DOI : https://doi.org/10.18517/ijaseit.11.3.14134

Local Freshwater Fish Recognition Using Different CNN Architectures with Transfer Learning

Anup Majumder (1), Aditya Rajbongshi (2), Md. Mahbubur Rahman (3), Al Amin Biswas (4)
(1) Department of CSE, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh
(2) Department of CSE, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh
(3) Software Engineer, Crowd Realty, Tokyo, 1700005, Japan
(4) Department of CSE, Daffodil International University, Dhanmondi, Dhaka, 1207, Bangladesh
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How to cite (IJASEIT) :
Majumder, Anup, et al. “Local Freshwater Fish Recognition Using Different CNN Architectures With Transfer Learning”. International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 3, June 2021, pp. 1078-83, doi:10.18517/ijaseit.11.3.14134.
Citation Format :
Bangladesh has its profusion of water resources, but due to environmental issues and some other significant causes, the quantity of water resources is lessening continuously. As a result, many of our local freshwater fishes are being abolished, leading to a lack of knowledge about freshwater fish among the new generation of people in Bangladesh. It is also very difficult to recognize freshwater fish because of their nature, color, shape, and structure. To recognize the local freshwater fish efficiently, transfer learning can be used, one of the significant parts of deep learning that concentrates on storing knowledge gained while solving one problem and employing it to a distinct but related problem. This paper has used six CNN-based architecture with transfer learning, namely DenseNet201, InceptionResnetV2, InceptionV3, ResNet50, ResNet152V2, and Xception. A total of seven freshwater fish image data is used here, which is collected from the various local fish markets of Bangladesh. To check the effectiveness of the working approach, we have calculated the accuracy, precision, Recall, and F1-Score. The approach InceptionResnetV2 and Xception achieved the highest accuracy with 98.81% over the other approach which is a very significant result.

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