CNN-Based Identification of Pathogens of Concern in Shrimp
Issued Date
2025-11-01
Resource Type
eISSN
20762615
Scopus ID
2-s2.0-105021442644
Journal Title
Animals
Volume
15
Issue
21
Rights Holder(s)
SCOPUS
Bibliographic Citation
Animals Vol.15 No.21 (2025)
Suggested Citation
Aung T., Vanichviriyakit R., Chayantrakom K., Amornsamankul S., Huabsomboon P. CNN-Based Identification of Pathogens of Concern in Shrimp. Animals Vol.15 No.21 (2025). doi:10.3390/ani15213194 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/113110
Title
CNN-Based Identification of Pathogens of Concern in Shrimp
Corresponding Author(s)
Other Contributor(s)
Abstract
Concerning shrimp diseases, including acute hepatopancreatic necrosis disease (AHPND), hepatopancreatic parvovirus (HPV) infection and Enterocytozoon hepatopenaei (EHP) microsporidiosis negatively impact shrimp aquaculture through acute mortality, chronic growth retardation or compromised health that increases susceptibility to concurrent infections. All three diseases damage hepatopancreas, a vital organ for nutrient absorption and growth, though their clinical outcomes differ: AHPND is typically associated with rapid, high mortality, EHP primarily causes chronic production losses and HPV, while currently of lower pathogenic significance, may still impair health under certain conditions. Outbreak severity is often intensified by poor water quality, inadequate farm management, antibiotic misuse and pathogen vectors, leading to substantial economic losses. Timely and accurate diagnosis is therefore critical for effective disease management. This study investigates two convolutional neural network (CNN) architectures, EfficientNet and MobileNet. A curated and preprocessed dataset was used to fine-tune both models with a standardized custom classification head, ensuring a controlled backbone comparison. Experimental results show both architectures achieving over 95% accuracy, with MobileNet providing faster inference suitable for on-site deployment. These findings demonstrate the practical feasibility of lightweight CNN-based diagnostics tools for real-time, scalable, and cost-efficient health monitoring in shrimp aquaculture, bridging the gap between the laboratory-grade performance and field-level usability.