Deep learning-based appearance features extraction for automated carp species identification |
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| Institution: | 1. Department of Animal Sciences, Lorestan University, Khorramabad, Iran;2. Department of Mechanical Engineering of Agricultural Machinery, University of Tehran, Karaj, Iran;3. Mechanical Engineering of Biosystems Department, Lorestan University, Khorramabad, Iran;1. Department of Animal Sciences, Lorestan University, Khorramabad, Iran;2. Department of Mechanical Engineering of Agricultural Machinery, University of Tehran, Karaj, Iran;3. Mechanical Engineering of Biosystems Department, Lorestan University, Khorramabad, Iran;1. Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran;2. Department of Fisheries, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran;1. Faculty of Computer Science and Engineering, Frankfurt University of Applied Sciences, Frankfurt am Main, Germany;2. Institute for Molecular and Infectious Biology, Julius-Maximilians-Universität Würzburg, Würzburg, Germany;3. Faculty of Computers and Information, Cairo University, Egypt;4. Scientific Research Group in Egypt (SRGE), Egypt;1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China;2. Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry, Jiangsu University, Zhenjiang 212013, China;1. Dalian Ocean University, Dalian 116023, China;2. Key Laboratory of Environment Controlled Aquaculture, Ministry of Education, Dalian 116023, China;3. Key Laboratory of Marine Information Technology of Liaoning Province, Dalian 116023, China |
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| Abstract: | Fish species identification is vital for aquaculture and fishery industries, stock management of water bodies and environmental monitoring of aquatics. Traditional fish species identification approaches are costly, time consuming, expert-based and unsuitable for large-scale applications. Hence, in this study, a deep learning neural network as a smart, real-time and non-destructive method was developed and applied to automate the identification of four economically important carp species namely common carp (Cyprinus carpio), grass carp (Ctenopharingodon idella), bighead carp (Hypophtalmichthys nobilis) and silver carp (Hypophthalmichthys molitrix). The obtained results proved that our approach, evaluated through 5-fold cross-validation, achieved the highest possible accuracy of 100 %. The achieved high level of classification accuracy was due to the ability of the suggested deep model to build a hierarchy of self-learned features, which was in accordance with the hierarchy of these fish’s identification keys. In conclusion, the proposed convolutional neural network (CNN)-based method has a single and generic trained architecture with promising performance for fish species identification. |
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| Keywords: | Carp species Classification Deep learning VGG16 Feature visualization |
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