Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time
This work reports the development of automated systems based on computer vision to improve the quality control and sorting of dried figs of Cosenza (protected denomination of origin) focusing on two research issues. The first was based on qualitative discrimination of figs through colour assessment...
| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.11939/6957 https://www.sciencedirect.com/science/article/abs/pii/S0168169915003397 |
| _version_ | 1855032520978989056 |
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| author | Benalia, Souraya Cubero, Sergio Prats-Montalbán, José M. Bernardi, Bruno Zimbalatti, Giuseppe Blasco, José |
| author_browse | Benalia, Souraya Bernardi, Bruno Blasco, José Cubero, Sergio Prats-Montalbán, José M. Zimbalatti, Giuseppe |
| author_facet | Benalia, Souraya Cubero, Sergio Prats-Montalbán, José M. Bernardi, Bruno Zimbalatti, Giuseppe Blasco, José |
| author_sort | Benalia, Souraya |
| collection | ReDivia |
| description | This work reports the development of automated systems based on computer vision to improve the quality control and sorting of dried figs of Cosenza (protected denomination of origin) focusing on two research issues. The first was based on qualitative discrimination of figs through colour assessment comparing the analysis of colour images obtained using a digital camera with those obtained according to conventional instrumental methods, i.e. colourimetry currently done in laboratories. Data were expressed in terms of CIE XYZ, CIELAB and HunterLab colour spaces, as well as the browning index measurement of each fruit, and then, analysed using PCA and PLS-DA based methods. The results showed that both chroma meter and image analysis allowed a complete distinction between high quality and deteriorated figs, according to colour attributes. The second research issue had the purpose of developing image processing algorithms to achieve real-time sorting of figs using an experimental prototype based on machine vision, simulating an industrial application. An extremely high 99.5% of deteriorated figs were classified correctly as well as 89.0% of light coloured good quality figs A lower percentage was obtained for dark good quality figs but results were acceptable since the most of the confusion was among the two classes of good product. |
| format | article |
| id | ReDivia6957 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | ReDivia69572025-04-25T14:47:59Z Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time Benalia, Souraya Cubero, Sergio Prats-Montalbán, José M. Bernardi, Bruno Zimbalatti, Giuseppe Blasco, José Computer vision Postharvest processing N01 Agricultural engineering Q01 Food science and technology Image analysis Quality Colour This work reports the development of automated systems based on computer vision to improve the quality control and sorting of dried figs of Cosenza (protected denomination of origin) focusing on two research issues. The first was based on qualitative discrimination of figs through colour assessment comparing the analysis of colour images obtained using a digital camera with those obtained according to conventional instrumental methods, i.e. colourimetry currently done in laboratories. Data were expressed in terms of CIE XYZ, CIELAB and HunterLab colour spaces, as well as the browning index measurement of each fruit, and then, analysed using PCA and PLS-DA based methods. The results showed that both chroma meter and image analysis allowed a complete distinction between high quality and deteriorated figs, according to colour attributes. The second research issue had the purpose of developing image processing algorithms to achieve real-time sorting of figs using an experimental prototype based on machine vision, simulating an industrial application. An extremely high 99.5% of deteriorated figs were classified correctly as well as 89.0% of light coloured good quality figs A lower percentage was obtained for dark good quality figs but results were acceptable since the most of the confusion was among the two classes of good product. 2021-01-12T08:10:50Z 2021-01-12T08:10:50Z 2016 article acceptedVersion Benalia, S., Cubero, S., Prats-Montalbán, J. M., Bernardi, B., Zimbalatti, G., & Blasco, J. (2016). Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time. Computers and Electronics in Agriculture, 120, 17-25. 0168-1699 http://hdl.handle.net/20.500.11939/6957 10.1016/j.compag.2015.11.002 https://www.sciencedirect.com/science/article/abs/pii/S0168169915003397 en Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ openAccess Elsevier electronico |
| spellingShingle | Computer vision Postharvest processing N01 Agricultural engineering Q01 Food science and technology Image analysis Quality Colour Benalia, Souraya Cubero, Sergio Prats-Montalbán, José M. Bernardi, Bruno Zimbalatti, Giuseppe Blasco, José Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time |
| title | Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time |
| title_full | Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time |
| title_fullStr | Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time |
| title_full_unstemmed | Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time |
| title_short | Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time |
| title_sort | computer vision for automatic quality inspection of dried figs ficus carica l in real time |
| topic | Computer vision Postharvest processing N01 Agricultural engineering Q01 Food science and technology Image analysis Quality Colour |
| url | http://hdl.handle.net/20.500.11939/6957 https://www.sciencedirect.com/science/article/abs/pii/S0168169915003397 |
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