Investigation of Fusion Features for Apple Classification in Smart Manufacturing

Symmetry

MohdYamani Idna Idris¹  Ahsiah Ismail¹  MohamadNizam Ayub¹  LipYee Por¹ 

[1] Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia;

关键词: smart manufacturing;    vision sensor;    big data;    data analytics;    image recognition;    fusion features;   

DOI  :  10.3390/sym11101194

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown