AN INTELLIGENT COMPUTER VISION SYSTEM FOR VEGETABLES AND FRUITS QUALITY INSPECTION USING SOFT COMPUTING TECHNIQUES

Abstract

The quality of food products is very important for the human health. The large population and the increased requirements of food products makes it difficult to arrive the desired quality. The quality inspection and sorting tons of fruits and vegetables manually is a slow, costly, and an inaccurate process. In this research a vision-based quality inspection and sorting system is developed, to increase the quality of food products. The quality inspection and sorting process depends on capturing the image of the fruits/vegetables, analyzing captured image to discard defected products in order to identify the good or bad. Four different systems for different food products have been developed namely, Orange, Lemon, Sweet Lime, and Tomato. A dataset of 1200 images is used to train and test the vision systems (300 images for each). The obtained accuracy ranges from 85.00% to 95.00% for Orange, Lemon, Sweet Lime and Tomato used soft-computing techniques such as Back propagation neural network and Probabilistic neural network.