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 at the optimum stage for maximum oil production. Current harvesting methods based on observing the number of loose fruits on ground and the color of the fruits using hum...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://eprints.usm.my/61135/1/24%20Pages%20from%2000001785141.pdf http://eprints.usm.my/61135/ |
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| Institution: | Universiti Sains Malaysia |
| Language: | English |
| Summary: | Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is
important to ensure that they are harvested at the optimum stage for maximum oil
production. Current harvesting methods based on observing the number of loose
fruits on ground and the color of the fruits using human vision lead to subjective
evaluation, laborious work, and low quality oil. Therefore, this research focuses on
the development of an automated system with the ability to process the image of oil
palm FFB and determine its ripeness category. The system consists of an image
acquisition system, image processing component and oil palm FFB classification
system. Images of oil palm FFBs of type DxP Yangambi are acquired using an IP
camera which is attached to the end of a pole and connected to a computer via the
RJ45 cable. The images are collected and analyzed using digital image processing
techniques. k-means clustering algorithm is used to segment the image into two
separate regions which are fruit and spike regions. Then, the color features of the fruit
region are extracted from the images and used as inputs to an Artificial Neural
Network (ANN) model learning algorithm. |
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