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Abstract
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Article Information:
Food Quality Detection Using Machine Vision Based on Genetic Optimized RBF Network
Wei Ding
Corresponding Author: Wei Ding
Submitted: March 22, 2014
Accepted: April 28, 2014
Published: August 10, 2014 |
Abstract:
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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.
Key words: Food quality detection, image processing, machine vision, , , ,
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Cite this Reference:
Wei Ding, . Food Quality Detection Using Machine Vision Based on Genetic Optimized RBF Network. Advance Journal of Food Science and Technology, (8): 981-983.
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ISSN (Online): 2042-4876
ISSN (Print): 2042-4868 |
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