Analysis of rice granules using image processing and neural network - Neelamegam, P.; Abirami, S.; Vishnu Priya, K.; Rubalya Valantina, S. (2013 IEEE Conference on Information and Communication Technologies, 2013)
In food handling industry, grading of granular food materials is necessary because samples of material are subjected to adulteration. In the past, food products in the form of particles or granules were passed through sieves or other mechanical means for grading purposes. In this paper, analysis is performed on basmati rice granules; to evaluate the performance using image processing and Neural Network is implemented based on the features extracted from rice granules for classification grades of granules. Digital imaging is recognized as an efficient technique, to extract the features from rice granules in a non-contact manner. Images are acquired for rice using camera. Conversion to gray scale, Median smoothing, Adaptive thresholding, Canny edge detection, Sobel edge Detection, morphological operations, extraction of quantitative information are the checks that are performed on the acquired image using image processing technique through Open source Computer Vision (Open CV) which is a library of functions that aids image processing in real time. The morphological features acquired from the image are given to Neural Network. This work has been done to identify the relevant quality category for a given rice sample based on its parameters. The performance of image processing reduced the time of operation and improved the crop recognition greatly. Grading results obtained from Neural Network system shows greater accuracy when compared with the outputs from human experts.