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50120130406019 2

  1. 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 6, November - December (2013), © IAEME TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 6, November - December (2013), pp. 167-174 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET ©IAEME IDENTIFICATION OF ARABLE AND TREE CROPS BY EDGE AND TEXTURE FUSION TECHNIQUES C. S. Sumathi Assistant Professor, O/o Dean (SPGS), TamilNadu Agricultural University, Coimbatore- 641 003, Tamil Nadu, India A.V. Senthil Kumar Director, Department of Post Graduate and Research in Computer Applications, Hindusthan College of Arts and Science, Coimbatore- 641 028, Tamil Nadu, India ABSTRACT Crop identification has vast applications in agriculture and medicine. Leaves are classified as simple or compound leaves by their characteristics. It is important to classify them accurately as they are important for the country’s economy and environmental protection. This research suggests a new leaf image based arable and tree crops identification process. Leaves come in many shapes and sizes; they are often used to help identify plants. Some leaves are flat and wide; others are spiky and thin. They are made of a single leaf blade connected by a petiole to the stem. Data’s were collected by the use of digital camera, which are then pre-processed and used. Edge and texture extraction are through Gabor filter, fusing them for classification. CART and Radial basis function measure classification accuracy. The experimental result shows this method for classification gives average accuracy of 88.21% when tested with 195 instances of 6 species of arable and tree crops leaf images. Index terms: Crop Identification, Leaf Image, Gabor Filter, CART, RBF, Sobel Edge Detector, Texture, Edge. I. INTRODUCTION Plant species identifies individual plants correctly assigning them to descending series of related groups through common characteristics [1]. Plants are sources for living be it industry, food or medicine. Arable farming refers to agriculture concerned with cultivation of field crops on fertile land. The crops can be cereals, vegetables and plants that produce oil or cloth. Arable land is occupied by crops of both source and harvested during the same agricultural year and sometimes 167
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME more than once. It usually has a nearby water source and an adequate temperature and requires less attention than livestock farming. Leaves are basis for identifying plants as they are observed easily. Tree crops are groves or orchards of tree grown for economic and environmental benefit while fruit trees are the common types of tree crop. Tree crops may be grown in massive quantities, but popular for small business and family farms. In many regions tree crops make up a significant portion of farming community. Fruit trees are typically grown for their seasonal harvest and may live for decades as producing trees. Leaves on a plant have different sizes and appearance varying in size, color and shape. Some leaves are unique and easy to identify and some are similar and hard to classify, as many crops share leaves of similar shape. To securely identify a crop it is necessary to look at the features of leaves viz., whether it is a toothed or lobed leaves, compound or simple leaves, texture of the leaves etc., Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. This software is one of a number of analytical tools for analyzing data. It also allows users to analyze data from many different dimensions and summarize the relationships identified. Different levels of analysis include Artificial Neural Networks, Genetic Algorithms, Decision trees, Nearest Neighbor method, Rule induction and Data visualization. Classification, association rule, clustering etc. are the methodologies to approach the various problems. To classify the leaves, classification technique is applied to obtain better results. Automatic digital plant classification and retrieval are by extracting features from leaves [2]. This work identifies crops based on shape and texture. Plants play a role in preserving ecology and environment, maintaining atmosphere and providing sustenance and shelter to insect and animal species [2]. Botanists name a plant to ensure its unique place in the biological world and to clarify its relationships in that world. Classification is difficult. As modern botany advances, increasing understanding of biochemical mechanisms, criteria of plant classification has been transformed. Also, nature is not fixed, and plants are capable to changes. Despite great advances in botany, there are plants yet to be discovered, classified, and utilized; unknown plants are treasures awaiting finding. Ethno botanists combine world regions looking for tomorrow's medicines and food crops. They explore plants functional properties and relationships in ecosystems to understand the diversity in how we manage plant resources The plant world is in constant flux. Due to human and other factors, many plants and animals becoming extinct are possible. Plant classification aids tracking our planet's endangered inhabitants. Realizing the need to understand ecological systems, preserving biodiversity, scientists are exploring how genetic diversity and ecological sensitivity are necessary to solve problems like feeding populations and fighting disease. Plant classification is vital for this. However, there are disadvantages as some are appropriate to certain species and some methods compare feature similarity when it is required that the human element enter queries manually [2]. According to Choras [3], texture is a powerful regional descriptor helping retrieval. Texture, on its own is incapable of finding similar images, but can classify textured images from non-textured ones and then combine with other visual attributes like shape to ensure effective retrieval. Texture features are extracted through various methods. Local binary pattern (LBP), gray-level occurrence matrices (GLCMs) and Gabor Filter are popular feature extraction methods [4, 5]. Most approaches use global shape features. This research proposes to improve leaf image classification by considering edge and texture features. II. LITERATURE REVIEW A contour signature classification of plant species was proposed by Beghin, et al, [6]. The shape-based method extracts contour signature from leaves and calculates dissimilarities between them using a Jeffrey-divergence measure. The orientation of edge gradients analyzes the leaf’s 168
