Discussions

AI can be integrated with the FishBase API to improve species classification by using FishBase as a structured biological dataset and applying machine learning models on top of it.

A practical approach looks like this:

AI systems first pull species-level data from FishBase, including taxonomy, morphology (length, weight, body shape), habitat type, and distribution. This structured data is then converted into usable features for machine learning models.

For classification, traditional models like Random Forest or XGBoost can be used when working with structured data. If image data is included, deep learning models such as Convolutional Neural Networks (CNNs) can classify fish species based on visual features, using FishBase labels as training references.

In more advanced setups, AI can combine multiple inputs—biological data from FishBase, environmental sensor data (temperature, depth, salinity), and images—to improve accuracy in real-world marine identification systems.

Overall, FishBase provides reliable scientific ground truth, while AI adds predictive capability, making automated and scalable species classification possible for research, conservation, and fisheries management.