Faculty of Computing and Information Technology in Rabigh | Researches | Natural Produce Classification Using Computer Vision Based on Statistical Color Features and Derivative of Radius Function

Faculty of Computing and Information Technology in Rabigh

Document Details

Document Type	:	Article In Journal
Document Title	:	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
Subject	:	Computer Science
Document Language	:	English
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.
ISSN	:	1662-7482
Journal Name	:	Applied Mechanics and Materials
Volume	:	771
Issue Number	:	2015
Publishing Year	:	1436 AH 2015 AD
Article Type	:	Article
Added Date	:	Monday, March 7, 2016

Researchers

Researcher Name (Arabic)	Researcher Name (English)	Researcher Type	Dr Grade	Email
Anton Satria Prabuwono	Satria Prabuwono, Anton	Researcher	Doctorate	antonsatria@eu4m.eu

Files

File Name	Type	Description
38351.pdf	pdf

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