worldwide. To handle such volumes of information, development of a quick and efficient classification method has become an area of active research. In addition to the conservation aspect, 204
  • 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME recognition of plants is also necessary to utilize their medicinal properties. There are several ways to recognize a plant, like flower, root, leaf, fruit etc. In recent times computer vision methodologies and pattern recognition techniques have been applied towards automated procedures of plant recognition. Leaf classification and recognition is an important component of automated plant recognition system. Plant identification using leaf images is a very challenging task. Leaf features contain significant information that helps in plant species recognition. Leaf vein is an integral part of the leaf. The type of the vein is an important morphological characteristic of the leaf. The shape, size, texture and color of the leaves also play an important role in plant identification. In this paper, we have carried out a survey of plant leaf classification and various existing techniques used for classification. The paper is organized as follows. The next section provides the detailed review of some of the research carried out for plant leaf classification. Section 3 discusses the general approach used for classification. Section 4 presents the various leaf feature extraction techniques and Section 5 discusses the different feature classification methods used. 2. LITERATURE REVIEW Rashad, et al.[1] , introduced an approach for classification of plants which was based on the characterization of texture properties. They have used a combined classifier learning vector quantization along with the radial basis function. The proposed system has an ability to classify and recognize the plant from a small part of the leaf. The main advantage of this system is it neither depends on the shape of the leaf nor on its color feature as the system depends only on the textural features. This system is useful for the researchers of Botany who need to identify damaged plants. This system is applicable as the combined classifier method produced high performance which is superior to other tested methods as its correct recognition rate was 98.7% which has been revealed in the result. Kadir, et al.[2] , proposed a method that incorporates shape ,vein, color and texture features. They have used Probabilistic Neural Network(PNN) as a classifier for Plant Leaf Classification. There are several method available but none of them have captured color information because color was not recognized as an important aspect to the identification. In this paper, Fourier descriptors, slimness ratio, roundness ratio and dispersion were used to represent shape features. To represent color, color moments that consist of mean, standard deviation and skewness were used. Twelve texture features are extracted from lacunarity. The experimental result shows that the proposed method gives average accuracy of 93.75% when it was tested on Flavia dataset which contains 32 kinds of plant leaves. Hossain, et al.[3], proposed a method which works for the plants with broad flat leaves and which were more or less two dimensional in nature. The method used Probabilistic Neural Network for classification. In this method, the user selects the base point of the leaf and a few reference points on the leaf blades. On the basis of these points, the leaf shape is extracted from the background and a binary image is produced. Several morphological features were extracted such as eccentricity, area, perimeter, major axis, minor axis, equivalent diameter, convex area and extent by aligning the leaf horizontally with its base points on the left of the image. Several unique features were also extracted by slicing across the major axis and parallel to the minor axis. The proposed has been tested using ten-fold cross validation technique and it showed 91.41% average recognition accuracy. Zheng, et al.[4] , proposed a new method of leaf vein extraction based on gray scale morphology. The main idea of the method is to look upon the leaf vein as the noise on the leaf surface and adopt the method of noise detection to extract the leaf vein. This paper includes five steps : gray transformation, gray scale morphological processing, image enhancement, image segmentation and processing on details. The main advantage of this method, it is applicable for 205
