Recognition and Classification of Different Types of Food Grains and Detection of Foreign Bodies using Neural Networks

Abstract

This paper deals with the classification of bulk food grain samples and detection of foreign bodies in food grains. A new method for inspecting food samples is presented, using ANN and segmentation to classify grain samples and detect foreign bodies that are not detectable using conventional methods easily. A BPNN based classifier is designed to classify the unknown grain samples. The algorithms are developed to extract color, texture and combined features are extracted from grains and after normalization presented to neural network for training purpose. The trained network is then used to identify the unknown grain type and it's quality in terms of pure/impure type. A Segmentation based detection model is developed to

Cite this paper

@inproceedings{Gujjar2014RecognitionAC, title={Recognition and Classification of Different Types of Food Grains and Detection of Foreign Bodies using Neural Networks}, author={Harish S Gujjar and M. Siddappa and Basavaraj S. Anami and D. G. Savakar and Cheng-Jin Du and Prakash H. Unki}, year={2014} }