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.
Keywords: artificial neural network; principal component analysis; digital image processing; oil palm fresh fruit bunch
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Fadilah, N.; Mohamad-Saleh, J.; Abdul Halim, Z.; Ibrahim, H.; Syed Ali, S.S. Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch. Sensors 2012, 12, 14179-14195.
Fadilah N, Mohamad-Saleh J, Abdul Halim Z, Ibrahim H, Syed Ali SS. Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch. Sensors. 2012; 12(10):14179-14195.
Fadilah, Norasyikin; Mohamad-Saleh, Junita; Abdul Halim, Zaini; Ibrahim, Haidi; Syed Ali, Syed S. 2012. "Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch." Sensors 12, no. 10: 14179-14195.