  • 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME uneven illumination images. The basic idea of the method is also applicable to other linear object extraction. Zheng, et al.[5] ,proposed a universal method for the extraction of the leaf vein using hue and intensity information. Due to the color difference between the leaf vein and mesophyll, leaves are classified as the contrasting color leaf and the concolorous leaf. The vein of a contrasting color leaf can be extracted using hue information in HIS color space. Leaf vein extraction of concolorous leaf needs both hue and intensity information. Compared with other methods the proposed method is simple, fast and universal. The key problem is to identify a contrasting color leaf and a concolorous leaf automatically. Lee, et al.[6] , the proposed method implements a leaf recognition system using the leaf veins and shape for plant classification. The method used the main vein and the frequency domain data by using Fast Fourier Transform methods in conjunction with distance measurement between the contours and centroids on detected leaf images. Total 21 The experimental results showed that the performance of the proposed leaf recognition system is 97.19%. Uluturk, et al[7] , proposed a simple method which was based on bisection of leaves for recognition. After preprocessing techniques were applied on leaves, 7 low cost morphological features and 3 additional features using half leaf images were extracted. Many of the leaf species have morphological structure that resembles each other a lot. For these kind of leaves, the present new morphological features for plant identification which was based on splitting the leaf images vertically into two regions. Area, extent and eccentricity features were extracted for each part and their proportions to each other were taken. These all 10 features were used as an input to Probabilistic Neural Network. The results showed that the proposed method gives 92.5% recognition accuracy. The summary of literature review on plant leaf classification based on vein, shape, color and texture is depicted in the Table 1. Table 1 Summary of Literature Review Research Paper Classific ation Based on Classifiers Features Advantages Disadvantages [1] Plants Images Classificatio n Based on Textural Features using Combined Classifier [Aug 2011] Texture Combined Classifier (LVQ + RBF) 1. Ability of classifying and recognizing the plant from small part of the leaf. 2. Useful in cases when plant is damaged etc. 1.High Performance 2. No need to consider shape or color of leaf. 1. Do not consider noise. [2].Leaf Classificatio n using shape, color, and texture features [Aug 2011]. Shape, vein, color, and texture. 1. Make use of several features for classification. 2. Texture feature is based on lacunarity. 3. Color feature consideration 1. Better performance of the system due to consideration of several features. 2. Works on large dataset. 1. Lots of Mathematical calculation. Probabilist ic neural network (PNN). 206 Accuracy Dataset 30 98.7% 32 93.75%
  • 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME [3]Leaf Shape identificatio n Based Plant Biometrics [Dec 2010] Shape [4]Leaf Vein Extraction Based On Gray Scale Morphology Vein [5]Fast Leaf Vein Extraction using Hue and Intensity Information Vein [6] An Implementat ion of Leaf Recognition System Vein and shape [7]Recogniti on of Leaves Based on Morphologic al Features Derived From Two HalfRegions Shape Probabilist ic neural network (PNN). Can use Clustering Method Can use clustering method - Probabilist ic neural network (PNN). 1.Various morphological features are extracted. 2. Unique feature Leaf width Factor is used. 1.Easy to implement 2.Identify the type of plant from a partially damaged or broken leaf. 1.Require user help in preprocessing stage. 2.Inability to work with images with complicated background. 1. OTSU method used to segment leaf vein from background. 2.Gray-scale morphology used to remove overlapping between vein and background. 1. Feasible and more practical. 2. Applicable for uneven illumination images. 1.Processing speed is affected due to the width of the b structuring element. 1. Due to color difference between leaf vein and mesophyll, leaves are classified into two. 2. Vein of contrasting color leaf extracted using hue information and vein of concolorous leaf use both hue and intensity information. 1. Simple, fast and universal. 1. Identification of contrasting and concolorous leaf automatically. 1. Main vein and frequency domain data is used for extraction. 2. Total 21 features of distance, FFT and convex hull extracted. 1. Recognition rate of the system is better than the existing system. 1. Bisection of leaves done for recognition. 2. New features extracted using half leaf images. 1. Proposed features are adaptable with the data reduction techniques. 207 30 91.41% 1 - 1 - 1. Leaf image size and position of the dataset is not constant. 32 97.19% 32 92.5%
