Investigation of fusion features for apple classification in smart manufacturing
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. How...
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| Format: | Article |
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MDPI
2019
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| author | Ismail, Ahsiah Idris, Mohd Yamani Idna Ayub, Mohamad Nizam Por, Lip Yee |
| author_facet | Ismail, Ahsiah Idris, Mohd Yamani Idna Ayub, Mohamad Nizam Por, Lip Yee |
| author_sort | Ismail, Ahsiah |
| collection | UM |
| description | 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 CurveletWavelet-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 NDDAWwith 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. © 2019 by the authors. |
| first_indexed | 2024-03-06T06:01:45Z |
| format | Article |
| id | um.eprints-24085 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T06:01:45Z |
| publishDate | 2019 |
| publisher | MDPI |
| record_format | dspace |
| spelling | um.eprints-240852020-03-22T10:40:46Z http://eprints.um.edu.my/24085/ Investigation of fusion features for apple classification in smart manufacturing Ismail, Ahsiah Idris, Mohd Yamani Idna Ayub, Mohamad Nizam Por, Lip Yee QA75 Electronic computers. Computer science 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 CurveletWavelet-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 NDDAWwith 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. © 2019 by the authors. MDPI 2019 Article PeerReviewed Ismail, Ahsiah and Idris, Mohd Yamani Idna and Ayub, Mohamad Nizam and Por, Lip Yee (2019) Investigation of fusion features for apple classification in smart manufacturing. Symmetry, 11 (10). p. 1194. ISSN 2073-8994, https://doi.org/10.3390/sym11101194 |
| spellingShingle | QA75 Electronic computers. Computer science Ismail, Ahsiah Idris, Mohd Yamani Idna Ayub, Mohamad Nizam Por, Lip Yee Investigation of fusion features for apple classification in smart manufacturing |
| title | Investigation of fusion features for apple classification in smart manufacturing |
| title_full | Investigation of fusion features for apple classification in smart manufacturing |
| title_fullStr | Investigation of fusion features for apple classification in smart manufacturing |
| title_full_unstemmed | Investigation of fusion features for apple classification in smart manufacturing |
| title_short | Investigation of fusion features for apple classification in smart manufacturing |
| title_sort | investigation of fusion features for apple classification in smart manufacturing |
| topic | QA75 Electronic computers. Computer science |
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