Application of machine vision systems in aquaculture with emphasis on fish: state-of-the-art and key issues

渔业 鱼类加工 钥匙(锁)
作者
Mohammadmehdi Saberioon,Asa Gholizadeh,Petr Cisar,Aliaksandr Pautsina,Jan Urban
出处
期刊:Reviews in Aquaculture [Wiley]
卷期号:9 (4): 369-387 被引量:91
标识
DOI:10.1111/raq.12143
摘要

Demands of aquatic products are increasing dramatically during past decades. Also quality assurance has gradually received more attention by both producers and consumers. Thus, fish producers are exploring all possible approaches for improving the productivity and profitability. Monitoring of fish state and behaviour during cultivation may help to improve profitability for producers and also reduce the threat of severe loss because of disease and stress incidents. It is necessary to evaluate and measure quality of fish products in accurate, fast and objective way for meeting the different demands of the fish-processing industry after harvesting. Traditional methods are usually time-consuming, expensive, laborious and invasive. Using rapid, inexpensive and noninvasive methods is therefore important and desirable. Optical sensors and machine vision system provide the possibility of developing faster, cheaper and noninvasive methods for in situ and after harvesting monitoring of quality in aquaculture. This review describes the most recent technologies and the suitability of different optical sensors for the fish farming management and also assessment, measurement and prediction of fish products quality. Two major areas of optical sensors applications in aquaculture are discussed in this review: (i) preharvesting and during cultivation; and (ii) post-harvesting. Finally, accuracy and uncertainty of optical sensors applications in aquaculture are discussed. This review showed that MVSs and optical sensors have found real-world application based on tremendous possibility offered by digital camera development and increasing the speed of computer-based processing; however, still new algorithms, methods and re-engineered sensors need to be developed to meet real-world requirements.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
Shopping完成签到,获得积分10
2秒前
wade2016发布了新的文献求助10
2秒前
2秒前
张水果发布了新的文献求助10
2秒前
3秒前
萌萌完成签到,获得积分10
3秒前
繁星完成签到,获得积分10
3秒前
ky完成签到,获得积分10
4秒前
朴实傲白完成签到 ,获得积分10
5秒前
大白不白发布了新的文献求助10
5秒前
zhuo完成签到,获得积分10
5秒前
濠哥妈咪发布了新的文献求助10
5秒前
Nole应助8023采纳,获得10
5秒前
5秒前
Felix发布了新的文献求助10
6秒前
MHR发布了新的文献求助10
6秒前
yyyyyzy完成签到,获得积分10
6秒前
sp1cy完成签到,获得积分10
6秒前
yi应助好好学习采纳,获得10
7秒前
7秒前
佛光辉发布了新的文献求助10
7秒前
桐桐应助fortune采纳,获得10
7秒前
maowei发布了新的文献求助10
8秒前
傲娇的期待完成签到,获得积分10
8秒前
流光云集发布了新的文献求助10
9秒前
Hanyi完成签到,获得积分10
9秒前
是我发布了新的文献求助10
10秒前
科研通AI6.4应助MUXIYOU采纳,获得10
10秒前
珍珠火龙果完成签到 ,获得积分0
10秒前
濠哥妈咪完成签到,获得积分10
10秒前
11秒前
江梦槐完成签到,获得积分10
11秒前
赵月丽完成签到,获得积分20
11秒前
pluto应助fly采纳,获得10
11秒前
丘比特应助田心采纳,获得10
12秒前
12秒前
kaiz发布了新的文献求助30
12秒前
13秒前
传奇3应助wade2016采纳,获得10
13秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
An Introduction to Foreign Language Learning and Teaching 750
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
What is the Future of Psychotherapy in Digital Age? Technology, AI Bots, and Psychotherapy after Covid 444
Synthesis of P-Chiral Phosphine Ligands and Their Applications in Asymmetric Catalysis 400
Management and the Arts 310
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7629771
求助须知:如何正确求助?哪些是违规求助? 9204099
关于积分的说明 19737206
捐赠科研通 7199233
什么是DOI,文献DOI怎么找? 3274326
关于科研通互助平台的介绍 2436461
邀请新用户注册赠送积分活动 2270482