Quality Grading of a Pea Using Artificial Intelligence

In the recent years, computer vision has emerged as a prospective field, related to the recognition of the object by a computer or machine. In the presented work, the application of computer vision to extract the features of a pea is explored. Feature related to pea are shape, texture, and color. The present work analyzes the object, on the basis of surface areas of the pea, computed from different angle. The quality assigning system based on artificial intelligence is developed. The input to back propagation neural network (BPNN) is range data, consisting of surface areas from different views of object. Surface based analysis technique has advantage that the recognition of object becomes simpler and faster. BPNN uses mean square error as a performance index. The selected network models are simulated with available test data, to evaluate the performance. The result shows the effectiveness of the proposed approach to classify the pea on basis of quality. Key word: Image segmentation, edge detection, back propagation neural network, classifier design.