Automatic Monitoring of Relevant Behaviors for Crustacean Production in Aquaculture: A Review.

Daoliang Li,Guangxu Wang,Zhaoyang Song,Chang Liu

doi:10.3390/ani11092709

Daoliang Li, Guangxu Wang + Show 2 more

Open Access

https://doi.org/10.3390/ani11092709

Copy DOI

Abstract

Simple SummaryAutomatic behavior monitoring, also called automated analytics or automated reporting, is the ability of an analytics platform to auto-detect relevant insights—anomalies, trends, patterns—and deliver them to users in real time, without users having to manually explore their data to find the answers they need. An analytics platform with automated behavior monitoring uses algorithms to auto-analyze datasets to search for notable changes in data. It then generates alerts at fixed intervals or triggers (thresholds), and delivers the findings to each user, ready-made. In-aquaculture scoring of behavioral indicators of aquatic animal welfare is challenging, but the increasing availability of low-cost technology now makes the automated monitoring of behavior feasible.Crustacean farming is a fast-growing sector and has contributed to improving incomes. Many studies have focused on how to improve crustacean production. Information about crustacean behavior is important in this respect. Manual methods of detecting crustacean behavior are usually infectible, time-consuming, and imprecise. Therefore, automatic growth situation monitoring according to changes in behavior has gained more attention, including acoustic technology, machine vision, and sensors. This article reviews the development of these automatic behavior monitoring methods over the past three decades and summarizes their domains of application, as well as their advantages and disadvantages. Furthermore, the challenges of individual sensitivity and aquaculture environment for future research on the behavior of crustaceans are also highlighted. Studies show that feeding behavior, movement rhythms, and reproduction behavior are the three most important behaviors of crustaceans, and the applications of information technology such as advanced machine vision technology have great significance to accelerate the development of new means and techniques for more effective automatic monitoring. However, the accuracy and intelligence still need to be improved to meet intensive aquaculture requirements. Our purpose is to provide researchers and practitioners with a better understanding of the state of the art of automatic monitoring of crustacean behaviors, pursuant of supporting the implementation of smart crustacean farming applications.

Highlights

Aquaculture has become one of the largest commercial and economically important industries in recent years [1]
This paper aims to summarize the characteristics of different crustacean behaviors and various automatic aquaculture behavior monitoring methods that have been used over the past three decades
As a large-scale application technology, acoustics are not affected by water turbidity and they can work well in almost invisible conditions

Summary

Introduction

Aquaculture has become one of the largest commercial and economically important industries in recent years [1]. Crayfish, crabs, crayfish, prawns, and shrimp are the most valuable crustacean species groups with significant production. Shrimp and prawn catches recorded new highs in 2017 and 2018 at over 336,000 tons [2]. Most of the modern information technologies are applied to production management and reliable monitoring of crustacean behavior is very important for aquaculture industries because it provides a starting point for welfare assessment [3,4]. Traditional crustacean behavior monitoring is mostly based on manual measurement. Manual monitoring is usually laborious, time-consuming, and ineffective which limits its economic benefits [5,6]

Objectives

Methods

Conclusion