Measuring rainbow trout by using simple statistics

Romero, Marcelo; Miranda, José Manuel; Montes-Venegas, Hector A.

doi:10.1016/b978-0-12-802045-6.00003-x

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…However, the rapid development of computer vision technology provides an effective method of measuring the relationship between fish length and quality (Torisawa et al., 2011), and provides the possibility to determine feeding strategies and changes from the breeding environment (Li et al., 2019). Therefore, many researchers use computer vision technology to measure the length and weight of fish (Romero et al, 2015).…”

Section: Estimation Of the Weight And Length Of Fishmentioning

confidence: 99%

Application of computer vision in fish intelligent feeding system—A review

Jiang

Wei

et al. 2020

Aquac. Res.

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Section: Estimation Of the Weight And Length Of Fishmentioning

confidence: 99%

Application of computer vision in fish intelligent feeding system—A review

Jiang

Wei

et al. 2020

Aquac. Res.

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Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review

Hao

Duan

2019

Reviews in Aquaculture

130

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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.

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Measuring rainbow trout by using simple statistics

Cited by 2 publications

References 3 publications

Application of computer vision in fish intelligent feeding system—A review

Application of computer vision in fish intelligent feeding system—A review

Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review

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