Convolutional Neural Network for Optimal Pineapple Harvesting
AbstractUpon ripening, colour of pineappleâ€™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 fruitâ€™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% classificationâ€™s accuracy for determining unripe and fully ripe level and 82% accuracy for partially ripe level.
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, â€œBackpropagation applied to handwritten zip code recognition,â€ Neural Computation, vol. 1, no. 4, pp. 541â€“551, 1989.
S. Ji, W. Xu, M. Yang, and K. Yu, â€œ3D convolutional neural networks for human action recognition,â€ IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 221â€“231, 2013.
A. Krizhevsky, I. Sutskever, and G. Hinton, â€œImageNet classification with deep convolutional neural networks,â€ Advances in Neural Information Processing Systems, vol. 25, pp. 1106â€“1114, 2012,
P. Sermanet and K. Kavukcuoglu, â€œPedestrian detection with unsupervised multi-stage feature learning,â€ International Conference on Computer Vision and Pattern Recognition, in press, 2013.
D. Ciresan, U. Meier, J. Masci, and J. Schmidhuber, â€œA committee of neural networks for traffic sign classification,â€ International Joint Conference on Neural Networks, 2011, pp. 1918â€“1921.
M. Osadchy, Y. Cun, and M. Miller, â€œSynergistic face detection and pose estimation with energy-based models,â€ The Journal of Machine Learning Research, vol. 8, pp. 1197â€“1215, 2007.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, â€œGradient based learning applied to document recognition,â€ Proceedings of the IEEE, 1998, vol. 86, no. 11, pp. 2278â€“2324.
FAMA, â€œMenuju kearah Kualiti Malaysiaâ€™s Best Siri Panduan Kualiti Nenasâ€, 2006.
B. H Abu Bakar, A. J Ishak, R. Shamsuddin, W. Z. Wan Hassan,"Ripeness Level Classification for Pineapple Using RGB and HSI Colour Maps". Journal of Theoretical and Apllied Information Technology, 2013, 57, pp. 1817-3195.
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