Deep Learning Based Fish Species and Freshness Detection Using Convolutional Neural Networks

Maintaining fish freshness is essential in the seafood industry to ensure product safety, quality, and consumer satisfaction. Conventional assessment techniques primarily depend on manual inspection or laboratory-based analysis, which may be subjective, time-intensive, and unsuitable for rapid evaluation. This study introduces a deep learning–driven method for identifying fish species and determining freshness levels using Convolutional Neural Networks (CNN). The framework categorizes fish images into defined freshness classes, including Fresh, Medium, and Spoiled. A MobileNet-based model is adopted due to its lightweight architecture and efficient computational performance. The dataset comprises annotated fish images collected under different storage conditions. Model training and evaluation are conducted using standard classification metrics. Experimental findings indicate strong predictive accuracy and demonstrate the feasibility of deploying the system for real-time applications in seafood markets and processing facilities.