FishNet: A Hybrid Deep Learning and Machine Learning Framework for Precise Fish Species Identification

This article uses CNNs and Random Forest models to automate fish species identification. The neural network design in Table 3 combines CNN hierarchical feature extraction and Random Forest interpretable ensemble learning, combining their capabilities. The study carefully addresses data gathering and preparation problems, emphasizing the need for a broad, well-prepared dataset. Model optimization in Section C uses hyperparameter tweaking, regularization, and machine learning to create a balanced and effective model. Section D shows the model's resilience to varied environmental conditions during species recognition and execution. Table 2 displays precision, recall, and Fl scores, demonstrating the model's versatility across fish species. The findings advance ecological computer vision and offer a viable tool for regulating fisheries, environmental monitoring, and conservation. From the matrix of confusion, the class-specific metrics, and future research, the report suggests that automated identification of fish species systems can have real-world impact and be continuously improved.