Sistematik Derlemeler ve Meta Analiz
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Reduction of Losses and Wastage in Seafoods: The Role of Smart Tools and Biosensors Based on Artificial Intelligence

Yıl 2024, Cilt: 8 Sayı: 1, 14 - 44
https://doi.org/10.61969/jai.1394542

Öz

This paper reviews current knowledge on the role of smart tools and biosensors based on artificial intelligence in reducing seafood loss and wastage. This study shows that a variety of biosensors, categorised according to how they function, can be used to measure the quality of seafood. These include optical biosensors, enzyme-based biosensors, immunosensors, microbial biosensors, DNA-based biosensors, electrochemical biosensors, optical biosensors, tissue-based biosensors, and piezoelectric biosensors. Among these biosensors, optical biosensors, electrochemical biosensors, and mechanical biosensors are the most significant. Again, this study report that, for seafood traceability and management, a variety of smart solutions including blockchain technology, quick response (QR) codes, data analytics, digital twins, and radio frequency identification (RFID) tags can be utilised. Catch data, vessel tracking data, and data from the processing plant are some of the different data sources that can be utilised to trace seafood products. Artificial intelligence tools like neural networks, deep learning, machine learning, and others can be used to forecast and improve seafood quality. It is crucial to study the development of biosensors that can properly identify the earliest signs of seafood contamination or rotting.

Kaynakça

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Reduction of Losses and Wastage in Seafoods: The Role of Smart Tools and Biosensors Based on Artificial Intelligence

Yıl 2024, Cilt: 8 Sayı: 1, 14 - 44
https://doi.org/10.61969/jai.1394542

Öz

This paper reviews current knowledge on the role of smart tools and biosensors based on artificial intelligence in reducing seafood loss and wastage. This study shows that a variety of biosensors, categorised according to how they function, can be used to measure the quality of seafood. These include optical biosensors, enzyme-based biosensors, immunosensors, microbial biosensors, DNA-based biosensors, electrochemical biosensors, optical biosensors, tissue-based biosensors, and piezoelectric biosensors. Among these biosensors, optical biosensors, electrochemical biosensors, and mechanical biosensors are the most significant. Again, this study report that, for seafood traceability and management, a variety of smart solutions including blockchain technology, quick response (QR) codes, data analytics, digital twins, and radio frequency identification (RFID) tags can be utilised. Catch data, vessel tracking data, and data from the processing plant are some of the different data sources that can be utilised to trace seafood products. Artificial intelligence tools like neural networks, deep learning, machine learning, and others can be used to forecast and improve seafood quality. It is crucial to study the development of biosensors that can properly identify the earliest signs of seafood contamination or rotting.

