dc.contributor.author | Li, Daoliang | |
dc.contributor.author | Hao, Yinfeng | |
dc.contributor.author | Duan, Yanqing | |
dc.date.accessioned | 2022-01-17T21:18:49Z | |
dc.date.available | 2022-01-17T21:18:49Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Li, D., Hao, Y. and Duan, Y. (2020) Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review. Reviews in Aquaculture, 12, pp.1390-1411. DOI: https://doi.org/10.1111/raq.12388 | en_US |
dc.identifier.uri | https://repository.oceanbestpractices.org/handle/11329/1846 | |
dc.description.abstract | Fish biomass estimation is one of the most common and important practices in
aquaculture. The regular acquisition of fish biomass information has been identified
as an urgent need for managers to optimize daily feeding, control stocking
densities and ultimately determine the optimal time for harvesting. However, it is
difficult to estimate fish biomass without human intervention because fishes are
sensitive and move freely in an environment where visibility, lighting and stability
are uncontrollable. Until now, fish biomass estimation has been mostly based on
manual sampling, which is usually invasive, time-consuming and laborious.
Therefore, it is imperative and highly desirable to develop a noninvasive, rapid
and cost-effective means. Machine vision, acoustics, environmental DNA and
resistivity counter provide the possibility of developing nonintrusive, faster and
cheaper methods for in situ estimation of fish biomass. This article summarizes
the development of these nonintrusive methods for fish biomass estimation over
the past three decades and presents their basic concepts and principles. The
strengths and weaknesses of each method are analysed and future research directions
are also presented. Studies show that the applications of information technology
such as advanced sensors and communication technologies have great
significance to accelerate the development of new means and techniques for more
effective biomass estimation. However, the accuracy and intelligence still need to
be improved to meet intensive aquaculture requirements. Through close cooperation
between fisheries experts and engineers, the precision and the level of intelligence
for fish biomass estimation will be further improved based on the above
methods. | en_US |
dc.language.iso | en | en_US |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject.other | Fish acoustics | en_US |
dc.subject.other | Fish biomass estimation | en_US |
dc.subject.other | Environmental DNA | en_US |
dc.subject.other | eDNA | en_US |
dc.subject.other | Machine vision | en_US |
dc.subject.other | Resistivity counter | en_US |
dc.title | Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review. | en_US |
dc.type | Journal Contribution | en_US |
dc.description.refereed | Refereed | en_US |
dc.format.pagerange | pp.1390–1411 | en_US |
dc.identifier.doi | https://doi.org/10.1111/raq.12388 | |
dc.subject.parameterDiscipline | Fish | en_US |
dc.subject.dmProcesses | Data acquisition | en_US |
dc.bibliographicCitation.title | Reviews in Aquaculture | en_US |
dc.bibliographicCitation.volume | 12 | en_US |
dc.description.sdg | 14.a | en_US |
dc.description.eov | Fish abundance and distribution | en_US |
dc.description.methodologyType | Method | en_US |
dc.description.methodologyType | Reports with methodological relevance | en_US |
obps.contact.contactname | Daoliang Li | |
obps.contact.contactemail | dliangl@cau.edu.cn | |
obps.resourceurl.publisher | https://onlinelibrary.wiley.com/doi/10.1111/raq.12388 | |