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Explainable Multi-Loss Distillation Framework for Efficient and Interpretable Shrimp Disease Text Classification

arXiv · AI, language, vision and robotics · article · Aug 29, 2026 · UTC

Shrimp disease classification has become an urgent issue due to its significant impact on the import-export output of producing countries, particularly Vietnam. Most existing studies focus on image-based classification, which typically operates at the late stage of disease manifestation. Therefore, text-based classification has the potential to enable early and timely disease detection. To address this limitation, we introduce the SALT (Shrimp disease text Analysis with multi-Loss disTillation) framework, which incorporates explainability analysis using Local Interpretable Model-agnostic Expla

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