Abstract

Background and Aim: Aquaculture plays a crucial role in global food security; however, disease outbreaks remain a major constraint to sustainable production. Rapid and reliable detection of fish diseases is essential to reduce mortality, economic losses, and the misuse of antimicrobials in aquaculture systems. Conventional diagnostic approaches, such as clinical observation and bacterial culturing, are time-consuming, costly, and require specialized expertise. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have shown promise in automating image-based disease detection. This study aimed to compare a lightweight three-layer CNN model with pre-trained deep learning architectures (VGG16, InceptionV3, and ResNet50) for multi-class classification of infected freshwater fish species using a balanced image dataset collected from aquaculture farms in Thailand.

Materials and Methods: Images from clinically infected freshwater fish were collected during routine farm inspections across six provinces in Thailand. The dataset included 424 images of four species: Asian seabass (Lates calcarifer), red tilapia (Oreochromis sp.), snakeskin gourami (Trichopodus pectoralis), and snakeheads (Channa striata). After preprocessing, a balanced dataset of 56 images per class (totaling 224) was created. The dataset was divided into training (80%) and testing (20%) subsets. On-the-fly data augmentation techniques, such as rotation, brightness adjustment, flipping, shifting, shearing, and zooming, were applied to the training data to reduce overfitting. A lightweight, three-layer CNN model with stochastic gradient descent was used and compared with pre-trained architectures (VGG16, InceptionV3, and ResNet50). Model performance was assessed through accuracy, precision, recall, F1-score, confusion matrix analysis, and five-fold cross-validation.

Results: Among the evaluated models, InceptionV3 achieved the highest classification accuracy (56.82%), followed by VGG16 (43.18%) and the proposed CNN (38.64%), while ResNet50 performed poorly (25%). The InceptionV3 model also demonstrated higher average precision (63%), recall (57%), and F1-score (56.75%), indicating superior classification capabilities. Confusion matrix analysis revealed that InceptionV3 correctly classified 25 out of 44 test images, outperforming the proposed CNN (17 correct predictions) and VGG16 (19 correct predictions). Five-fold cross-validation further confirmed the stability and relatively better performance of the InceptionV3 model.

Conclusion: The comparative evaluation shows that pre-trained CNN architectures, especially InceptionV3, outperform a lightweight three-layer CNN when trained on small, balanced datasets of infected fish images. Although the proposed lightweight CNN has limited accuracy, its low computational needs suggest it could be useful in resource-limited aquaculture settings. Incorporating deep learning–based image analysis into aquaculture health monitoring systems could enable quick disease triage, support timely management decisions, and promote better biosecurity and sustainable fish production. Future research should increase the dataset size, include more fish species and disease types, and test model performance across different farming environments to improve its generalizability and practical use.

Keywords: aquaculture disease detection, convolutional neural network, deep learning, fish disease classification, image-based diagnosis, infected freshwater fish, InceptionV3, machine learning in aquaculture.