Classification and Recognition of Fish Farming by Extraction New Features to Control the Economic Aquatic Product

Abstract

With the rapid emergence of the technology of deep learning (DL), it was successfully used in different fields such as the aquatic product. New opportunities in addition to challenges can be created according to this change for helping data processing in the smart fish farm. This study focuses on deep learning applications and how to support different activities in aquatic like identification of the fish, species classification, feeding decision, behavior analysis, estimation size, and prediction of water quality. Power and performance of computing with the analyzed given data are applied in the proposed DL method within fish farming. Results of the proposed method show the significance of contributions in deep learning and how automatic features are extracted. Still, there is a big challenge of using deep learning in an era of artificial intelligence. Training of the proposed method used a large number of labeled images got from the Fish4Knowledge dataset. The proposed method based on suitable feature extracted from the fish achieved good results in terms of recognition rate and accuracy.