Article
ENHANCED AQUACULTURE HEALTH MANAGEMENT USING DEEP TRANSFER LEARNING
Fish disease classification plays a vital role in the success and sustainability of seafood aquaculture, especially in countries like India where the sector makes a substantial economic contribution. Traditionally, disease identification has relied on manual inspection and visual assessment by aquaculture experts. However, these conventional methods are often time-consuming, inconsistent, and heavily reliant on subjective human expertise, leading to potential delays and inaccuracies in diagnosis. To overcome these limitations, the present study focuses on developing automated fish health monitoring systems using cameras and sensors for real-time surveillance. By integrating image processing techniques with deep learning and transfer learning algorithms, the proposed system aims to significantly improve the precision and speed of fish disease classification. The implementation of such smart aquaculture systems marks a transformative step in aquaculture management. These intelligent solutions not only enhance early disease detection and response but also promote more sustainable farming practices by reducing dependency on manual processes. Overall, the use of realtime data analytics and automation contributes to increased productivity, better fish welfare, and lower environmental impact
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