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Convolutional neural network for optimal pineapple harvesting
Ahmad Aizuddin Azman1, Fatimah Sham Ismail2.
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.
Affiliation:
- Universiti Teknologi Malaysia, Malaysia
- Universiti Teknologi Malaysia, Malaysia
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