(untitled) (Global Journal of Computer Science and Technology, 2014)
Use of Image processing technique is increasing day by day in all fields and including the agriculture to classify fruits. Shape, color and texture are the image features which help in classification of fruits. This paper proposes an algorithm for fruits classification based on the shape, color and texture. For shape based classification of fruit area, perimeter, major axis length and minor axis length is calculated. Shape features are calculated by segmenting the object with the background using edge detection techniques. Mean and standard deviation is calculated for the color space like HSI, HSV which can be used for color base classification. Texture features is also calculated to enhance the classification process. Gray Level Co-occurrence Matrix (GLCM) is used to calculate texture features. Artificial neural network is used for classification of fruits. Artificial neural network classifies the fruits by comparing shape, color and texture feature provided at the time of training. MATLAB/ SIMULINK software is used to obtain result. Results obtained are better over the previous techniques and gives the accuracy upto 96%.