On-line separation and sorting of chicken portions using a robust vision-based intelligent modelling approach. (March 2018)

• Record Type: Journal Article
• Authors: Teimouri, Nima; Omid, Mahmoud; Mollazade, Kaveh; Mousazadeh, Hossein; Alimardani, Reza; Karstoft, Henrik
• Is Part Of: Biosystems engineering. Volume 167(2018)
• Page Start: 8
• Page End: 20
• Publication Date: 2018-03
• Subjects: Computer vision -- Food processing -- PLSR -- ANN -- LDA; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2017.12.009
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : One of the major issues in food industry is automatic sorting of chicken portions. In the present study, we propose a new on-line method based on combined machine vision techniques and linear and nonlinear classifiers to categorise chicken portions automatically. Mechanical framework, conveyor belt, electrical and control units, lighting box, charge-coupled device (CCD) camera, separating unit, and air compressor are included in the study proposed system. Major classes of chicken portions can be categorised as breast, leg, fillet, wing, and drumstick. Imaging procedure in the study is carried out using CCD camera and a computer system. Geometrical aspects, colour, and textural features are extracted in the next step using the study dataset, so the best ones could be selected accordingly through Chi-Square methodology. Partial least squares regression (PLSR), linear discriminant analysis (LDA) and artificial neural network (ANN) respectively are employed to classify the data. Considering total accuracy level of PLSR, LDA and ANN obtained in the study, results indicated better performance level of ANN compared to linear models. The machine vision algorithm developed here together with the ANN classifier were evaluated on a sorting machine to separate test samples using separating units in the on-line mode. The processing time of proposed method is estimated as 15 ms for each image. The overall accuracy in maximum speed of conveyor, 0.2 m s −1, was obtained 93 percent that is appropriate in real-time applications. The total rate of processing and sorting chicken portions was also measured as approximately 2800 samples per hour. Graphical abstract: Highlights: A sorting machine designed and fabricated for classifying chicken portions. Proposed a fast and robust image processing methodology for automated separating. ANN as a nonlinear method showed better performance compared with PLSR and LDA. In total 93 percent chicken portions were sorted correctly.