Table 1 Survey of various fish species classification models

Fouad et al. [10]

Scale Invariant Feature Transform (SIFT) along with Speeded up Robust Features (SURF) followed by Support Vector machine (SVM)

Experimentation had illustrated that 94.4% detection accuracy was attained by the fish classification utilizing SURF and SVM

The presented work was not utilized in a real-time environment

Qin et al. [17]

Framework centered upon Principal Component Analysis (PCA)

SVM was utilized as a machine learning classifier that illustrated 98.64% classification accuracy against a real-world fish recognition dataset

The filters of the proffered system were not as nice

Siddiqui et al. [18]

CNN

For the fish captured by the Western Australia coast, this strategy depicted 94.3% classification accuracy

In this work, the training data used was limited

Lakshmi et al. [20]

Gaussian mixture model + SURF

Utilizing multiclass-SVM (MSVM), the testing was executed which exhibited 88.9% classification accuracy

High false positives were possessed by this method

Jalal et al. [21]

YOLO deep neural network

Fish detection F-scores of 95.47% along with 91.2% were attained by this method, whilst fish species classification accuracies of 91.64% along with 79.8% were obtained on datasets correspondingly

More computational power was utilized by the proposed system since it comprised a complex machine learning tool when contrasted to traditional computer vision along with image processing approaches

Villon et al. [19]

Few Shot Learning (FSL)

This work found that the classic DL approach was outperformed by FSL in situations along with good classification accuracy was offered

The annotated images in this work were restricted