Deep Learning and AI Tools for Monitoring and Detecting Diseases in Freshwater Fish Populations

Freshwater fish populations sustainability and well-being are essential to aquaculture biodiversity and food security conventional approaches to fish disease diagnosis are frequently labor-intensive time-consuming and necessitate professional intervention which causes treatment delays and large financial losses recent developments in deep learning dl a subfield of artificial intelligence AI present viable substitutes for automated quick and precise fish disease detection this study investigates how to use AI and deep learning tools to monitor and diagnose illnesses that impact freshwater fish predictive modeling pattern recognition and image recognition techniques are used by these systems to accurately identify visual symptoms like lesions discoloration or abnormal behavior along with their datasets training procedures and performance metrics the paper examines a variety of machine learning models used in fish health assessment such as convolutional neural networks CNNS support vector machines SVMS and hybrid architectures real-time monitoring systems made possible by internet of things IOT gadgets and AI-powered image processing frameworks are also covered the results show how deep learning can transform aquaculture disease management by improving fish welfare enabling early detection and lowering manual labor the development of robust scalable and economical solutions is one of the future directions