## 4 Applications of GenAI-Driven CV in Smart Agriculture
### Livestock and Aquaculture Monitoring
#### 4.5.2 Aquaculture Monitoring  
Aquaculture presents unique challenges for computer vision systems, primarily due to the significant domain gap across different imaging environments, as illustrated in Figure 5. Real-world underwater images are often degraded by lighting variations, water turbidity, and occlusions, starkly contrasting with clearer laboratory or Internet-sourced images. This discrepancy can cause models to overfit to clean data and fail to generalise to operational conditions. To address these issues, [PERSON] et al. [239] refined the YOLOv5 model for fish detection tasks in aquatic environments, achieving a 1.6% improvement in precision over the original model and reaching an accuracy of 95.7%. Moreover, [PERSON] et al. [240] developed a two-mode AIoT-based smart sensor system that combines CV with environmental monitoring for precision aquaculture. [PERSON] et al. [241] compared YOLOv8 and YOLO-NAS models for underwater fish detection, demonstrating the superior performance \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Application** & **Methodology Used** & **Sensor Type** & **Outcomes** \\ \hline
[223,224] & Target Localisation & YOLOv4-Tiny, YOLO-SLAM2 & RGB + Depth & Accurate target identification and positioning \\ \hline
[213,214,221] & Land Cover Mapping & Self-attention 3D CNN, SJANet & Hyperspectral & Fine-resolution land and vegetation type segmentation \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._of YOLOv8 even in visually noisy conditions. Tackling the complexity of underwater environments, [PERSON] et al. [242] designed Composited FishNet, a framework that utilises a novel composite backbone and an enhanced path aggregation network to enable fish recognition even in challenging conditions with poor lighting and turbidity. Understanding fish behaviour is critical for stress detection and disease prediction. [PERSON] et al. [243] analysed the spatial behavioural characteristics of fish schools to identify special behaviours, while [PERSON] et al. [244] presented a two-stage framework for recognising fish feeding behaviour, supporting optimised feeding schedules and waste reduction. [PERSON] et al. [245] proposed DMDnet, a cross-domain model capable of robust detection across species and environments. Advanced image processing techniques are also vital for aquaculture monitoring. [PERSON] et al. [246] introduced the UICE-MIRNet framework for underwater image enhancement, improving the detection of submerged objects and the accuracy of downstream CV tasks. [PERSON] et al. [247] employed VIS-NIR hyperspectral imaging for pelagic fish characterisation, offering new possibilities for non-destructive species classification. [PERSON] et al. [248] utilised SE-ResNet152 and transfer learning to accurately identify unbalanced fish datasets. Health monitoring is a critical application in aquaculture. [PERSON] et al. [249] reviewed emerging technologies in aquatic animal health, emphasising the role of CV in early disease detection. [PERSON] et al. [250] proposed a lightweight model for detecting dead fish in recirculating systems, while [PERSON] et al. [251] addressed hypoxia detection through multi-target fish tracking to infer oxygen stress levels. [PERSON] et al. [252] explored the real-time identification of uneaten feed pellets, enabling optimised feeding and waste reduction. Feeding behaviour is an essential metric for health and growth performance. [PERSON] et al. [253] demonstrated how mouth-opening frequency in salmon could be detected using video data, offering an indirect measure of feeding readiness. [PERSON] et al. [254] introduced SED-RCNN-BE, a binocular depth estimation model for automatic fish size measurement, aiding in growth tracking and harvest planning. [PERSON] et al. [255] showed the potential of multispectral imaging for real-time meat quality evaluation. Cross-species and complex environments require models with strong generalisation abilities. [PERSON] et al.