Deep Learning Algorithm Development for Intelligent Detection and Classification of Carp Species
2021
Taheri-Garavand, Amin | nasiri, amin | Banan, َAshkan
ABSTRACT: Identifying fish species is critical for aquaculture and fishery industries, managing aquatic stocks and environmental monitoring of aquatics. In this study, deep learning neural network as a non-destructive and real-time approach was developed and used to identify four economically important species of carp family including common carp, grass carp, bighead carp and silver carp. For this purpose, the architecture of pre-trained VGG19 (Visual Geometry Group-19) was updated by pooling, fully-connected, normalization and dropout layers. 409 images were used for training and evaluating the developed model. The mean value of accuracy, precision, sensitivity, specificity and AUC parameters was calculated as 98.39, 96.87, 96.87, 98.96, and 97.92%, respectively. The obtained high level of accuracy is due to the ability of the proposed deep model in constructing a hierarchy of self-learned features which was consistent with the hierarchy of fish identification keys.
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