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME macro-texture. This method presents good results for 10 of 18 species which were successfully classified with classification rates of about 81.1% A new classification method Move Median Centers [MMC], based on digital morphological features was proposed by Du, et al., [7]. Plant leaf is acquired through a digital camera and its grey level image is obtained after preprocessing. Features are extracted from leaf contours. Digital morphological features include geometrical features and invariable moment features. Instances are trained with MMC to ensure efficient plant recognition classifier with a recognition rate greater than 75% for every class exceeding three samples. Hence experiment results proved the new method is effective by comparing 1-NN and k-NN classifier and MMC classifier leading to saving storage space and reducing classification time. Lee and Chen [8] constructed a leaf database and proposed region based features classification. It is robust than contour based features as major curvature points are hard to detect. The experiment classifies every test image to a single class as all leaf images are real and true classes known. This method used 3 features, including Centroid Contour Distance (CCD) curve, eccentricity and Angle Code Histogram (ACH). This method’s effectiveness is demonstrated with experiments revealing 82.33% classification accuracy for 1-NN rule and recall rate for 10 returned images as 48.27%. A novel approach for plant classification based on texture properties characterization was introduced by Rashad, et al., [9]. They used a combined classifier learning vector quantization with radial basis function. The new system’s ability to classify and recognize a plant from a leaf’s part is its advantage. Without depending on the leaf’s shape or its color features, it classifies a plant with only a portion as the new system needs textural features alone. This system is useful for researchers who identify damaged plants as this is possible from a small available part. This system is applicable as a combined classifier procedure producing high performance, superior to tested methods with 98.7% correct recognition rate as seen in results. A method that incorporates shape, vein, color, and texture features was proposed by Kadir, et al., [10]. They used Probabilistic Neural Networks (PNN) as classifier for plant leaf classification. Several methods exist for plant leaf classification, but none captured color information as color was not recognized as an important identification aspect. Here, color plays an important role in identification. Experiment reveal that the proposed method provides 93.75% average accuracy when tested on Flavia dataset having 32 plant leaf kinds. III. METHODOLOGY Edge detection detects pixels representing the boundary image. Edge orientation, edge structure and noise environment are variables involved in edge operator selection. Laplacian based Edge detection, Sobel edge detection, gradient based edge detection and Canny edge detection methods are common edge detection methods. This research uses Sobel edge detection method with default threshold is being applied for edge detection. Sobel edge detector is a discrete differentiation operator using derivative approximation to locate edges. Resulting gradient approximations at every image point combined to give gradient magnitude represented as Edge location is declared if gradient value exceeds a threshold. When threshold value exceeds gradient value it is compared to threshold value and the edge is detected. 169
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME A gradient magnitudes series is created using a simple convolution kernel. The convolution is mathematically represented as: 1 N ( x, y ) = 1 ∑ ∑ K ( j, k ) p ( x − j, y − k ) k =−1 j =−1 For easy computation, Sobel detector uses 2 convolution kernels to detect changes in horizontal contrast (hy) and vertical contrast (hx). Standard Sobel operators, for a 3×3 neighborhood, each a simple central gradient estimate is vector sum of a pair of orthogonal vectors [11]. Every orthogonal vector is a directional derivative estimate multiplied by a unit vector specifying derivative’s direction. Vector sum of these simple gradient estimates amount to a vector sum of 8 directional derivative vectors. Pseudo-Codes For Sobel Edge Detection Input: A Sample Image. Output: Detected Edges. Step 1: Accept the input image. Step 2: Apply mask Gx,Gy to the input image. Step 3: Apply Sobel edge detection algorithm and the gradient. Step 4: Masks manipulation of Gx,Gy separately on the input image. Step 5: Results combined to find the absolute magnitude of the gradient. Step 6: The absolute magnitude is the output edges. The Gabor wavelet represents a band-pass linear filter, and harmonic function by a Gaussian function provides impulse