* Mohit Mudgil Research Scholar, PDM College of Engineering, Bahadurgarh, Distt. Jhajjar (HARYANA).

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1 Multi-Scale Distance Matrix for leaf Recognition using MATLAB * Mohit Mudgil Research Scholar, PDM College of Engineering, Bahadurgarh, Distt. Jhajjar (HARYANA). ** Rajiv Dahiya H.O.D. PDM College of Engineering, Bahadurgarh, Distt. Jhajjar (HARYANA). ABSTRACT The most important features of an object is shape and size. It plays a key role in many object recognition tasks. Each leaf has its own features and carries significant information that can help people to recognise and classify the plant by looking at it. Leaf shape is a prominent feature that most people use to recognise and classify a plant Diameter, physiological length, physiological width, leaf area and perimeter are basic geometry information can be extract from the leaf shape. KEYWORDS : Image Acquisition, Shape recognition, Multi Scale Distance Matrix, MATLAB. I. INTRODUCTION Leaf of different plants has different characteristics which can be used to classify them. Shape is one of the most important features of an object. It plays a key role in many object recognition tasks, in which objects are easily distinguished by shape rather than other features such as edge, corner, colours, and texture. There are usually two critical parts in a shape recognition approach, shape representation and shape matching. We describe a visual recognition system for automatic plant species identification. A typical contour based shape recognition approach contains two stages: shape representation and shape matching. We can divide contour based approaches into two Global International Research Thoughts (GIRT) Page 70

2 subcategories, i.e., local based and global based. The local based approaches extract local features of a shape and then find the correspondence of contour points in the matching stage, while the global based approaches extract global features of a shape, and perform the matching without finding the correspondence of contour points. II. PROPOSED WORK The proposed work is concerned with Multi scale Distance Matrix for Fast Plant Leaf Recognition. We study the detail literature survey on the plant leaf recognition techniques used by different authors. Then some biological term that how a Computer can recognize leaf based on the features of different leaf such as leaf shape, leaf colour, textures and vein. Finally, a computational model of leaf recognition which is fast, reasonably simple and accurate in constrained environments is presented [3]. Different Techniques are used for the feature extraction of the leaf for plant identification [4]. This approach transforms plant leafs images into smaller set of characteristic feature images is called Eigen faces which is principal components of the initial training set of images. Recognition is performed by projecting a new leaf image into the subspace spanned by the Eigen faces and then classifying the leaf by comparing its different feature such as diameter, physiological length, physiological width, leaf area and perimeter are basic geometry information can be extract from the leaf shape. Different features used for the Plant identifications are shapes, colours, textures and structures of their leaf, bark, flower, seedling and morph[5]. The proposed algorithm is simple, easy to implement. Here, performance of the propose distance matrix method will accurately identified the plant. To reduce the computation time we classified the plant leaf in different classes such that similar characteristics plant leaf will be presented in the same data base such that can easily recognized. Computation time is also reduced by this method which is the basic requirement. III. SIMULATION In this Chapter, the analysis of the plant identification based on the feature extraction is presented. Simulation has been done in MATLAB and image processing toolbox of matlab. Leaf shape is a prominent feature that most people use to recognise and classify a plant in had stated that diameter, physiological length, physiological width, leaf area and perimeter are basic geometry information can be extract from the leaf shape. In addition, leaf colour, textures and vein are also considered as features. All these features are useful for recognition and classification of leaf image. Plants can be usually identified according to the shapes, colors, textures and structures of their leaf, bark, flower, seedling and morph. Following steps are followed feature extraction of the multi scale matrix is given as 1. Each test image is first mean centered by subtracting the mean image. t i =t i A (3.1) 2. Then, projected into the same eigen space defined by V t i=vt t i (3.2) Global International Research Thoughts (GIRT) Page 71

3 We get the feature vector of the testing image leaf. 3. Now, calculate the euclidean distance [6] to measure the distance between the projected feature vector of the test image with projected feature vector of each leaf image in the training set. = t i i (3.3) 4. At the last compare the euclidean distance, and showing the leaf image which has minimum euclidean distance. We present the leaf identification of a particular type based on the multi scale matrix mated. The main purpose of this thesis is to identify the type of plant using different features of the plants. Some of the commonly used feature of the plant identification is colour, vein, and texture, geometric and Morphological Feature. Based on these feature we identify the type of leaf and on that bases we identify the plants. The development of an automatic system for plant recognition through leaf images could provide both specialists and non specialists with a valuable tool with reduced or no costs. IV. RESULT Fig. 4.1 shows the test leaf image used for multi scale matrix method. Fig. 4.2 shows the different types of images that are presented in the data base. This data base can be modified by adding or deleting more number of images. Fig. 4.3 shows the Eigen images of the present images in the data base. This will provide better approximation to the best match. Fig. 4.4 shows the identified image from the existing data base. This the image that best approximate the test image on the basing of different features such as colour, vein, and texture, geometric and Morphological Feature. Fig. 4.1 Tested Leaf for multi scale matrix method Global International Research Thoughts (GIRT) Page 72

4 Fig. 4.2 Data base of the different types of leaf Fig. 4.3 Eigen images of the leaf present in the database The test image is first converted into grayscale and then converted into Binary scale. After that the eigen values of test image is compared with the eigen values of each image present in the database. At the output we get the image whose eigen values matches appropriately with the eigen values of the image in the database. Fig. 4.4 Identified Leaf for multi scale matrix method. V CONCLUSION We are using different geometric feature for the identification of the plant leaf based on this we can identify the plant type. Different features that are considered other than Global International Research Thoughts (GIRT) Page 73

5 geometric feature, leaf colour, textures and vein. Result reveals that the matrix method perform superior than the other conventional method in terms of accuracy and time. We consider the multi scale matrix method for the identification of particular type of plant. This will make it possible to identify the plant by computer based approach. Other techniques are also implemented for the improving the accuracy in plant identification. In this case, we are using a database of eight leaves. However, we can generalize this method for the large data. This make it is applicable to the different application of biomedical. REFERENCES 1. Ji Xiang Du, Xiao Feng Wang, Guo Jun Zhang Leaf shape based plant species recognition Department of Automation, University of Science and Technology of China, Applied Mathematics and Computation 185 (2007) Y. Li et al., A leaf vein extraction method based on snakes technique, in Proc. Neural Networks and Brain Int. Conf., Beijing, China, Matthew Alan Turk Interactive Time Vision: Face Recognition as a Visual Behavior Massachusetts Institute of Technology A.P. Cisilino, M.H. Aliabadi, A boundary element method for three dimensional elastoplastic problems Engineering Computations, Vol. 15 Iss: 8, pp Dr. V.S.Rajpurohit, Shruti Bhat, Shreya Devurkar, Shubhangi Patil, Supriya Sirbi Application of Image Processing Techniques in Object Shape Recognition International Journal of Latest Trends in Engineering and Technology (IJLTET) Special Issue IDEAS Javed, J., Yasin, H., Ali, S.F. Human movement Recognition using Euclidean Distance: A tricky approach, Image and Signal Processing (CISP), rd International Congress. Global International Research Thoughts (GIRT) Page 74

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