Search Results for real-time-classification

Abstract

Identifying fish species in natural aquatic environments remains challenging due to changing light conditions, turbid water, and complex underwater scenes. Most current deep-learning models rely on controlled datasets, which limits their use in real-world settings. This study presents Auto Fish, a mobile deep-learning system for real-time, offline fish species identification on Android devices. The system uses the MobileNetV2 architecture, optimized with TensorFlow Lite for processing on the device. This approach ensures high accuracy while keeping computational costs low. We trained and evaluated the model on a balanced dataset of 8,000 annotated images, including nine marine species: Sea bass, Red sea bream, Horse mackerel, Gilt-head bream, Shrimp, Black sea sprat, Trout, Red mullet, and Striped red mullet. Extensive preprocessing, image enhancement, and stratified sampling helped the model perform well despite variations in lighting and background conditions. The experimental results showed a validation accuracy of 99.2%, with both macro and micro Precision, Recall, and F1-scores around 99.3%, and an average False Positive Rate (FPR) of 0.09%. The system supports offline recognition, cloud syncing via Firebase, and delivers real-time results within 4.2 seconds per image on mid-range smartphones. These findings show that Auto Fish can effectively classify fish species in the field while remaining efficient and easy to use. This work offers a practical AI-based solution that connects research with ecological monitoring, empowering citizen scientists and conservationists to document biodiversity using mobile technology.