Artificial Intelligence Assessment of Parasitic Infections in Freshwater Fish

Abstract

The health status of freshwater fish is significant for ensuring biodiversity, food security, and sustainable development of aquaculture. Nevertheless, parasitic infections are still considered a big problem, because they negatively influence growth, survival, immune response of fish and the equilibrium of aquatic ecosystems. The early diagnosis of diseases caused by parasites is crucial in order to minimize mortality and enhance aquatic health monitoring. Existing traditional parasite detection techniques, such as microscopic observation and laboratory diagnosis, demand high level of expertise, time and financial investment, and therefore cannot be used for the purposes of large-scale monitoring. The suggested system takes into consideration the fish health parameters, biological attributes, environmental parameters, and AI-based analysis methods to detect the diseases automatically. The AI systems detect the infected conditions by analysing the image-based attributes, abnormal biological features, and other related factors. Such systems provide effective solutions for parasite detection, disease monitoring, and decision-making in the field of aquatic health management. The use of AI in the field of freshwater fish disease detection is an intelligent and scalable solution for sustainable aquaculture and ecosystem protection