The expansion of aquaculture necessitates innovative disease detection methods to ensure sustainable production. Fish diseases caused by bacteria, viruses, fungi, and parasites result in significant economic losses and threaten food security. Traditional detection methods are labor-intensive and time-consuming, emphasizing the need for automated approaches. This study investigates the application of convolutional neural networks (CNNs) for classifying freshwater fish diseases. Such CNNs offer an efficient and automated solution for fish disease detection, reducing the burden on aquatic health experts and enabling timely interventions to mitigate economic losses. A dataset of 2444 images was used across seven classes—bacterial red disease, bacterial Aeromoniasis disease, bacterial gill disease, fungal disease, parasitic diseases, white tail disease, and healthy fish. The CNNs model incorporates convolutional layers for feature extraction, max-pooling for down-sampling, dense layers for classification, and dropout for regularization. Categorical cross-entropy loss and the Adam optimizer were used over 50 epochs, with continuous training and validation performance monitoring. The results indicated that the model achieved an accuracy of 99.71% and a test loss of 0.0119. This study highlights the transformative potential of artificial intelligence in aquaculture for enhancing food security.

The aquaculture and fisheries sectors are critical sources of nutrition and employment generation in many developed and developing countries. Hence, any disruptions to these sectors due to the COVID-19 pandemic have a far-reaching impact, leading to a cascading chain of disorder affecting people’s food security and livelihoods. Relating to these fish sectors, we reviewed COVID-19’s implications for the food security of these vulnerable countries. The current study indicated that COVID-19 and its related preventive measures have severely disrupted the fish demand and supply chain by creating considerable fish price volatility. As a result, the vulnerable aquatic communities have adopted several short-term coping strategies, including fish overwintering, delayed fish stocking time, and feeding the overcrowded fish with low-priced food. Since the long-term coping strategies are still unclear, we recommended certain longstanding methods that are likely to safeguard food security and livelihoods if adopted.