  • 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME 3. GENERAL CLASSIFICATION APPROACH Classification process is carried out through number of steps. Initially a leaf image database is constructed which consists of leaf sample pictures with their corresponding plant details. There is a lack of standard leaf image database that can be used for plant classification. First step for plant leaf classification is image acquisition which includes capturing the digital image of leaf with digital camera, and it is termed as an input image. In the second step, the input image is preprocessed to enhance the important features. In this step grayscale conversion, image segmentation, binary conversion and image smoothing is done. The main aim of image pre-processing is to improve image data so that it can remove undesired distortions and enhances the image features that are relevant for further processing. In the next step, the important features are extracted and are matched with the database image. The input image is categorized to the plant whose leaf image has maximum match score using some classifier giving the information of the inputted leaf. The overall classification process is shown in the Fig. 1. Figure 1. Block diagram for plant leaf classification 4. LEAF FEATURE EXTRACTION METHODS 4.1 Texture Feature Extraction 4.1.1 GLCM Grey Level Co-occurrence Matrices (GLCM) is a statistical method[15][16]. It is an old and widely used feature extraction method for texture classification. It remains an important feature extraction method in the domain of texture classification that computes the relationship between pixel pairs in the image. Based on the GLCM four statistical parameters energy, entropy, contrast 208
  • 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME and correlation are computed. Lastly a feature vector is computed using the means and variances of all the parameters. 4.1.2 Gabor Filters Gabor filters also popular as the Gabor wavelets, is a widely used signal processing method[15][17], The Gabor filters consists of parameters such as the radial center frequency, orientation and standard deviation. It can be used by defining a set of radial center frequencies and orientations. Even though orientation may vary, it usually covers 180° in direction in order to cover all possible orientations. Since signal processing methods produces large feature size, the Gabor filters needs to be downsized for the prevention of the dimensionality issues. 4.1.3 Fractal Measure (Lacunarity) Other method to get texture features is using fractals[2]. Although, the fractal dimension is not considered for a good texture description, there is a fractal measure known as “lacunarity” which is a measure of nonhomogeneity of the data as well as measures lumpiness of the data. It defined in term of the ratio of the variance over the mean value of the function. It may help in distinguishing two fractals with the same fractal dimension. We can define lacunarity by some predefined formulas which were originally applied to grayscale images. But we can also apply them to color images in our implementation using RGB values in order to increase the number of features to represent texture features. Table 2 Summary of the Texture Features Sr. No. [1] Technique Features Advantages Disadvantages GLCM (Gray Level CoOccurrence Matrix)[15][1 6] 1. It is a tabulation of how often different combinations of pixel brightness values (grey levels) occur in an image. 2. It is usually defined for aseries of “second order" texture calculations. 1. It is a signal processing method used for defining a set of radial center frequencies and orientations. 1. Smaller length of feature vector. 2. Used to estimate image properties related to second-order statistics. 3. It can be improved to be applied on different color space for color cooccurrence matrix. 1. It’s a multi-scale, Multiresolution filter. 2. It has selectivity for orientation, spectral bandwidth and spatial extent. 1. They require a lot Of computation (many matrices to be computed). 2. Features are not invariant to rotation or Scale changes in the texture. 1.Computational cost often high, due to the necessity of using a large bank of filters in most applications 1. Lacunarity analysis is a multi-scaled method of determining the texture associated with patterns of spatial dispersion 1. It is easily implemented on the computer and provides readily interpretable graphic results. 2. Differences in pattern can be detected even among very sparsely [2] Gabor Filters[15] [3] Fractal Measures(La cunarity)[2] 209 -
