PriMera Scientific Medicine and Public Health (ISSN: 2833-5627)

Research Article

Volume 8 Issue 5

Emerging AI and ML Tools for Rapid Diagnosis of Bacterial Diseases in Aquaculture

Podeti Koteshwar Rao*

May 09, 2026

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Abstract

The aquaculture industry is crucial to global food security but remains highly vulnerable to bacterial diseases, which can cause substantial economic losses and environmental degradation. Conventional diagnostic methods, although effective, are often slow, labor-intensive, and dependent on specialized laboratory infrastructure. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have introduced innovative, rapid, and cost-effective approaches for diagnosing bacterial infections in aquaculture. AI and ML models are capable of analyzing complex datasets, including clinical symptoms, water quality indicators, histopathological images, and molecular profiles, thereby enabling early detection and accurate classification of pathogenic bacteria. Techniques such as convolutional neural networks (CNNs), support vector machines (SVMs), decision trees, and deep learning algorithms have shown impressive success in automating the diagnostic process with high levels of sensitivity and specificity. Furthermore, the integration of AI-powered diagnostic tools with Internet of Things (IoT) technologies and mobile applications has enhanced real-time monitoring and early warning capabilities, allowing aqua farmers to undertake timely interventions. This review explores the current landscape of AI and ML applications in the rapid diagnosis of bacterial diseases in aquaculture, presents notable case studies, discusses challenges such as data scarcity and model generalization, and outlines future research directions aimed at developing more robust, explainable, and field-deployable systems. The widespread adoption of these emerging technologies holds the potential to transform fish health management, foster sustainable aquaculture practices, and protect aquatic ecosystems.

Keywords: Aquaculture; Bacterial Diseases; Artificial Intelligence; Machine Learning; Rapid Diagnosis

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