response. Segmenting texture requires partitioning images into different textured regions accurately. The 2D Gabor filter constitutes a sinusoidal plane of specific frequency and modulated Gaussian. The approach uses 2D Gabor filter for leaf classification having form, where h(x,y) = cos (2πu0x) represents Gaussian curve directions along x and y axis respectively and u0 modulation frequency. This method’s advantage is that arable crops leaves are identified without dependence on color or whole leaf or its characters as it requires only the arable leaf’s texture and edge. Hence, this method is used by scientists who identify different crops. Moreover, the new system can be used to identify crops based on leaves available as this system requires only leaf texture and edge and not color or other features. Arable crops like neem, guava, sapotta, mango, sunflower which are highly dynamic in nature were classified with a combined classifier method. Results reveal that the new method produced high performance compared to other tested methods and hence was applied. 170
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME Flowchart IV. RESULT AND DISCUSSION Six species and a total of 195 samples of arable crops were selected. Sample image of the leaves used is shown in figure 1. The features are extracted using Sobel edge and Gabor filters. The extracted features are classified using Classification and Regression Trees (CART) and Radial Basis Kernel (RBF). Figure 1: Leaf Samples taken from a digital camera 171
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME Matlab was used to extract the edge and texture features and fused. The feature extracted using Sobel edge detector is shown in figure 2 Figure 2: Edge feature extracted Gabor filter, used to extract the texture are shown in figure 3 and figure 4 respectively. Figure 3: Gabor features Figure 4: DCT representation of frequency domain Classification accuracy of CART and RBF are given in figure 5, figure 6, figure 7. Figure 5: Classification accuracy 172
  7. 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME Figure 6: Root relative squared error Figure 7: Root mean squared error It is observed that the CART achieves the best classification accuracy of 88.21%. Further investigations are required to improve the classification accuracy. V. CONCLUSION This research proposed a fusion of features of arable crops using Gabor filter and Sobel edge detector for edge based features. CART and RBF classifiers trained and tested extracted features. The output with CART is feasible with low percentage of incorrectly classified instances and relative squared error for 195 instances. It can be seen that CART outperforms RBF by improvement in recall and precision. Further work includes classification of deficit leaves and combining flexible neural networks to increase the accuracy rate are necessary. 173
  8. 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. Du, J. X. et al. (2006). Computer-aided plant species identification (CAPSI) based on leaf shape matching technique. Transaction of the Institute of Measurement and Control. Vol 28: 275-284. C.S.Sumathi and A.V.Senthil Kumar, “International Journal of Computer Science and Telecommunicaitons”, Vol 3, 2012 (online). R.S. Choras, “Image Feature Extraction Techniques and their Application from CBIR and Biometrics Sytems”, International Journal of Biology and Biomedical Engineering, Vol. 1(1), pp 6-16, 2007. Heymans, B. C. et al. (1991). A neural network for opuntia leaf-form recognition. Proceedings of IEEE International Joint Conference on Neural Networks, 1991. 18-21 November 1991. Singapore. Vol 3: 2116-2121. Vishakha Metre, Jayshree Ghorpade, “An overview of the research on texture based plant leaf classification”, International Journal of Computer Science and Network(IJCSN), Vol 2, Issue 3, 2013 (online) :2277-5420. Thibaut Beghin, James S Cope, Paolo Rernagnio and Sarah Barman “Shape and Texture Based Plant Leaf Classification”, Advanced concepts of Intelligent Systems volume 6473, 2010, pp 345-353. Ji-Xiang Du, Xiao-Feng Wand, Guo-Jun Z hang, “Leaf shape based plant species recognition”, Applied mathematics and Computations 185(2007) 883-89316th IPPR, Conference on Computer Vision, Graphics and Image Processing (CVGIP 2003) 2003/8/17-19. Lee, C. L., & Chen, S. Y. (2006). Classification of leaf images. International Journal of Imaging Systems and Technology, 16(1), 15-23. 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. Abdul Kadir, Lukito Edi Nugroho, and Paulus Insap Santosa, “Leaf classification using shape, color, and texture”, International Journal of Computer Trends & Technology (IJCTT), July-August 2011, pp.225-230. SOBEL, I., An Isotropic 3×3 Gradient Operator, Machine Vision for Three – Dimensional Scenes, Freeman, H., Academic Pres, NY, 376-379, 1990. 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. Abhishek Choubey, Omprakash Firke and Bahgwan Swaroop Sharma, “Rotation and Illumination Invariant Image Retrieval using Texture Features”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 3, Issue 2, 2013, pp. 48 - 55, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. Jaspreet Kaur and Chirag Sharma, “Multimodality Medical Image Fusion using Improved Contourlet Transformation with Log Gabor Filters”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 4, Issue 2, 2013, pp. 383 - 389, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. Garima Agarwal, Rekha Nair and Pravin Shrinath, “A Review of Plant Leaf Classification Features and Techniques”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 5, 2013, pp. 204 - 216, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 174

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