Detection of White Spot Syndrome Virus in Shrimp using Convolutional Neural Network

Background: Shrimp rearing and wild-catch shrimp fisheries provide employment and income for millions worldwide. As the global population rises, shrimp plays a key role in addressing food security challenges. It is a nutritious and accessible food, widely consumed in diverse cuisines. Ecologically, it serves as scavengers and detritivores, maintaining stability in aquatic environments. This ecological role has driven the development of sustainable shrimp farming practices. Disease management, especially prevention and early detection, is a critical concern in shrimp farming. Automated image processing could speed up disease detection, ensuring timely intervention. This study focuses on detecting white spot syndrome virus (WSSV) in shrimp using convolutional neural networks (CNN) with promising accuracy. Methods: The model uses a sequential convolutional neural network (CNN) to identify WSSV in shrimp. It is trained with images categorized into two groups: Healthy and diseased. Features like lesions and discoloration are used to detect infection. The dataset is preprocessed to reduce resolution variation through bilinear interpolation. The model’s architecture consists of multiple layers that extract features and reduce spatial dimensions. Concatenation layers combine information from different network parts to enhance feature extraction. Performance is evaluated to determine the model’s ability to make reliable predictions. Result: The CNN model achieved a remarkable accuracy (93.75%) in detecting WSSV. It correctly identified all WSSV instances with minimal misclassification. The overall performance of the model demonstrates its robustness. The ROC curves further confirm its ability to classify both healthy and diseased shrimp effectively, highlighting its potential for early disease detection in shrimp farming systems.