FROM MORPHOLOGY TO MACHINE LEARNING: RANDOM FOREST-BASED APPROACH FOR FISH SPECIES IDENTIFICATION - Aalia Zafar; Muhammad Umer Sarwar; Muhammad Kashif Hanif; Ramzan Talib (Journal of Animal and Plant Sciences, 2026)
Fish are important components of freshwater ecosystems and are one of the main sources of protein for humans. One of the main problems in fishery resource investigation is the accurate identification of fish species and the accurate discrimination of fish stocks. In the context of large data, machine learning techniques as developing data processing techniques have gradually replaced traditional methods. This study demonstrated an automated study, using machine learning, to classify fish species by morphological features (e.g., body shape, size, and fins etc.). Machine learning-based fish species identification is essential for automated ecological monitoring, biodiversity assessment, and fisheries management. This study used a dataset of 540 samples across 20 distinct fish species, a Random Forest model was trained and evaluated using a 10-fold cross-validation strategy, outperforming alternative comparative models in classification accuracy. The procedure used to prepare the dataset consisted of preprocessing, including categorical to binary encoded categorical data (converting non-numeric data to numbers the machine could interpret). Several machine learning models were examined, with the Random Forest (RF) providing the best results, and the RF became a trained, tested, and saved machine learning model for user applications. Subsequently, a slight implementation interface was additionally developed to demonstrate the practical applicability of the trained classification framework that allowed users to enter fish features and receive predictions. The proposed framework illustrates how machine learning approaches may support species identification in fisheries and ecological studies, increase prediction accuracy, and have potential applications in fish farming and research as well as in aquatic ecosystem protection and conservation.
Keywords: journal article; Machine learning; Fish Morphology; Random forest; Streamlit web analytic Approach