Explainable Neuro-fuzzy System for Coral Reef Fish Classification Using Multi-Scale Visual Features
Keywords:
Coral reef fish classification, Explainable AI, Fuzzy inference, Marine biodiversity monitoring, Multi-scale features, Neuro-fuzzy systemsAbstract
Automated coral reef fish classification from underwater imagery is essential for scalable biodiversity monitoring, yet the deep convolutional networks achieving the highest accuracy remain largely opaque, limiting adoption by marine scientists who must justify conservation decisions. This article presents an explainable neuro-fuzzy system that classifies coral reef fish species from multi-scale visual features while producing human-readable justifications for every prediction. The proposed architecture extracts fine-, mid-, and coarse-scale convolutional features corresponding to local texture, body shape, and global coloration, fuses them into a unified embedding, and passes this embedding through a Takagi-Sugeno neuro-fuzzy inference layer whose linguistic rules and membership functions remain directly inspectable. A complementary explanation module combines fuzzy rule firing strengths with gradient-based saliency maps to generate joint symbolic-visual explanations. The architecture, its multi-scale feature extraction strategy, the fuzzy rule base, and representative underwater fish and coral datasets are described, and comparative tables summarize architectural components, datasets, and accuracy-interpretability trade-offs against convolutional and standard neuro-fuzzy baselines. Results indicate the proposed system retains competitive classification accuracy while substantially improving interpretability relative to conventional deep learning classifiers, supporting its use as a trustworthy tool for automated reef fish monitoring and conservation decision support.
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