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

被引:1
|
作者
Rahman, Wahidur [1 ,2 ]
Rahman, Mohammad Motiur [1 ]
Mozumder, Md Ariful Islam [3 ]
Sumon, Rashadul Islam [3 ]
Chelloug, Samia Allaoua [4 ]
Alnashwan, Rana Othman [4 ]
Muthanna, Mohammed Saleh Ali [5 ,6 ]
机构
[1] Mawlana Bhashani Sci & Technol Univ, Dept Comp Sci & Engn, Tangail 1902, Bangladesh
[2] Uttara Univ, Dept Comp Sci & Engn, Dhaka 1230, Bangladesh
[3] Inje Univ, Inst Digital Antiaging Healthcare, Coll AI Convergence, u AHRC, Gimhae 50834, South Korea
[4] Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Technol, POB 84428, Riyadh 11671, Saudi Arabia
[5] Tashkent State Univ Econ, Dept Int Business Management, Tashkent 100066, Uzbekistan
[6] Southern Fed Univ, Inst Comp Technol & Informat Secur, Taganrog 347922, Russia
关键词
fish classification; salinity fish; freshwater fish; convolutional neural network; classifiers; sustainability; CONSERVATION;
D O I
10.3390/su16187933
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
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页数:23
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