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Investigation of Fusion Features for Apple Classification in Smart Manufacturing

Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
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Symmetry 2019, 11(10), 1194; https://doi.org/10.3390/sym11101194
Received: 19 August 2019 / Revised: 12 September 2019 / Accepted: 16 September 2019 / Published: 24 September 2019
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data 2019)
Smart manufacturing optimizes productivity with the integration of computer control and various high level adaptability technologies including the big data evolution. The evolution of big data offers optimization through data analytics as a predictive solution in future planning decision making. However, this requires accurate and reliable informative data as input for analytics. Therefore, in this paper, the fusion features for apple classification is investigated to classify between defective and non-defective apple for automatic inspection, sorting and further predictive analytics. The fusion features with Decision Tree classifier called Curvelet Wavelet-Gray Level Co-occurrence Matrix (CW-GLCM) is designed based on symmetrical pattern. The CW-GLCM is tested on two apple datasets namely NDDA and NDDAW with a total of 1110 apple images. Each dataset consists of a binary class of apple which are defective and non-defective. The NDDAW consists more low-quality region images. Experimental results show that CW-GLCM successfully classify 98.15% of NDDA dataset and 89.11% of NDDAW dataset. A lower classification accuracy is observed in other five existing image recognition methods especially on NDDAW dataset. Finally, the results show that CW-GLCM is more accurate among all the methods with the difference of more than 10.54% of classification accuracy. View Full-Text
Keywords: smart manufacturing; vision sensor; big data; data analytics; image recognition; fusion features smart manufacturing; vision sensor; big data; data analytics; image recognition; fusion features
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Ismail, A.; Idris, M.Y.I.; Ayub, M.N.; Por, L.Y. Investigation of Fusion Features for Apple Classification in Smart Manufacturing. Symmetry 2019, 11, 1194.

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