A Deep CNN-Based Salinity and Freshwater Fish Identification and Classification Using Deep Learning and Machine Learning

Wahidur Rahman, Mohammad Motiur Rahman, Md Ariful Islam Mozumder, Rashadul Islam Sumon, Samia Allaoua Chelloug (), Rana Othman Alnashwan () and Mohammed Saleh Ali Muthanna
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Wahidur Rahman: Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh
Mohammad Motiur Rahman: Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh
Md Ariful Islam Mozumder: Institute of Digital Anti-Aging Healthcare, College of AI Convergence, u-AHRC, Inje University, Gimhae 50834, Republic of Korea
Rashadul Islam Sumon: Institute of Digital Anti-Aging Healthcare, College of AI Convergence, u-AHRC, Inje University, Gimhae 50834, Republic of Korea
Samia Allaoua Chelloug: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Rana Othman Alnashwan: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Mohammed Saleh Ali Muthanna: Department of International Business Management, Tashkent State University of Economics, Tashkent 100066, Uzbekistan

Sustainability, 2024, vol. 16, issue 18, 1-23

Abstract: Concerning the oversight and safeguarding of aquatic environments, it is necessary to ascertain the quantity of fish, their size, and their distribution. Many deep learning (DL), artificial intelligence (AI), and machine learning (ML) techniques have been developed to oversee and safeguard the fish species. Still, all the previous work had some limitations, such as a limited dataset, only binary class categorization, only employing one technique (ML/DL), etc. Therefore, in the proposed work, the authors develop an architecture that will eliminate all the limitations. Both DL and ML techniques were used in the suggested framework to identify and categorize multiple classes of the salinity and freshwater fish species. Two different datasets of fish images with thirteen fish species were employed in the current research. Seven CNN architectures were implemented to find out the important features of the fish images. Then, seven ML classifiers were utilized in the suggested work to identify the binary class (freshwater and salinity) of fish species. Following that, the multiclass classification of thirteen fish species was evaluated through the ML algorithms, where the present model diagnosed the freshwater or salinity fish in the specific fish species. To achieve the primary goals of the proposed study, several assessments of the experimental data are provided. The results of the investigation indicated that DenseNet121, EfficientNetB0, ResNet50, VGG16, and VGG19 architectures of the CNN with SVC ML technique achieved 100% accuracy, F1-score, precision, and recall for binary classification (freshwater/salinity) of fish images. Additionally, the ResNet50 architecture of the CNN with SVC ML technique achieved 98.06% and 100% accuracy for multiclass classification (freshwater and salinity fish species) of fish images. However, the proposed pipeline can be very effective in sustainable fish management in fish identification and classification.

Keywords: fish classification; salinity fish; freshwater fish; convolutional neural network; classifiers; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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