TY - GEN
T1 - Fruit classification by HPA-SLFN
AU - Lu, Siyuan
AU - Lu, Zhihai
AU - Phillips, Preetha
AU - Wang, Shuihua
AU - Wu, Jianguo
AU - Zhang, Yudong
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/21
Y1 - 2016/11/21
N2 - (Objective) Fruit classification remains a challenge because of the similarities involved by a large quantities of types of fruits. With the aim of recognizing fruits accurately and efficiently, this paper offered a novel fruit-classification tool. (Method) The proposed methodology consisted of following four processes: (i) A four-step preprocessing was performed. (ii) The color, shape, texture features were combined. (iii) Principal component analysis was employed for feature reduction. (iv) We presented a novel classification method with the combination of 'Hybridization of PSO and ABC (HPA)' and 'single-hidden layer feedforward neural-network (SLFN)', which was termed as HPA-SLFN. (Results) The experiment results demonstrated that the proposed HPA-SLFN achieved an 89.5% accuracy that was superior to existing methods. (Conclusion) The proposed HPA-SLFN was effective.
AB - (Objective) Fruit classification remains a challenge because of the similarities involved by a large quantities of types of fruits. With the aim of recognizing fruits accurately and efficiently, this paper offered a novel fruit-classification tool. (Method) The proposed methodology consisted of following four processes: (i) A four-step preprocessing was performed. (ii) The color, shape, texture features were combined. (iii) Principal component analysis was employed for feature reduction. (iv) We presented a novel classification method with the combination of 'Hybridization of PSO and ABC (HPA)' and 'single-hidden layer feedforward neural-network (SLFN)', which was termed as HPA-SLFN. (Results) The experiment results demonstrated that the proposed HPA-SLFN achieved an 89.5% accuracy that was superior to existing methods. (Conclusion) The proposed HPA-SLFN was effective.
KW - Fruit classification
KW - artificial bee colony
KW - machine learning
KW - particle swarm optimization
KW - principal component analysis
KW - single-hidden layer feedforward neural-network
UR - http://www.scopus.com/inward/record.url?scp=85006754123&partnerID=8YFLogxK
U2 - 10.1109/WCSP.2016.7752639
DO - 10.1109/WCSP.2016.7752639
M3 - Conference Proceeding
AN - SCOPUS:85006754123
T3 - 2016 8th International Conference on Wireless Communications and Signal Processing, WCSP 2016
BT - 2016 8th International Conference on Wireless Communications and Signal Processing, WCSP 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Wireless Communications and Signal Processing, WCSP 2016
Y2 - 13 October 2016 through 15 October 2016
ER -