Tilapia Fish Freshness Detection Using CNN Models

In the seafood business, fish freshness plays a crucial role since it directly affects quality, customer happiness, and safety. This study uses a well-selected dataset of fresh and non-fresh Tilapia fish species to assess the performance of several CNN models, including VGG-19, MobileNetV2, DenseNet201, and ResNet50, for classifying fish freshness. DenseNet201 performed exceptionally well with an accuracy of 1.0, MobileNetV2 had a high accuracy of 0.99104, VGG19 performed admirably with an accuracy of 0.964809, and Resnet50 offered competitive accuracy of 0.82098. To achieve these results, we designed and implemented a rigorous procedure for training and testing these CNN models using the dataset of both fresh and non-fresh fish species. We used meticulous data preprocessing and model training, bearing in mind the importance of high-quality datasets. Our study's primary findings emphasize how crucial it is to select an appropriate CNN architecture and take dataset quality into account when determining the freshness of seafood. Concerning fish freshness evaluation, DenseNet201 and MobileNetV2 in particular demonstrated remarkable accuracy, underscoring the importance of model selection and data quality.