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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| Format: | Article |
| Language: | English |
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MDPI AG
2012-10-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/12/10/14179 |
| _version_ | 1811184511952617472 |
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| author | Haidi Ibrahim Syed Salim Syed Ali Junita Mohamad-Saleh Zaini Abdul Halim Norasyikin Fadilah |
| author_facet | Haidi Ibrahim Syed Salim Syed Ali Junita Mohamad-Saleh Zaini Abdul Halim Norasyikin Fadilah |
| author_sort | Haidi Ibrahim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T13:14:28Z |
| format | Article |
| id | doaj.art-29dfa53f48c7465cac34f07730847178 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:14:28Z |
| publishDate | 2012-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-29dfa53f48c7465cac34f077308471782022-12-22T04:22:27ZengMDPI AGSensors1424-82202012-10-011210141791419510.3390/s121014179Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit BunchHaidi IbrahimSyed Salim Syed AliJunita Mohamad-SalehZaini Abdul HalimNorasyikin FadilahRipeness 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.http://www.mdpi.com/1424-8220/12/10/14179artificial neural networkprincipal component analysisdigital image processingoil palm fresh fruit bunch |
| spellingShingle | Haidi Ibrahim Syed Salim Syed Ali Junita Mohamad-Saleh Zaini Abdul Halim Norasyikin Fadilah Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch Sensors artificial neural network principal component analysis digital image processing oil palm fresh fruit bunch |
| title | Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch |
| title_full | Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch |
| title_fullStr | Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch |
| title_full_unstemmed | Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch |
| title_short | Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch |
| title_sort | intelligent color vision system for ripeness classification of oil palm fresh fruit bunch |
| topic | artificial neural network principal component analysis digital image processing oil palm fresh fruit bunch |
| url | http://www.mdpi.com/1424-8220/12/10/14179 |
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