## 4 Applications of GenAI-Driven CV in Smart Agriculture
### Agricultural Product Quality Control and Food Safety  
Ensuring the quality and safety of agricultural products is critical in the smart agriculture paradigm. The integration of CV and GenAI has revolutionised quality control, enabling real-time detection of defects, contamination, and freshness issues. This advancement enhances consumer safety and regulatory compliance while improving efficiency and scalability. _Deep Learning in Quality Monitoring_. Deep learning models trained on hyperspectral, RGB, and NIR images have become central to detecting quality indicators in fresh produce. Hyperspectral imaging combined with deep learning excels in early bruise detection on apples, offering a non-invasive alternative to conventional methods [263]. Similarly, CNNs can monitor damage caused by the oxidation of lipids and proteins in pork that has been frozen and thawed, doing so with high sensitivity and specificity [264]. Multi-task learning \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Task Type** & **Method** & **Key Contribution** \\ \hline \multirow{2}{*}{[229]} & \multirow{2}{*}{Behaviour recognition} & \multirow{2}{*}{Pose estimation + deep learning} & Demonstrated fine-grained motion analysis in wild animals \\ \cline{3-4}  & & & \\ \hline \multirow{2}{*}{[235]} & \multirow{2}{*}{Social behaviour detection} & \multirow{2}{*}{2D CNN + data augmentation} & \multirow{2}{*}{Non-invasive contact behaviour recognition} \\ \cline{3-4}  & & & \\ \hline \multirow{2}{*}{[234]} & \multirow{2}{*}{Activity detection} & \multirow{2}{*}{YOLO + efficient aggregation} & Improved real-time behaviour recognition in commercial barns \\ \hline \multirow{2}{*}{[236]} & \multirow{2}{*}{Behaviour recognition/tracking} & \multirow{2}{*}{PrunedYOLO + Kalman filter} & Lightweight model for multi-cow tracking and action classification \\ \cline{3-4}  & & & \\ \hline \multirow{2}{*}{[238]} & \multirow{2}{*}{Weight estimation} & \multirow{2}{*}{RGB-D camera + depth regression} & Portable real-time pig weight prediction system \\ \hline \multirow{2}{*}{[242]} & \multirow{2}{*}{Species recognition} & \multirow{2}{*}{FishNet + low-light enhancement} & Robust fish classification from low-quality videos \\ \cline{3-4}  & & & \\ \hline \multirow{2}{*}{[241]} & \multirow{2}{*}{Object detection} & \multirow{2}{*}{YOLOv8, YOLO-NAS} & Model comparison for fish detection under murky conditions \\ \cline{3-4}  & & & \\ \hline \multirow{2}{*}{[244]} & \multirow{2}{*}{Feeding behaviour recognition} & \multirow{2}{*}{Two-stage framework + temporal modelling} & Automated recognition of appetite-driven behaviour \\ \hline \multirow{2}{*}{[246]} & \multirow{2}{*}{Image enhancement} & \multirow{2}{*}{UICE-MIRNet} & Improved image clarity for better detection downstream \\ \hline \multirow{2}{*}{[254]} & \multirow{2}{*}{Size estimation} & \multirow{2}{*}{SED-RCNN-BE (binocular depth)} & Accurate growth estimation using stereo vision \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of representative studies on CV applications in livestock and aquaculture monitoring. further enhances these models by allowing simultaneous monitoring of multiple quality parameters, reducing computational costs and improving generalisation. Addressing Data Scarcity with Generative Models.Acquiring sufficient data for every possible product defect or contamination type is a major hurdle in quality control. Generative models offer a solution by creating synthetic data to augment sparse datasets. Synthetic data from these models have improved classifier performance in food safety tasks. For instance, in detecting pesticide residues in Hami melons, augmenting hyperspectral data with a Regression Generative Adversarial Network (RGAN) improved a Support Vector Regression (SVR) model's predictive performance, achieving a coefficient of determination (\(R_{p}^{2}\)) of 0.8781 and a Ratio of Prediction to Deviation (RPD) of 2.7882 [265].