Indian Journal of Animal Research
SCOPUSWeb of Science
  • Year: 2026
  • Volume: 60
  • Issue: 6

Application of Densenet201 - Convolution Neural Network for Detection of White Spot Syndrome Virus (WSSV) in Shrimp to Enhance Aquaculture Disease Management

  • Author:
  • Kyung-won Cho1, Taeho Kim2, In Seop Na3,*
  • Total Page Count: 7
  • Page Number: 989 to 995

1Cybertech Co., 276 Greenro, Educastle Apartment 102, Unit 1705, Naju-si, Jeollanam-do, Republic of Korea

2Department of Marine Production Management and Smart Aquaculture Research Center, Chonnam National University, 50, Daehak-ro, Yeosu-si, Jeollanam-do, 59626, Republic of Korea

3Division of Culture Contents, Chonnam National University, 50, Daehak-ro, Yeosu-si, Jeollanam-do, 59626, Republic of Korea

*Corresponding Author: In Seop NA, Division of Culture Contents, Chonnam National University, 50, Daehak-ro, Yeosu-si, Jeollanamdo, 59626, Republic of Korea. Email: ypencil@hanmail.net

Abstract

White Spot Syndrome Virus (WSSV) is a major pathogen in shrimp aquaculture, causing severe economic losses. The early detection of WSSV is essential for managing outbreaks. Traditional diagnostic methods are effective but often slow and resource-intensive. This study investigates the DenseNet201-Convolution Neural Network model for efficient WSSV detection.

An online dataset of shrimp images was prepared, including healthy and WSSV-infected samples. Images were preprocessed and fed into DenseNet201 - convolutional neural network. The model was fine-tuned for WSSV detection. Its performance was evaluated using classification metrics.

The model demonstrated high performance, achieving a training accuracy of 99.8% and a validation accuracy of 97%. Precision, recall and F1 score for the WSS class were 97.06%, 94.29% and 95.65%, respectively, while for the healthy class, they were 93.10%, 96.43% and 94.74%. The overall accuracy reached 95.24%, with an MCC of 0.905. The ROC curve showed an AUC of 1 for both classes, indicating perfect classification performance. The DenseNet201-based CNN model successfully detects WSS in shrimp with high accuracy and generalizability. This approach provides a robust tool for early disease detection in aquaculture, though future work should focus on dataset expansion and real-world validation to enhance the model’s robustness under diverse conditions.

Keywords

Convolutional neural network (CNN), Densenet201, Image classification, Shrimp disease detection, White spot syndrome (WSS)