Fish Freshness Detection Dataset Using Image Processing and Deep Learning

Md Mijanur Rahman, Sumaya Akter Shumi, Md.Shahidur Rahman Shahid, M Jahangir Alam, Sadiya Yesmin

This dataset contains over 6000+ real-world fish images collected by the group members to support ArtificialIntelligence and Deep Learning research on fish freshness classification. The images represent different fish species,multiple freshness levels, and diverse real-world market environments. The dataset includes three fish categories:Pangas, Rui, and Tilapia.The images are organized into three freshness classes: Fresh, Medium Fresh, and Spoiled, where each foldercontains images corresponding to that category. All images were manually labeled and verified using computersoftware to ensure accurate and consistent annotations. The dataset follows a structured naming convention basedon fish species, freshness level, and image number for easier organization and identification.Images were captured using smartphone cameras under different lighting conditions, viewing angles, andenvironmental settings to improve dataset diversity and real-world applicability. This dataset is intended for imageclassification tasks to train, validate, and evaluate CNN-based models capable of automatically detecting fishfreshness conditions. Although the dataset reflects practical market scenarios, variations in lighting, background, andimage quality may still be present.<br>Categories for this DatasetFresh – fish with bright eyes, red gills, and healthy skin textureMedium Fresh – fish showing moderate freshness characteristicsSpoiled – fish showing dull eyes, damaged texture, and spoilage signs