Automated within tank fish mass estimation using infrared reflection system

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12520%2F18%3A43897168" target="_blank" >RIV/60076658:12520/18:43897168 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0168169917310001" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0168169917310001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compag.2018.05.025" target="_blank" >10.1016/j.compag.2018.05.025</a>

Result language

angličtina

Original language name

Automated within tank fish mass estimation using infrared reflection system

Original language description

Fish size and mass information during different growth stages is important for precise feeding regime management, oxygen consumption calculations, antibiotic prescription and improving fish welfare, but also to facilitate decisions on grading, harvesting and time to harvest. The main purpose of this study was to develop an automatic system to estimate fish mass using fish dorsal geometrical features and machine learning algorithms such as random forest (RF) and support vector machine (SVM). To develop the model, Kinect as a RGB-D camera was used to acquire depth map and top view images of 295 farmed seabass (Dicentrarchus labrax, L.) of different sizes. Eight dorsal geometric features were extracted and used for model development. Ten-fold cross validation was used to optimize and validate the models. Comparison of models was made in term of the coefficient of determination (R-cv(2)) and Root Mean Square Error of prediction (RMSEP) of cross validation. Both models exhibited significant prediction, however, SVM algorithm with R-cv(2) of 0.872 (p &lt; 0.01) and RMSE of 0.13 gave a slightly better prediction of weight compared to RF with R-cv(2) of 0.868 (p &lt; 0.01) and RMSE of 0.13 was the highest R-2. Subsequently, Infrared reflection system (IREF) which is composed of a NIR range camera with an external illuminator, was also used in this study to acquire 20 fish dorsal images inside the tank. Like validation results, both algorithms had the significant prediction, however, despite the validation results, RF with R-2 = 0.84(p &lt; 0.01)andRMSE= 0.12 was better in comparison to SVM with R-2 = 0.72(p &lt; 0.01)andRMSE= 0.16. The study demonstrated that seabass geometrical dorsal features together with machine learning algorithms could be used for mass predictions. Furthermore, the IREF system can be used as a reliable, inexpensive, stressfree and accurate sensor for monitoring and estimating fish mass during cultivation within the tank.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20205 - Automation and control systems

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computers and electronic in agriculture

ISSN

0168-1699

e-ISSN

—

Volume of the periodical

150

Issue of the periodical within the volume

06/2018

Country of publishing house

GB - UNITED KINGDOM

Number of pages

9

Pages from-to

484-492

UT code for WoS article

000437079900048

EID of the result in the Scopus database

2-s2.0-85047604170