Development of Computer Vision System for Fruits
Y. Dileep Sean *
Dr.NTR College of Agricultural Engineering, Acharya NG Ranga Agricultural University, Bapatla – 522 101, India.
D.D. Smith
College of Food Science and Technology, Acharya NG Ranga Agricultural University, Pulivendula – 516390, India.
V.S.P. Bitra
PHETC, Bapatla, Acharya NG Ranga Agricultural University, Bapatla – 522 101, India.
Vimala Bera
Dr.NTR College of Food Science and Technology, Acharya NG Ranga Agricultural University, Bapatla – 522 101, India.
Sk. Nafeez Umar
Agriculture College, Acharya NG Ranga Agricultural University, Tirupati – 517 502, India.
*Author to whom correspondence should be addressed.
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.
Keywords: Computer vision, Image processing, ANN, KNN, SVM classifier, fruits inspection