Performance Analysis of Deep Learning Models for Identifying White Spot Syndrome Virus in Shrimp Aquaculture

Shrimp farming is vital to global aquaculture and seafood production.The White Spot Syndrome Virus (WSSV), which increases shrimp mortality, is among the most prevalent viral illnesses endangering the industry.Using cutting-edge deep learning methods, a novel prediction model for shrimp WSSV diagnosis and detection is developed.WSSV infection detection was performed utilising prawn datasets using VGG16, EfficientNetB0, and ResNet50 convolutional neural networks.The ResNet50 model surpassed the others in accuracy and data pattern recognition, making it a potential early disease detection model.In ResNet50, data enrichment improved model performance.These findings extend deep learning in aquaculture by enabling real-time WSSV monitoring and disease control.Optimisation may allow these technologies to be deployed in resource-constrained environments.