INTELLIGENT GRADING SYSTEM FOR BANANA FRUIT USING NEURAL NETWORK ARBITRATION

There is a need for quality production at a very fast rate in food processing industry. Therefore, developing a system that can perform the visual perception of the human operator in making decisions at a very fast rate will be of great advantage. Such machine vision system will reduce human errors such as individual perception differences in determining whether a product is healthy or defective for production. In this research, an intelligent identification system for grading banana fruit has been developed to replace or aid the human operator who may suffer from inconsistent slow decision-making. This work is divided into three phases. The first phase is the acquisition of the images and preparation of the database required for our experiments. In the second phase, several image processing techniques are employed to extract banana features for use in the last phase; which is the classification phase. Here, a neural network classifier is arbitrated using extracted banana image features in order to classify and grade the fruit. The sufficient classification rates obtained in this work, and the minimal time costs required when compared with previous works indicate that our novel banana grading system can be efficiently used in real life applications in the food processing industry.