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Sensors 2018, 18(4), 1027; https://doi.org/10.3390/s18041027

Comparative Performance Analysis of Support Vector Machine, Random Forest, Logistic Regression and k-Nearest Neighbours in Rainbow Trout (Oncorhynchus Mykiss) Classification Using Image-Based Features

1
Institute of Complex Systems, South Bohemian Research Centre of Aquaculture and Biodiversity of Hydrocenoses, Faculty of Fisheries and Protection of Waters, University of South Bohemia in České Budějovice, Zámek 136, Nové Hrady 37 333, Czech Republic
2
Institut National de la Recherche Agronomique (INRA), UE 0937 PEIMA (Pisciculture Expérimentale INRA des Monts d’Arrée), 29450 Sizun, France
*
Author to whom correspondence should be addressed.
Received: 21 February 2018 / Revised: 25 March 2018 / Accepted: 27 March 2018 / Published: 29 March 2018
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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Abstract

The main aim of this study was to develop a new objective method for evaluating the impacts of different diets on the live fish skin using image-based features. In total, one-hundred and sixty rainbow trout (Oncorhynchus mykiss) were fed either a fish-meal based diet (80 fish) or a 100% plant-based diet (80 fish) and photographed using consumer-grade digital camera. Twenty-three colour features and four texture features were extracted. Four different classification methods were used to evaluate fish diets including Random forest (RF), Support vector machine (SVM), Logistic regression (LR) and k-Nearest neighbours (k-NN). The SVM with radial based kernel provided the best classifier with correct classification rate (CCR) of 82% and Kappa coefficient of 0.65. Although the both LR and RF methods were less accurate than SVM, they achieved good classification with CCR 75% and 70% respectively. The k-NN was the least accurate (40%) classification model. Overall, it can be concluded that consumer-grade digital cameras could be employed as the fast, accurate and non-invasive sensor for classifying rainbow trout based on their diets. Furthermore, these was a close association between image-based features and fish diet received during cultivation. These procedures can be used as non-invasive, accurate and precise approaches for monitoring fish status during the cultivation by evaluating diet’s effects on fish skin. View Full-Text
Keywords: image colour properties; image texture properties; machine vision system; supervised classification; image processing image colour properties; image texture properties; machine vision system; supervised classification; image processing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

  • Externally hosted supplementary file 1
    Doi: 10.6084/m9.figshare.5978392
    Link: https://doi.org/10.6084/m9.figshare.5978392
    Description: RAW images from Rainbow trout (Oncorhynchus mykiss) which received either fish meal based diet or plant-based diet.
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Saberioon, M.; Císař, P.; Labbé, L.; Souček, P.; Pelissier, P.; Kerneis, T. Comparative Performance Analysis of Support Vector Machine, Random Forest, Logistic Regression and k-Nearest Neighbours in Rainbow Trout (Oncorhynchus Mykiss) Classification Using Image-Based Features. Sensors 2018, 18, 1027.

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