TITLE:               

Exploring Innovations in Aquaculture: A Comprehensive Investigation of Smart Fish Farming Techniques

AUTHORS: 

Mushtaq Ahmed D M, S R Mani Sekhar, Ashok Kumar A R, Pavithra N

 DOI:  10.5110/77. 1604               Page:   04-17         Vol: 19    Issue: 06   Year: 2024

ABSTRACT

Aquaculture, or fish farming, is a crucial industry for satisfying the rising demand for seafood on a global basis. However, issues with disease identification, feed optimization, environmental effect, and water quality control frequently confront conventional agricultural practices. Combining artificial intelligence (AI) and Internet of Things (IoT) technology offers a viable way to overcome these obstacles and improve the sustainability and effectiveness of fish farming methods. IoT sensors deployed in aquaculture facilities enable real-time monitoring of crucial parameters such as water temperature, pH levels, dissolved oxygen, and ammonia concentrations. These sensors collect vast amounts of data, which, when combined with AI algorithms, can provide valuable insights for farm management. AI algorithms can analyse the data to predict trends, identify anomalies, and optimize operational processes. Predictive analytics for managing water quality is an essential application of IoT and AI in fish farming. AI systems may anticipate any problems with water quality before they arise by evaluating recent sensor readings and previous data, giving farmers the opportunity to take proactive preventive action. Additionally, AI-driven feed optimization systems can modify feeding schedules and amounts to minimize waste and maximize growth by analysing fish behaviour, ambient factors, and dietary needs. AI-based disease detection systems are able to investigate physiological data and fish behaviour to identify signs of illness at an early stage. This allows for rapid diagnosis and lowers the likelihood of disease epidemics. IoT-enabled environmental monitoring systems may also measure variables like water flow and weather, which can assist farmers in reducing risks and adapting to changing environmental circumstances. Given that this study proposes an innovative approach for overseeing aquaculture, it was thought necessary to offer a thorough background scenario in order to frame the important concerns.

 Keywords:

Smart, Artificial Neural Network (ANN), Artificial Intelligence (AI), Deep Learning, Fish farming, Innovation, Technology, Community benefits

Received:  08 May 2024

Accepted:   24 May 2024

Published: 05 June 2024