Document Details
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Document Type |
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Article In Journal |
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Document Title |
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Natural Produce Classification Using Computer Vision Based on Statistical Color Features and Derivative of Radius Function Natural Produce Classification Using Computer Vision Based on Statistical Color Features and Derivative of Radius Function |
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Subject |
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Computer Science |
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Document Language |
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English |
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Abstract |
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In agriculture industry, natural produce classification is used in sorting, grading, measuring, and pricing. Currently, a lot of methods have been developed using computer vision to replace human expert in natural produce classification. However, some of the method used long features descriptor and complex classifier to obtain high classification rate. This paper proposes natural produce classification method using computer vision based on simple statistical color features and derivative of radius function. The k-nearest neighbors (k-NN) and artificial neural network (ANN) were used to classify the produce based on the extracted features. Preliminary experiment results show that the proposed method achieved best result with average classification accuracy of 99.875% using ANN classifier with nine nodes in hidden layer. |
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ISSN |
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1662-7482 |
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Journal Name |
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Applied Mechanics and Materials |
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Volume |
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771 |
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Issue Number |
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2015 |
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Publishing Year |
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1436 AH
2015 AD |
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Article Type |
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Article |
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Added Date |
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Monday, March 7, 2016 |
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Researchers
| Anton Satria Prabuwono | Satria Prabuwono, Anton | Researcher | Doctorate | antonsatria@eu4m.eu |
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