Vision-Based Apple Classification for Smart Manufacturing

Ismail, Ahsiah and Idris, Mohd Yamani Idna and Ayub, Mohamad Nizam and Por, Lip Yee (2018) Vision-Based Apple Classification for Smart Manufacturing. Sensors, 18 (12). p. 4353. ISSN 1424-8220

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Official URL: https://doi.org/10.3390/s18124353

Abstract

Smart manufacturing enables an efficient manufacturing process by optimizing production and product transaction. The optimization is performed through data analytics that requires reliable and informative data as input. Therefore, in this paper, an accurate data capture approach based on a vision sensor is proposed. Three image recognition methods are studied to determine the best vision-based classification technique, namely Bag of Words (BOW), Spatial Pyramid Matching (SPM) and Convolutional Neural Network (CNN). The vision-based classifiers categorize the apple as defective and non-defective that can be used for automatic inspection, sorting and further analytics. A total of 550 apple images are collected to test the classifiers. The images consist of 275 non-defective and 275 defective apples. The defective category includes various types of defect and severity. The vision-based classifiers are trained and evaluated according to the K-fold cross-validation. The performances of the classifiers from 2-fold, 3-fold, 4-fold, 5-fold and 10-fold are compared. From the evaluation, SPM with SVM classifier attained 98.15% classification accuracy for 10-fold and outperformed the others. In terms of computational time, CNN with SVM classifier is the fastest. However, minimal time difference is observed between the computational time of CNN and SPM, which were separated by only 0.05 s.

Item Type: Article
Uncontrolled Keywords: vision sensor; smart manufacturing; image recognition; Bag of Words; Spatial Pyramid Matching; Convolutional Neural Network
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Dept of Computer System & Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 05 Aug 2019 07:37
Last Modified: 05 Aug 2019 07:37
URI: http://eprints.um.edu.my/id/eprint/21787

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