A study on the oil palm fresh fruit bunch (FFB) ripeness detection by using Hue, Saturation and Intensity (HSI) approach
To increase the quality of palm oil means to accurately grade the oil palm fresh fruit bunches (FFB) for processing. In this paper, HSI color model was used to determine the relationship between FFB ' s color with the underipe and ripe category so that the grading system could be accurately don...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2016
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| Online Access: | http://psasir.upm.edu.my/id/eprint/54942/1/A%20study%20on%20the%20oil%20palm%20fresh%20fruit%20bunch%20%28FFB%29%20ripeness%20detection%20by%20using%20Hue%2C%20Saturation%20and%20Intensity%20%28HSI%29%20approach.pdf http://psasir.upm.edu.my/id/eprint/54942/ |
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| Summary: | To increase the quality of palm oil means to accurately grade the oil palm fresh fruit bunches (FFB) for processing. In this paper, HSI color model was used to determine the relationship between FFB ' s color with the underipe and ripe category so that the grading system could be accurately done. From the analysis manipulation, a formula was generated and applied to the data obtained. The by linear regression in the data shows an average success rate at 45% accuracy for oil palm ripeness detection. Artificial Neural Network (ANN) however return a better accuracy result for both underipe and ripe categories which are 60% and 80% respectively. This yield an overall accuracy of 70%. This can be increased more by improving the grading system. |
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