There are many foods in the form of grains in the food industry. Rice is a particularly important crop because it is a staple food. Since mislabeling rice grain varieties is a problem, it is desirable to be able to recognize and identify specific characteristics. In this study, the digital imaging approach was designed to investigate different types of traits to identify different types of rice. We introduce digital image recognition as an effective method for noncontact extraction of rice grain properties and present an automated system that can be used to identify and classify rice grain types. Images of rice are acquired using a camera. Image preprocessing, segmentation, and feature extraction techniques are checks performed on acquired images. Morphological features extracted from the images are fed into a neural network pattern recognition tool. This experiment was proposed to classify and identify specific rice samples based on their morphological features

Segmentation, Feature Extraction, ANN Classifier.