Seabream Freshness Classification Using Vision Transformers

Author:

Rodrigues João Pedro,Pacheco Osvaldo Rocha,Correia Paulo Lobato

Publisher

Springer Nature Switzerland

Reference33 articles.

1. FAO, The State of World Fisheries and Aquaculture (SOFIA), Rome, Italy (2022). https://doi.org/10.4060/cc0461en

2. United Nations Food and Agricultural Organization (FAO). (2020) Fish, Seafood- Food Supply Quantity (Kg/Capita/Yr) (FAO, 2020). http://www.fao.org/faostat/en/#data/FBS. Accessed 2 May 2023

3. Cultivated fish in Aquaculture: total and main species. Source: PORDATA. https://www.pordata.pt/db/portugal/ambiente+de+consulta/tabela. Accessed 2 May 2023

4. Muhamad, F., Hashim, H., Jarmin, R., Ahmad, A.: Fish freshness classification based on image processing and fuzzy logic. In: Proceedings of the 8th WSEAS International Conference on Circuits, Systems, Electronics, Control, pp. 109–115 (2009)

5. Lalabadi, H.M., Sadeghi, M., Mireei, S.A.: Fish freshness categorization from eyes and gills color features using multi-class artificial neural network and support vector machines. Aquacult. Eng. 90, 102076 (2020)

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