Intelligent System to Evaluate the Quality of Orange, Lemon, Sweet Lime and Tomato Using Back-Propagation Neural-Network (BPNN) and Probabilistic Neural Network (PNN)

Abstract

The quality assessment and sorting millions of fruits as well as vegetables by manual is usually slower. But also costly and cannot give an accurate result. In this research, to increase the quality of food above products were developed by using a vision-based quality inspection and sorting system. The quality assessment and sorting process analyzes taken image for its quality (good). It discards the defected one (bad). The image can be of vegetables or fruits. Four different systems for different food products (Orange, Lemon, Sweet Lime, and Tomato) have been developed. We have used a dataset of one thousand two hundred images which can be used to train as well as test the image systems. All images of 300 in the count. The obtained overall accuracy ranges between 85.0% to 95.00% for Orange, Lemon, Sweet Lime, and Tomato by using soft-computing techniques such as Backpropagation neural network and Probabilistic neural network.