AI-DRIVEN FISH DISEASE DETECTION FOR ACCURATE DISEASE IDENTIFICATION IN AQUATIC LIFE

Abstract

Fish diseases are a significant concern in aquaculture, causing economic losses and threatening biodiversity. Early and accurate detection is crucial to prevent the spread of these diseases and maintain the health of aquatic life. Traditional methods of disease detection relied on visual inspections and expert knowledge, which were time-consuming and often inaccurate. These systems, though helpful, lacked scalability and efficiency. AI-driven systems have revolutionized fish disease detection by leveraging machine learning and deep learning algorithms. These systems use large datasets and image recognition techniques to identify diseases based on visual symptoms, improving the accuracy and speed of diagnosis. The history of AI in this field began with basic image processing and evolved to more advanced models capable of accurate disease identification. Prior to AI, disease detection relied heavily on manual methods like bacterial culture tests and microscopic analysis, which were labor-intensive and slow. These methods made it difficult to diagnose diseases in a timely manner, leading to potential spread and economic loss. The introduction of AI has automated this process, enabling real-time detection and accurate diagnosis. The motivation for developing an AI-driven fish disease detection system lies in the need for efficient and scalable solutions in the aquaculture industry. The rising global demand for seafood, coupled with the challenges of maintaining fish health, necessitates technological advancements to ensure rapid disease management. AI offers an opportunity to optimize disease detection, making it faster, more accurate, and accessible. Traditional systems faced challenges such as subjectivity in diagnoses, slow diagnostic processes, and high reliance on human expertise. These issues increased costs and delayed interventions. The proposed AI-based system addresses these problems by using machine learning to automate the detection process, providing timely and accurate disease identification, and making the solution more accessible to a broader audience.