Fishing and Overfishing-Sustainable Harvest of the Sea

Rossi, Sergio

doi:10.1007/978-3-031-19467-2_4

Cited by 1 publication

(1 citation statement)

References 243 publications

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“…The development of selective fishing gear that lowers the accidental capture of non-target species can be facilitated by the application of machine learning algorithms. In addition to protecting biodiversity, this lowers fishermen's financial losses (Rossi, 2022). By analyzing data on the behavior and needs of individual species, machine learning algorithms enable individualized care while consuming the fewest resources possible.…”

Section: Machine Learningmentioning

confidence: 99%

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

AYISI LARBI,

OSEİ

2024

Journal of AI

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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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Section: Machine Learningmentioning

confidence: 99%