  • 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME 4.2 Color Feature Extraction 4.2.1 Color moments Color moments represent color features to characterize a color image[2]. Features involved are mean(µ), standard deviation(σ) and skewness(ϴ). In case of RGB color space, the three features are extracted from each plane R, G and B. 4.2.2 Color Histogram A histogram is the distribution of the number of pixels for an image[12][17].The number of elements in a histogram depends on the number of bits in each pixel of an image. For eg. , if we consider a pixel depth of n bit, the pixel values will be in between 0 and -1, and the histogram elements. will have 4.2.3 Color Averaging Technique Three color planes namely Red, Green and Blue are separated[11]. For each plane row mean and column mean of colours are calculated. Pictorially the average row and column mean is calculated as follows The average mean is calculated by adding up the mean of every row. The average of all row means and all columns means is calculated for each plane. The features of all 3 planes are combined to form a feature vector. As the feature vectors are generated for all images in the database, they are stored in a feature database. Table 3 Summary of the Color Feature Sr. No. Technique Color Moments[2] Feature 1. Characterise color distribution in an image 2. Mainly used for color indexing purposes Color Histogram [12][17] 1. Represent color distribution in an image 2. It is statistic that can be viewed as an approximation of an underlying continuous distributio n of colors values. Color Averaging Technique [11] 1. For each RGB plane row mean and column mean of colours are calculated. [1] [2] [3] Advantages 1. There is no need to store the complete color distribution. 2. Color and Shape are good feature to use under changing lighting condition. 3. Speeds up image retrieval since there are less features to compare. 1. Color information are faster to compute compared to other invariants. 2. color histogram are more often used for threedimensional spaces like RGB or HSV. 1. Size of the feature vector is small. 2. Classification speed is high. Disadvantages 1. High order color moments are not a part of the color moments as the required more data in order to obtain a good estimate of their values. 2. It cannot handle occlusion successfully. 1. It is based only on color, shape and texture of image are ignored. 2. It have high intensity towards noisy interference such as lightning intensity changes and quantization error. - 4.3 Shape Feature Extraction 4.3.1 Digital morphology features he features are extracted from the contours of leaf[3,6,7,8]. The digital morphology features (DMF) generally include geometrical features (GF) and invariable moment features (MF). The 210
  • 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME geometrical features consist of aspect ratio, rectangularity, area ratio of convexity, perimeter ratio of convexity, circularity, sphericity, eccentricity, form factor, etc. 4.3.2 Polar Fourier Transform Polar Fourier Transform (PFT) is very useful to capture shape of a leaf. The descriptors extracted from PFT are invariant under the action of translation, rotation, and scaling[8][2]. 4.3.3 Moment Invariant The moments are also widely used as the features for image processing and classification, which provide a more geometric and intuitive meaning than the morphological features[8][14]. It was Hu who first set out the mathematical foundation for two-dimensional moment invariants. Hu defined seven invariant moments computed from central moments through order three that are also invariant under object translation, rotation and scaling. Table 4 Summary of the Shape Feature Sr . No. Feature Advantages Digital Morphology Feature [3,6,7,8] 1. Feature is extracted from the contour of leaf. 2.It consist of geometrical features such as aspect ratio, area, perimeter etc. Polar Fourier Transform [1] Technique 1. Image is converted from Cartesian space to polar space. 2.The descriptors extracted from PFT are invariant under the action of translation, scaling, and rotation. 1.It provide a more geometric and intuitive meaning than the morphological features. 1. It provide critical information of the visual representation of the leaf. 2.It gives efficient classification of the leaves and for the detection of deformations and holes, in order to classify deformed samples, 1. Classification using PFT either in contours or regions are simple to compute, robust to noise and compact. [2] Moment Invariant 1. Computationally simple. 2. They are invariant to rotation, scaling and Translation [3] Disadvantages - - 1. Since the basis is not orthogonal, these moments suffer from a High degree of information redundancy 2. Higher-order moments are very sensitive to noise 4.4 Vein Features 4.4.1 Gray Scale Morphology Extracting leaf vein from the leaf in the image is usually regarded as a problem of image segmentation[4][13]. Image segmentation cannot be conducted directly on the gray image because the color difference is local between the leaf vein and its background. The gray overlap in the whole image leaf vein and the whole background should be eliminated before the image segmentation process. The purpose of gray-scale morphology processing is just to get rid of the gray overlap in the whole leaf vein and the whole background. 211
