Non-destructive watermelon ripeness determination using image processing and artificial neural network (ANN)
Agriculture products are being more demanding in market today. To increase its productivity, automation to produce these products will be very helpful. The purpose of this work is to measure and determine the ripeness and quality of watermelon. The textures on watermelon skin will be captured using...
| Published in: | World Academy of Science, Engineering and Technology |
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| Main Author: | |
| Format: | Article |
| Language: | English |
| Published: |
2009
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-77649303416&partnerID=40&md5=21d52ce14948265a66d57102e5418fe7 |
| Summary: | Agriculture products are being more demanding in market today. To increase its productivity, automation to produce these products will be very helpful. The purpose of this work is to measure and determine the ripeness and quality of watermelon. The textures on watermelon skin will be captured using digital camera. These images will be filtered using image processing technique. All these information gathered will be trained using ANN to determine the watermelon ripeness accuracy. Initial results showed that the best model has produced percentage accuracy of 86.51%, when measured at 32 hidden units with a balanced percentage rate of training dataset. © 2009 WASET.ORG. |
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| ISSN: | 20103778 |