# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 4 AIoT Applications in Aquaculture
### Disease Detection and Classification  
The health and productivity of fish and shrimp populations in aquaculture rely heavily on effective disease detection and classification. Disease outbreaks threaten aquatic species and cause significant economic losses. Integrating artificial intelligence (AI), sensor technologies, and advanced data analysis techniques has led to significant progress in early disease detection and management. These innovations enhance the accuracy of disease identification, reduce reliance on manual inspections, and allow for real-time monitoring in aquaculture systems. Computer vision techniques have been widely used to identify visual symptoms of diseases in fish and shrimp. Machine learning algorithms, especially convolutional neural networks (CNNs), have accurately detected infections such as White Spot Syndrome Virus (WSSV). For example, an artificial neural network (ANN) trained with Canny edge detection and grey-level co-occurrence matrix (GLCM) features demonstrated a classification accuracy of 94.71% for distinguishing infected from healthy shrimp [12; 67; 114]. Additionally, support vector machines (SVMs) and random forest classifiers, combined with K-means clustering for segmentation, have successfully classified diseases like Epizotic Ulcerative Syndrome (EUS) and Tail and Fin Rot, with reported accuracies exceeding 88% [114]. These approaches highlight the potential of AI-driven models in disease diagnostics. However, challenges such as variations in image quality due to environmental conditions and the need for disease-specific models still require further research. Deep learning architectures incorporating attention mechanisms have improved disease classification by focusing on disease-specific features. ResNet50 combined with the convolutional block attention module (CBAM), has improved classification accuracy, achieving 89.9% in detecting fish diseases [115]. Similarly, models combining CNNs with online sequential extreme learning machines (OSELM) have improved feature extraction, demonstrating 94.28% accuracy in identifying infections even under variable water conditions [116]. Transfer learning approaches using pre-trained deep learning models, such as InceptionV3 and VGG16, have also been successfully applied to fish disease classification, with accuracy levels above 91% [117]. Water quality monitoring plays a crucial role in predicting disease risks in aquaculture. Deviations in parameters such as pH, dissolved oxygen, and ammonia concentrations often indicate stress conditions that increase susceptibility to infections. Gradient boosting models (GBMs) have been employed to predict disease outbreaks based on water quality variations, achieving an accuracy of 92% [118; 119]. Additionally, integrating water quality sensors with deep learning models, such as MobileNetV2, has enabled real-time disease prediction, with reported accuracy rates of 91.5% [119]. These models provide valuable insights into environmental factors contributing to disease outbreaks, allowing farmers to take preventive measures before infections spread. Cross-modal and zero-shot learning techniques have emerged as innovative approaches for disease detection, particularly when labelled image datasets are limited. These methods utilise textual descriptions from the scientific literature to map disease characteristics onto image features, facilitating the recognition of previously unseen diseases [120]. Similarly, transfer learning models trained on diverse datasets have been adapted for fish disease detection, demonstrating classification accuracies above 91% when using architectures such as VGG-16 and AlexNet [121]. These techniques help overcome data scarcity challenges and expand the applicability of AI-driven disease detection systems. Ensemble and hybrid models have improved disease classification accuracy by integrating several machine-learning approaches.