Presented by: Kamakhaya Argulewar Guided by: Prof. Shweta V. Jain Technical seminar on CLASSIFICATION OF RICE GRANULES USING IMAGE PROCESSING AND NEURAL NETWORK Presented by: Kamakhaya Argulewar Guided by: Prof. Shweta V. Jain

Overview Introduction Papers Read Flow Diagram of Classification and Grading Techniques Technique for classification Issues in Existing system Conclusion Future Work References

INTRODUCTION Classification includes a broad range of decision-theoretic approaches to the identification of images . All classification algorithms are based on the assumption that the image in question depicts one or more features and that each of these features belongs to one of several distinct and exclusive classes. Image classification analyzes the numerical properties of various image features and organizes data into categories.

PAPERS READ Sr. No. Paper Name Author Year Conclusion 1 Analysis of rice granules using Image Processing and Neural Network Neelamegam. P, Abirami. S, Vishnu Priya. K, Rubalya Valantina.S. IEEE 2013 Back propagation based neural network well classify the rice granules. 2 A Grain Quality Classification System L.A.I.Pabamalie,H.L.Premaratne IEEE 2010 This research has been done to identify the relevant quality category for a given rice sample

CONT…. In food handling industry, grading of granular food materials is necessary because samples of material are subjected to adulteration. Existing system work on the feature which were extracted from images of rice kernels are parameter, Area, Minor-axis Length and Major-axis Length ,texture feature using Contour detection .

FLOW DIAGRAM OF CLASSIFICATION

IMAGE ACQUISITION : The first step in classification is image acquisition. This acquire image is given as input to pre-processing.

PREPROCESSING: Smoothing: Filtering technique is used to remove noise from image . Thresholding : It is the method of image segmentation . From a gray scale image threshold can create a binary image.

EDGE DETECTION TECHNIQUES 1) Sobel Edge Detection: In Sobel edge detection, for each position of the pixel in the image the gradient is calculated. Series of gradient magnitudes are created using a simple convolution kernel.

2. CANNY EDGE DETECTION Canny edge detector is an optimal detector which gives optimal filtered image. Canny edge detector also contain weak edges which is connected to strong edges.

FEATURE EXTRACTION Extraction of information from the image is base on feature extraction. Object recognition and classifications are performed based on the feature extraction.

TEXTURE FEATURE EXTRACTION At the beginning of texture feature extraction cropped the rice image from its background. Which reduces the background effect from the image. When creating the gray level co-occurrence matrix we have been considered R, G, B channels separately and creates three matrixes with 255 * 255*16 size based on these three channels. Pixel values of R, G, and B channels always in between 0-255. Therefore, size of the GLCM was 255*255.

They considered four angles which were 0°,45°, 90° and 135° to access the adjacent pixels from a particular pixel location. It has been considered four adjacent pixel distances, 1, 2, 3 and 4 for a particular direction. Finally, there were sixteen GLCM matrixes have been created for a particular channel regarding four directions and four pixel distance. Then, calculate values for those GLCM matrixes. Extract texture feature values using those sixteen GLCM matrixes and finally calculate the average value of them.

NEURAL NETWORK Supervised classification of objects into predefined categories. Neural network is typically organized in layers. layers are made up of number of interconnected node . Pattern are presented to the network via the input layer which communicate to one or more hidden layers where the actual processing is done via a system of weighted connection. The hidden layers then link to an output layer .

NEURAL NETWORK ARCHITECTURE

NEURAL NETWORK SPECIFICATION neural network was used for the classification based on the extracted features from the rice samples. The neural network is built with three neurons in input layer, seven neurons in the hidden layer and one neuron in the output layer. The network used for classification is back propagation algorithm.

CONT….. During the training, neural network weights are initiated with random values. The weights are stored during the end of training. When the training has completed, the network can be tested to calculate the accuracy with stored weights.

BACK PROPOGATION ALGORITHM Back propagation have two phases: Forward pass phase: computes ‘functional signal’, feed forward propagation of input pattern signals through network. Backward pass phase: computes ‘error signal’, propagates the error backwards through network starting at output units.

Errors output

ISSUES IN EXISTING SYSTEM Neural network can not work well in the presence of overlapping grains. Neural network does not accurately classifies the rice granules when there is overlapping of grains.

CONCLUSION Back Propagation based Neural Network is able to classify well when there is no overlapping of granules.

FUTURE WORK To make the result more accurate more features can be calculated.

REFERENCES Neelamegam. P, Abirami. S, Vishnu Priya. K, Rubalya Valantina.S. “Analysis of rice granules using Image Processing and Neural Network “Proceedings of 2013 IEEE Conference on Informati.on and Communication Technologies (ICT 2013). Bhupinder Verma “Image Processing Techniques for Grading & Classification of Rice” Int’l Conf. on Computer & Communication Technology . L.A.I.Pabamalie, H.L.Premaratne” A Grain Quality Classification System” 2010 IEEE Conference . on Computer & Communication Technology .

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