Development of Computer Vision System for Fruits

Abstract

Automated defect detection of fruits using computer vision and machine learning concepts has ‎become a significant area of research. In ‎this work, working prototype hardware model of conveyor with PC is designed, constructed and implemented to analyze the fruit quality. The prototype consists of low-cost microcontrollers, USB camera and MATLAB user interface. The automated classification model rejects or accepts the fruit based on the quality i.e., good (ripe, unripe) and bad. For the classification of fruit quality, machine learning algorithms such as Support Vector Machine, KNN, Random Forest classifier, Decision Tree classifier and ANN are used. The dataset used in this work consists of the following fruit varieties i.e., apple, orange, tomato, guava, lemon, and pomegranate. We trained, tested and ‎compared the performance of these five machine learning approaches and found out that the ANN based fruit detection performs better. The overall accuracy obtained by the ANN model for the dataset is 95.6%. In addition, the response time of the system is 50 seconds per fruit which is very low. Therefore, it will be very suitable and useful for small-scale industries and farmers to grow up their business.