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A method overview in smart aquaculture.

Zhuhua Hu1, Ruoqing Li1, Xin Xia1

  • 1School of Information and Communication Engineering, School of Computer Science and Cyberspace Security, State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, China.

Environmental Monitoring and Assessment
|July 10, 2020
PubMed
Summary
This summary is machine-generated.

Smart aquaculture uses technology for real-time monitoring and prediction of water quality and fish behavior. This review summarizes two decades of research, aiding the development of efficient and ecological aquaculture systems.

Keywords:
Artificial intelligenceFish behaviorFish body characteristicsPrecision agricultureWater quality prediction

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Area of Science:

  • Aquaculture
  • Agricultural Science
  • Environmental Monitoring

Background:

  • Aquaculture is vital to the agricultural economy, but historically prone to accidents due to reliance on subjective experience.
  • Water quality is a critical factor for aquaculture production efficiency and quality, necessitating robust management.
  • The integration of intelligence and informatization, termed smart aquaculture, is emerging as a significant trend.

Purpose of the Study:

  • To review and summarize research achievements in smart aquaculture over the past two decades.
  • To analyze the mechanisms linking fish behavior and water quality factors.
  • To provide a knowledge reference for future research and development in smart aquaculture.

Main Methods:

  • Comprehensive literature review of research over the last 20 years.
  • Analysis of studies focusing on water quality factor acquisition, pre-processing, and prediction.
  • Examination of research on fish morphological and behavioral characteristic recognition.

Main Results:

  • Summarized advancements in acquiring, processing, and predicting water quality parameters in aquaculture.
  • Highlighted progress in recognizing fish morphology and behavior for environmental inference.
  • Identified the interplay between fish characteristics, behavior, and water quality dynamics.

Conclusions:

  • Smart aquaculture technologies offer real-time monitoring, prediction, and risk control for aquaculture environments.
  • Understanding the relationship between fish behavior and water quality is key to ecological and efficient aquaculture.
  • This review provides a foundation for advancing smart, ecological, and efficient aquaculture practices.