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  • Vazquez-Briseno, M., Hirata, F.I., Sanchez-Lopez, J.D., Jimenez-Garcia, E., Navarro-Cota, C., and Nieto-Hipolito, J.I. (2012). Using RFID/NFC and QR-Code in Mobile Phones to Link the Physical and the Digital World, IntechOpen, 2012. Doi: https://doi.org/10.5772/37447.
  • Verdouw, C., Tekinerdogan, B., Beulens, A., & Wolfert, S. (2021). Digital twins in smart farming. Agricultural Systems, 189, 103046.
  • Vo S.A., Scanlan J. and Turner P. (2020). An application of Convolutional Neural Network to lobster grading in the Southern Rock Lobster supply chain. Food Control, Doi: https://doi.org/10.1016/j.foodcont.2020.107184.
  • Wang, F., Zang, Y., Wo, Q., Zou, C., Wang, N., Wang, X., & Li, D. (2013, March). Fish freshness rapid detection based on fish-eye image. In PIAGENG 2013: Image Processing and Photonics for Agricultural Engineering (Vol. 8761, pp. 52-56). SPIE.
  • Wang, J., Yue, H., Zenan Zhou, Z. (2017). An improved traceability system for food quality assurance and evaluation based on fuzzy classification and neural network, Food Control, 79: 363-370. Doi: https://doi.org/10.1016/j.foodcont.2017.04.013.
  • Wang, X., Li, F., Cai, Z., Liu, K., Li, J., Zhang, B., and He, J. (2018). Sensitive colorimetric assay for uric acid and glucose detection based on multilayer-modified paper with smartphone as signal readout, Anal. Bioanal. Chem. 410: 2647–2655
  • Wang, X., Luo, Y., Huang, K., and Cheng, N (2022). Biosensor for agriculture and food safety: Recent advances and future perspectives. Advanced Agrochem, 1(1): 3-6. Doi: https://doi.org/10.1016/j.aac.2022.08.002.
  • Waziry, S., Wardak, A.B., Rasheed, J., Shubair, R.M., Rajab, K., and Shaikh, A. (2023). Performance comparison of machine learning driven approaches for classification of complex noises in quick response code images. Heliyon, 9(4): e15108. Doi: https://doi.org/10.1016/j.heliyon.2023.e15108.
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  • Xiang, Y., Sheng, J., Wang, L., Cai, Y., Meng, Y., & Cai, W. (2022). Research progresses on equipment technologies used in safety inspection, repair, and reinforcement for deepwater dams. Science China Technological Sciences, 65(5), 1059-1071.
  • Yan, B., Hu, D., & Shi, P. (2012). A traceable platform of aquatic foods supply chain based on RFID and EPC Internet of Things. International Journal of RF Technologies, 4(1), 55-70.
  • Yang, L., Liu, Y., Yu, H., Fang, X., Song, L., Li, D. and Chen, Y. (2021). Computer vision models in intelligent aquaculture with emphasis on fish detection and behavior analysis: A review. Archives of Computational Methods in Engineering, 28, pp.2785-2816.
  • Yenket, R., Chambers IV, E. and Johnson, D.E. (2011) Statistical package clustering may not be best for grouping consumers to understand their most liked products. Journal of Sensory Studies 26, 209–225.
  • Zhang, Y., Wang, W., Yan, L., Glamuzina, B., & Zhang, X. (2019). Development and evaluation of an intelligent traceability system for waterless live fish transportation. Food control, 95, 283-297.
  • Zhang, Z., Wang, S., Diao, Y., Zhang, J., Decheng, L.V. (2010). Fatty acid extracts from Lucilia sericata larvae promote murine cutaneous wound healing by angiogenic activity. Lipids Health Dis, 9:1–9.
  • Zhou, C., Xu, D., Lin, K., Sun, C. and Yang, X. (2018). Intelligent feeding control methods in aquaculture with an emphasis on fish: a review. Reviews in Aquaculture, 10(4), pp.975- 993.
  • Zhou, L., C. Zhang, F. Liu, Z. J. Qiu, and Y. He. (2019). Application of deep learning in food: A review. Comprehensive Reviews in Food Science and Food Safety 18 (6):1793–811. Doi: https://doi.org/10.1111/1541-4337.12492
  • Zhou, L., Zhang, C., Liu, F., Qiu, Z. and He, Y. (2019). Application of Deep Learning in Food: A Review. Comprehensive Reviews in Food Science and Food Safety, 18: 1793-1811. Doi: https://doi.org/10.1111/1541-4337.12492
  • Zhu, L., Spachos, P., Pensini, E.,& Plataniotis, K.N. (2021). Deep learning and machine vision for food processing: A survey. Current Research in Food Science 4:233–249
  • Zion, B., Shklyar, A., and Karplus, I. (1999). Sorting fish by computer vision. Computers and Electronics in Agriculture, 23(3): 175–187.
Toplam 175 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Konuşma Tanıma, Yapay Zeka (Diğer)
Bölüm Review Articles
Yazarlar

Chrıstıan Ayısı Larbı 0000-0002-0779-5067

Samuel Ayeh Osei 0000-0003-1753-8088

Erken Görünüm Tarihi 20 Mart 2024
Yayımlanma Tarihi
Gönderilme Tarihi 22 Kasım 2023
Kabul Tarihi 19 Şubat 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 8 Sayı: 1

Kaynak Göster

APA Ayısı Larbı, C., & Osei, S. A. (2024). Reduction of Losses and Wastage in Seafoods: The Role of Smart Tools and Biosensors Based on Artificial Intelligence. Journal of AI, 8(1), 14-44. https://doi.org/10.61969/jai.1394542

Journal of AI
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Publisher
Izmir Academy Association
www.izmirakademi.org