GRADING AND QUALITY TESTING OF FOOD GRAINS USING NEURAL NETWORK

Abstract

The quality of food grains is referred to the every aspect of the profit of supply and marketing. The varietals purity is one of the factors whose inspection is more difficult and more complicated than that of other factors. In the present grain-handling system, grain type and quality are rapidly assessed by visual inspection. This evaluation process is, however, tedious and time consuming. The decision-making capabilities of a grain inspector can be seriously affected by his/her physical condition such as fatigue and eyesight, mental state caused by biases and work pressure, and working conditions such as improper lighting, climate, etc. The farmers are affected by this manual activity. Hence, these tasks require automation and develop imaging systems that can be helpful to identify quality of grain images. A model of quality grade testing and identification is built which is based on appearance features such as the morphological and colour with technology of computer image processing and neural network. The morphological and colour features are presented to the neural network for training purposes. The trained network is then used to identify the unknown grain types, impurities and its quality.