Abstract

This study aims to investigate the food quality detection using an intelligent method. The machine vision applies image processing softwares to monitor the food quality. The Artificial Neural Network (ANN) in the image processing softwares is crucial for food quality detection precision. However, improper structure parameters of ANN may lead to the low detection performance. In order to overcome this problem, a new detection method based on Genetic Algorithm (GA) -Chaos optimized Radial Basis Function (RBF) neural network is proposed in this study. The GA-Chaos was used to optimize the structure of the RBF as well as its weight values to obtain high generalization ability of the RBF-detection model. Then the RBF model was employed to train and test the food data sets. Experimental results show that the method could enhance the food quality detection rate and outperforms the traditional GA-based methods.

Keywords:

Food quality detection, image processing, machine vision,

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Competing interests

The authors have no competing interests.

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Copyright

The authors have no competing interests.