  • 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME 4.4.2 Using Hue And Intensity Information Since there is a color difference between the leaf vein and mesophyll, leaves can be classified as the contrasting leaf and concolorous leaf[5]. A contrasting color leaf is a leaf with an obvious difference between leaf vein and mesophyll. If the colors of leaf vein and mesophyll are similar or belong to a same color category, such a leaf is a concolorous leaf. The vein of a contrasting color leaf can be extracted using hue information in HIS color space. Leaf vein extraction of a concolorous leaf needs both hue and intensity information. 4.4.3 Independent Component Analysis Independent Component Analysis (ICA) is a signal processing method to extract independent sources given only observed data that are mixtures of the unknown sources[10]. This method is applied to the patches of leaf images to learn basis function and then the basis functions are used as the pattern map for vein detection. A gray scale image is transformed into a pattern map in which the leaf, edge, background and other pixels are classified into different classes by pattern matching. High accuracy of vein detection is achieved and it is free of the influences of illumination and there is no need to preprocessing. Table 5 Summary of the Vein Feature Sr. No. [1] [2] [3] 5. Technique Features Advantages Gray Scale 1. The purpose of gray-scale Morphology[ morphology processing is just 4][13] to get rid of the gray overlap in the whole leaf vein and the whole background Using Hue 1. Due to color difference and Intensity between leaf vein and Information mesophyll, leaves are [5] classified into two. 2. Vein of contrasting color leaf extracted using hue information and vein of concolorous leaf use both hue and intensity information Independent 1. Method is applied to the Component patches of leaf images to Analysis learn basis function and then [10] the basis functions are used as the pattern map for vein detection. 2. Method to extract independent sources. Disadvantages 1. Method is 1. Processing speed feasible and is affected due to the more practical. width of the b structuring element. 1. Method is 1. Identification of faster and more contrasting and applicable concolorous leaf automatically 1. High accuracy of vein detection is achieved. 2.No need of preprocessing 1.The performance of ICA algorithm is not better than those of Prewitt. 2.Results of ICA algorithm on whole image are not good. FEATURE CLASSIFICATION METHODS 5.1 k-Nearest Neighbor k-Nearest Neighbor classifier is used to calculate the minimum distance between the given point and other points to determine which class the given point belongs. It selects the training samples with the closest distance to the query sample[9][15]. Conceptually, this simple classifier computes the distance from the query sample to every training sample and selects the neighbor or neighbors that are having minimum distance. In terms of plant leaf classification; the distance to be 212
  • 10. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME calculated is termed as Euclidian distance. k-NN is a popular implementation where k number of best neighbors is selected (i.e. k is a small positive integer, k = 1). And the appropriate class is decided based on the highest number of votes from the k neighbors. The nearest neighbor is popular as simpler classifier since it does not include any training process. It is mainly applicable in case of a small dataset which is not trained. However, it suffers the limitation that the speed of computing distance increases according to the number available training samples 5.2 Learning Vector Quantization Learning Vector Quantization (LVQ) can be understood as a special case of an artificial neural network, and is a predecessor to Self-organizing maps (SOM)[9].It is a supervised version of vector quantization that can be used when we have labeled input data. An LVQ system can be represented as a set of prototypes given by W= (w(i),..., w(n)) which are defined in the observed data’s feature space. According to a given distance for each data point, the prototype that is much closer to the input is measured and the winner prototype is then adapted. If it gets incorrectly classified then moves away. An advantage of LVQ is that it creates easy to interpret prototypes used by an experts in the respective application domains and also applies to multi-class classification problems yielding variety of practical applications. A key issue in LVQ is the choice of an appropriate measure of distance or similarity for training and classification. 5.3 Probabilistic Neural Network (PNN) PNN is derived from Radial Basis Function (RBF) Network and it has parallel distributed processor that has a natural tendency for storing experiential knowledge[9]. It is predominantly a classifier that maps any input pattern to a number of classifications and can be forced into a more general function approximator. A PNN is an implementation of a statistical algorithm called kernel discriminate analysis in which the operations are organized into a multilayered feed forward network having four layers such as Input layer, Pattern layer, summation layer, and output layer. 