Automated within tank fish mass estimation using infrared reflection system

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Automated within tank fish mass estimation using infrared reflection system

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Automated within tank fish mass estimation using infrared reflection system

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Computers and electronic in agriculture

ISSN

0168-1699

e-ISSN

—

Svazek periodika

150

Číslo periodika v rámci svazku

06/2018

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

9

Strana od-do

484-492

Kód UT WoS článku

000437079900048

EID výsledku v databázi Scopus

2-s2.0-85047604170