Research on a fish disease diagnosis method integrating domain knowledge and Bayesian inference

To address the problems of limited fish disease case data, heterogeneous feature descriptions, and incomplete information in aquaculture scenarios, this study proposes a knowledge-driven lightweight diagnostic method. Authoritative literature and monographs on freshwater fish diseases in China were systematically reviewed, and the acquired disease-related information was subjected to multi-granularity semantic disambiguation and standardized processing. An expert knowledge base was constructed, covering 4 disease type, 83 diseases, 9 symptom part, and 61 typical symptom. Boolean encoding was used to transform expert knowledge into feature vectors, and a Naive Bayes diagnostic model was developed to enable small-sample probabilistic reasoning. To evaluate the practical application performance of the model, an interference validation strategy based on combinatorial subset enumeration was designed to simulate symptom omission and false-reporting scenarios in real diagnostic processes. The results showed that under complete-information conditions, the proposed model achieved accuracy, F1-score, and other core metrics above 0.98; under missing or noisy conditions, its performance remained above 0.85 and outperformed the comparative models. On a Raspberry Pi 5 platform, the training and validation times of the model were both at the millisecond level, with an average central processing unit (CPU) utilization of 4.8%. These results indicate that the proposed method has good diagnostic stability and anti-interference ability under small-sample, symptom-omission, and false-reporting conditions. It also has low computational overhead and is suitable for deployment on low-computing-power terminals, providing technical support for auxiliary fish disease diagnosis in grassroots aquaculture scenarios.