5.4 Radial Basis Function A radial basis function (RBF) is a real-valued function whose value depends only on the distance from the origin. Any function that satisfies this property is a radial function[9]. The frequently used measuring norm is Euclidean distance. Basically, RBF’s are the networks where the activation of hidden units is based on the distance between the input vector and a prototype vector. There are several properties associated with variety of scientific disciplines. This includes function approximation, density estimation, regularization theory, and interpolation in the presence of noise. It allows for a straightforward interpretation of the internal representation produced by the hidden layer and training algorithms for RBFs are significantly faster than those for Probabilistic Neural Networks. 5.5 Support Vector Machine Support vector machine (SVM) is a non-linear classifier, which is a newer trend in machine learning algorithm and is popularly used in many pattern recognition problems, including texture classification. In SVM, the input data is non-linearly mapped to linearly separated data in some high dimensional space providing good classification performance[9][15]. SVM maximizes the marginal distance between different classes. SVM is designed to work with only two classes by determining the hyper plane to divide two classes. This ca be done by maximizing the margin from the hyper plane to the two classes. The samples closest to the margin that were selected to determine the hyper plane is known as support vectors. The main advantage of SVM is its simple geometric interpretation and a sparse solution. Unlike neural networks, the computational complexity of SVMs does not depend on the dimensionality of the input space. One of the drawbacks of the SVM is the large 213
  • 11. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME number of support vectors used from the training set to perform classification task. However, SVM is still considered to be powerful classifier, soon to be replacing the ANNs. 5.6 Genetic Algorithm Genetic Algorithms are mainly used for feature classification and feature selection. The basic purpose of genetic algorithms (GAs) is optimization[9] .GAs give a heuristic way of searching the input space for optimal x that approximates brute force without enumerating all the elements and therefore bypasses performance issues specific to exhaustive search. Genetic algorithm is used effectively in the evolution to find a near-optimal set of connection weights globally without computing gradient information and without weight connections initialization. Though solution found by genetic algorithms is not always best solution. It finds “good” solution always. Main advantage of GA is that is adaptable and it possess inherent parallelism. Genetic Algorithms handle large, complex, non differentiable and multi model spaces for image classification and many other real world applications. Table 6 Summary of the Feature Matching Techniques Sr.No. Techniques k-Nearest Neighbor(kNN)[9][15] Advantages 1. Simpler classifier since exclusion of any training process. 2. It is mainly applicable in case of a small dataset which is not trained. Disadvantages 1. The speed of computing distance increases according to the numbers available in training samples. 2. Expensive testing of each instance. 3. Sensitiveness to noisy or Irrelevant inputs. Learning Vector Quantization (LVQ)[9] 1. The choice of an appropriate measure of distance or similarity for training and classification. Radial Basis Function(RB F)[9] 1. It creates easy to interpret prototypes. 2. This can be applied to multiclass classification problems and useful in classifying textural features too. 1. Tolerant of noisy inputs and virtually no time consumed to train.. 2. Instances can be classified by more than one output. 3. Adaptive to changing data. 1. Training phase is faster. 2. The hidden layer is easier to interpret. Support Vector Machine(SV M)[9][15] 1. Simple geometric interpretation and a sparse solution. 2. SVMs can be robust, even when the training sample has some bias. Genetic Algorithm[9] 1. Handle large, complex, non differentiable and multi model spaces. 2. Refining irrelevant and noise genes. [1] [2] [3] [4] [5] [6] Probabilistic Neural Networks(P NN)[9] 214 1. Long training time. 2. Large complexity of network structure. 3. Need lot of memory for training data. 1. When training is finished and it is being used it is slower. So when speed is a factor then it is slower in execution. 1. Slow training. 2. Difficult to understand structure of algorithm. 3. Large no. support vectors are needed from the training set to perform classification task. 1. Complications involved in the representation of training/output data. 2. Not the most efficient method to find some optima, rather than global.
