Natural Produce Classification Using Computer Vision Based on Statistical Color Features and Derivative of Radius Function

Prabuwono, Anton Satria and Siswantoro, Joko and Abdullah, Azizi (2015) Natural Produce Classification Using Computer Vision Based on Statistical Color Features and Derivative of Radius Function. Applied Mechanics and Materials, 771. pp. 242-247. ISSN 1662-7482

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Official URL: https://www.scientific.net/AMM.771.242

Abstract

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.

Item Type: Article
Uncontrolled Keywords: Natural produce classification, Computer vision, Statistical color features, Derivative of radius function, k-nearest neighbors, Artificial neural network
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Engineering > Department of Informatic
Depositing User: Joko Siswantoro
Date Deposited: 07 Mar 2017 02:05
Last Modified: 07 Mar 2017 03:48
URI: http://repository.ubaya.ac.id/id/eprint/29042

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