Abstract

Abstract: Ensuring fish health is crucial in aquaculture, where water quality plays a significant role in disease outbreaks. Poor environmental conditions can lead to severe infections, impacting fish survival rates and economic productivity. This study presents a deep learningbased framework for the early diagnosis of fish diseases using water quality parameters. By leveraging advanced neural network models, the system analyzes key environmental factors such as pH, temperature, dissolved oxygen, and ammonia levels to detect potential health risks. The proposed model utilizes a combination of data preprocessing, feature extraction, and classification techniques to enhance prediction accuracy.Experimental results demonstrate that the deep learning framework effectively identifies disease patterns with high precision, providing an early warning mechanism for aquaculture farmers. The integration of AI-based monitoring systems reduces fish mortality rates and ensures better resource management. Furthermore, the automated detection system minimizes the need for manual inspections, allowing for real-time analysis and decision-making. The adaptability of the model enables its application across various aquaculture settings, making it a scalable and cost-effective solution for disease prevention.The findings of this research indicate that deep learning models can significantly enhance aquaculture disease management by providing accurate and timely predictions. Future enhancements may include real-time sensor data integration, edge computing for on-site analysis, and the application of explainable AI techniques to improve transparency and trust in decisionmaking. Additionally, expanding the model to include multiple fish species and a broader range of water quality indicators will further refine its effectiveness and usability.