  • 12. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME As per the literature survey, the LVQ method is giving the best accuracy in classifying the plant leaf [1] [9]. We can also combine these techniques with other methods in order to achieve a higher accuracy. For example, Rashad, et al., [1] has invented the combined classifier i.e. (LVQ + RBF) giving the maximum accuracy (98.7%) in classifying plant leaf. 6. CONCLUSION In this survey, we have discussed a brief overview on Plant classification and its importance in recent years. We have also discussed the different ways in which the problem of accurate plant leaf classification has been formulated in literature. An overview of the literature on various techniques that can be used for extraction and classification of various leaf features are also discussed. In our survey we have found that there are various techniques present to extract the features but only few gives the best result like GLCM which is a new and popular texture extraction method, color moments for color feature extraction, moment invariant for shape extraction and gray scale morphology for vein extraction. REFERENCES [1] M. Z. Rashad, B. S. el-Desouky,and Manal S. Khawasik, Plants Images Classification Based on Textural Features using Combined Classifier, International Journal of Computer Science & Information Technology (IJCSIT), Vol 3, No. 4, August 2011,pp.93-100. [2] Abdul Kadir, Lukito Edi Nugroho, and Paulus Insap Santosa, Leaf classification using shape, color, and texture, International Journal of Computer Trends & Technology (IJCTT), JulyAugust 2011,pp.225-230. [3] Javed Hossain, M. Ashraful Amin, Leaf Shape Identification Based Plant Biometrics, 978-14244-8494-2/10/$26.00 ©2010 IEEE [4] Xiaodong Zheng, Xiaojie Wang, Leaf Vein Extraction Based on Gray-scale Morphology, I.J. Image, Graphics and Signal Processing, 2010, 2, 25-31 Published Online December 2010 in MECS (http://www.mecs-press.org/) [5] Xiaodong Zheng, Xiaojie Wang, Fast Leaf Vein Extraction using Hue and Intensity Information, 978-1-4244-4994-1/09/$25.00 ©2009 IEEE [6] Kue-Bum Lee, Kwang-Woo Chung and Kwang-Seok Hong, An Implementation of Leaf Recognition System, ISA 2013, ASTL Vol. 21, pp. 152 - 155, 2013 © SERSC 2013 [7] Caner Uluturk, Aybars Ugur, Recognition of Leaves Based on Morphological Features Derived From Two Half-Regions 978-1-4673-1448-0/12/$31.00 ©2012 IEEE. [8] A. Kadir,L.E. Nugroho, A. Susanto and P.I. Santosa, A Comparative Experiment of Several Shape Methods In Recognizing Plants , International Journal of Computer Science & Information Technology (IJCSIT), Vol 3, No 3, June 2011 [9] Prof. Meeta Kumar, Mrunali Kamble, Shubhada Pawar, Prajakta Patil, Neha Bonde, Survey on Techniques for Plant Leaf Classification , International Journal of Modern Engineering Research (IJMER) www.ijmer.com Vol.1, Issue.2, pp-538-544 ISSN: 2249-6645 [10] Yan Li, Zheru Chi, David D. Feng, Leaf Vein Extraction Using Independent Component Analysis, 1-4244-0100-3/06/$20.00 C2006 IEEE [11] Dr. H.B.Kekre ,Dhirendra Mishra ,Stuti Narula ,Vidhi Shah, Color Feature Extraction For CBIR, ISSN : 0975-5462 Vol. 3 No.12 December 2011 [12] Jayamala K. Patil, Raj Kumar, Color Feature Extraction of Tomato Leaf Diseases, ISSN: 2231-5381 http://www.internationaljournalssrg.org 215
  • 13. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME [13] Xiaodong Zheng,Xiaojie Wang, Leaf vein extraction using a combined operation of mathematical morphology, 978-1-4244-7941-2/10/$26.00 ©2010 IEEE [14] A.H. Kulkarni, Dr. H.M.Rai, Dr. K.A.Jahagirdar, P.S.Upparamani, A Leaf Recognition Technique for Plant Classification Using RBPNN and Zernike Moments, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 1, January 2013 [15] Jing Yi Tou, Yong Haur Tay, Phooi Yee Lau, Recent Trends In Texture Classification: A Review, Symposium On Progress In Information & Communication Technology 2009 [16] Gurpreet Kaur, Gurpinder Kaur, Classification of Biological Species Based on Leaf Architecture, International Journal of Engineering Research and Development ISSN: 2278067X, Volume 1, Issue 6 (June 2012), PP.35-42 www.ijerd.com [17] Hanife Kebapci, Berrin Yanikoglu and Gozde Unal, Plant Image Retrieval Using Color, Shape and Texture Features, The Computer Journal Advance Access published April 9, 2010. [18] Nisargkumar Patel, Prof. V.V. Shete and Ashwini Charantimath, “LVQ Based Person Identification System”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 4, 2013, pp. 185 - 193, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [19] Ajeesh S. S. and Indu M.S., “Feature Extraction Techniques on CBIR-A Review”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 4, 2013, pp. 467 - 474, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 216