Intelligent color vision system for ripeness classification of oil palm fresh fruit bunch

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 pa...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2012-10, Vol.12 (10), p.14179-14195
Main Authors: Fadilah, Norasyikin, Mohamad-Saleh, Junita, Abdul Halim, Zaini, Ibrahim, Haidi, Syed Ali, Syed Salim
Format: Article
Language:English
Subjects:
Agriculture - instrumentation
Arecaceae
artificial neural network
Automation
Classification
Classifiers
Color
digital image processing
Food Handling - instrumentation
Food Industry - instrumentation
Food products
Fruit - classification
Fruits
Image Processing, Computer-Assisted - methods
Learning theory
Neural networks
Neural Networks (Computer)
oil palm fresh fruit bunch
Palm
Palm Oil
Pattern Recognition, Automated - methods
Plant Oils
Principal Component Analysis
Principal components analysis
Sensors
Training
Vegetable oils
Vision systems
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s121014179