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dc.contributor.authorLi, Daoliang
dc.contributor.authorHao, Yinfeng
dc.contributor.authorDuan, Yanqing
dc.date.accessioned2022-01-17T21:18:49Z
dc.date.available2022-01-17T21:18:49Z
dc.date.issued2020
dc.identifier.citationLi, 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.12388en_US
dc.identifier.urihttps://repository.oceanbestpractices.org/handle/11329/1846
dc.description.abstractFish 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.isoenen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.otherFish acousticsen_US
dc.subject.otherFish biomass estimationen_US
dc.subject.otherEnvironmental DNAen_US
dc.subject.othereDNAen_US
dc.subject.otherMachine visionen_US
dc.subject.otherResistivity counteren_US
dc.titleNonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review.en_US
dc.typeJournal Contributionen_US
dc.description.refereedRefereeden_US
dc.format.pagerangepp.1390–1411en_US
dc.identifier.doihttps://doi.org/10.1111/raq.12388
dc.subject.parameterDisciplineFishen_US
dc.subject.dmProcessesData acquisitionen_US
dc.bibliographicCitation.titleReviews in Aquacultureen_US
dc.bibliographicCitation.volume12en_US
dc.description.sdg14.aen_US
dc.description.eovFish abundance and distributionen_US
dc.description.methodologyTypeMethoden_US
dc.description.methodologyTypeReports with methodological relevanceen_US
obps.contact.contactnameDaoliang Li
obps.contact.contactemaildliangl@cau.edu.cn
obps.resourceurl.publisherhttps://onlinelibrary.wiley.com/doi/10.1111/raq.12388


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International