Convolutional neural network for optimal pineapple harvesting
Upon ripening, colour of pineapple’s peel gradually changes from green to yellowish, which spreading from bottom to the top. The objective of this project is to develop a computational intelligence method for pineapple maturity indices classification for optimal harvasting. Pineapple maturity indice...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Penerbit UTM Press
2017
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| Online Access: | http://eprints.utm.my/80309/1/FatimahShamIsmail2017_ConvolutionalNeuralNetworkforOptimalPineapple.pdf |
| _version_ | 1796863163921072128 |
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| author | Azman, Ahmad Aizuddin Ismail, Fatimah Sham |
| author_facet | Azman, Ahmad Aizuddin Ismail, Fatimah Sham |
| author_sort | Azman, Ahmad Aizuddin |
| collection | ePrints |
| description | Upon ripening, colour of pineapple’s peel gradually changes from green to yellowish, which spreading from bottom to the top. The objective of this project is to develop a computational intelligence method for pineapple maturity indices classification for optimal harvasting. Pineapple maturity indices can be grouped into three levels, which are unripe, partially ripe and fully ripe for determining optimal pineapple harvesting. Previous works on classifying fruit’s ripeness rely on manual hand-engineered feature extraction and selection. This project proposes new intelligent method using convolutional neural network (CNN) that has the ability to learn several unique features from the given task automatically through supervised learning. The simulation results show that the method achieved 100% classification’s accuracy for determining unripe and fully ripe level and 82% accuracy for partially ripe level. |
| first_indexed | 2024-03-05T20:22:38Z |
| format | Article |
| id | utm.eprints-80309 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:22:38Z |
| publishDate | 2017 |
| publisher | Penerbit UTM Press |
| record_format | dspace |
| spelling | utm.eprints-803092019-04-25T01:31:56Z http://eprints.utm.my/80309/ Convolutional neural network for optimal pineapple harvesting Azman, Ahmad Aizuddin Ismail, Fatimah Sham TK Electrical engineering. Electronics Nuclear engineering Upon ripening, colour of pineapple’s peel gradually changes from green to yellowish, which spreading from bottom to the top. The objective of this project is to develop a computational intelligence method for pineapple maturity indices classification for optimal harvasting. Pineapple maturity indices can be grouped into three levels, which are unripe, partially ripe and fully ripe for determining optimal pineapple harvesting. Previous works on classifying fruit’s ripeness rely on manual hand-engineered feature extraction and selection. This project proposes new intelligent method using convolutional neural network (CNN) that has the ability to learn several unique features from the given task automatically through supervised learning. The simulation results show that the method achieved 100% classification’s accuracy for determining unripe and fully ripe level and 82% accuracy for partially ripe level. Penerbit UTM Press 2017 Article PeerReviewed application/pdf en http://eprints.utm.my/80309/1/FatimahShamIsmail2017_ConvolutionalNeuralNetworkforOptimalPineapple.pdf Azman, Ahmad Aizuddin and Ismail, Fatimah Sham (2017) Convolutional neural network for optimal pineapple harvesting. Elektrika, 16 (2). pp. 1-4. ISSN 0128-4428 https://elektrika.utm.my/index.php/ELEKTRIKA_Journal/article/view/54 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Azman, Ahmad Aizuddin Ismail, Fatimah Sham Convolutional neural network for optimal pineapple harvesting |
| title | Convolutional neural network for optimal pineapple harvesting |
| title_full | Convolutional neural network for optimal pineapple harvesting |
| title_fullStr | Convolutional neural network for optimal pineapple harvesting |
| title_full_unstemmed | Convolutional neural network for optimal pineapple harvesting |
| title_short | Convolutional neural network for optimal pineapple harvesting |
| title_sort | convolutional neural network for optimal pineapple harvesting |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/80309/1/FatimahShamIsmail2017_ConvolutionalNeuralNetworkforOptimalPineapple.pdf |
| work_keys_str_mv | AT azmanahmadaizuddin convolutionalneuralnetworkforoptimalpineappleharvesting AT ismailfatimahsham convolutionalneuralnetworkforoptimalpineappleharvesting |