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Oil Palm Fruit Ripeness Grading System Based on Basic Gray Level Aura Matrix Feature Extraction and Support Vector Machine, K Nearest Neighbourhood and Artificial Neural Network Supervised Machine Learning

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There is a processing need for a fast, easy and accurate classification system for oil palm fruit ripeness. Such a system will be invaluable to farmers and plantation managers who need to sell their oil palm fresh fruit bunch (FFB) for the mill as this will avoid disputes. In this paper the authors investigated and present the way of grading the oil palm FFB automatically by using the digital image processing techniques based on the Basic Gray Level Aura Matrix (BGLAM) texture feature extraction and support vector machine (SVM), K nearest neighbourhood (KNN) and artificial Neural Network (ANN) supervised machine learning. The received operating carve (ROC) and area under curve (AUC) show that the result of BGLAM with SVM as 92% accurate significantly higher than the other two classifiers techniques which both of them have an accuracy of 82%.
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Document Type: Research Article

Publication date: September 1, 2013

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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