Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch

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ID: 110285
2012
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Abstract
Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category.
Reference Key
fadilah2012sensorsintelligent Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Norasyikin Fadilah;Junita Mohamad-Saleh;Zaini Abdul Halim;Haidi Ibrahim;Syed Salim Syed Ali;Fadilah, Norasyikin;Mohamad-Saleh, Junita;Abdul Halim, Zaini;Ibrahim, Haidi;Syed Ali, Syed Salim;
Journal sensors
Year 2012
DOI
10.3390/s121014179
URL
https://www.mdpi.com/1424-8220/12/10/14179/htm##authors
Keywords
artificial neural network principal component analysis digital image processing oil palm fresh fruit bunch neural networks image processing National Center for Biotechnology Information NCBI NLM MEDLINE pubmed abstract nih national institutes of health national library of medicine computer* research support non-u.s. gov't pattern recognition automated / methods* principal component analysis computer-assisted / methods pmid:23202043 pmc3545614 doi:10.3390/s121014179 norasyikin fadilah junita mohamad-saleh syed salim syed ali agriculture / instrumentation arecaceae* color food handling / instrumentation food industry / instrumentation fruit / classification* palm oil plant oils

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