Automated Tomato Maturity Grading Using ABC-Trained Artificial Neural Networks

Abstract

Tomato maturity grading is quite essential in commercial farms that produce large quantities of tomatoes, and human graders usually perform tomato maturity grading. This task is carried out by matching the surface color of tomatoes to the United States Department of Agriculture’s (USDA) tomato color chart which shows six maturity stages: Green, Breakers, Turning, Pink, Light Red, and Red. However, due to some uncontrollable factors, manual classification involving human graders is prone to misclassification. Thus, this paper introduces an automated tomato classification system that uses Artificial Neural Network (ANN) classifier trained using the Artificial Bee Colony (ABC) algorithm. To effectively classify tomatoes, the researchers combined five color features (Red, Green, Red-Green, Hue, a*) from three color models (RGB, HSI, CIE L*a*b*). These features are the inputs to the ANN classifier. Experiment results show that the ABC-trained ANN classifiers performed well in tomato classification and achieved high accuracy rate. Also, results show that combining the color features from different color models produce better accuracy rate than using color features from a single color model. With these results, an automated tomato classification system using an ABC-trained ANN classifier can be used to replace the manual classification procedure as it minimizes the chances of misclassification.