Recent Advancements in Deep Learning Frameworks for Precision Fish Farming Opportunities, Challenges, and Applications - Gaganpreet Kaur; Nirmal Adhikari; Singamaneni Krishnapriya; Surindar Gopalrao Wawale; R. Q. Malik; Abu Sarwar Zamani; Julian Perez-Falcon; Jonathan Osei-Owusu (Journal of Food Quality)
The growth of the fish is influenced by a variety of scientific factors. So, profit can be easily achieved by using some clever techniques, for example, maintaining the correct pH level along with the dissolved oxygen (DO) level and temperature, as well as turbidity for good growth of fish. Fully grown fish are generally sold at a good price because price of fish in the market is governed by weight as well as size of nurtured fish. Artificial intelligence (AI)-based systems may be created to regulate key water quality factors including salinity, dissolved oxygen, pH, and temperature. The software programme operates on an application server and is connected to multiparameter water quality meters in this system. This study examines smart fish farming methods that show how complicated science and technology may be simplified for use in seafood production. This research focuses on the use of artificial intelligence in fish culture in this setting. The technical specifics of DL approaches used in smart fish farming which includes data and algorithms as well as performance was also examined. In a nutshell, our goal is to provide academics and practitioners with a better understanding of the current state of the art in DL implementation in aquaculture, which will help them deploy smart fish farming applications as well their benefits.