## 4 Applications of GenAI-Driven CV in Smart Agriculture
### Agricultural Drones and Remote Sensing Image Analysis
\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** & **Methodology** & **Task** & **Approach** & **Outcome** \\ \hline \multirow{2}{*}{[188]} & \multirow{2}{*}{GANs + CNN} & \multirow{2}{*}{Disease dataset expansion} & \multirow{2}{*}{Image synthesis} & Enabled training with limited real data \\ \cline{1-1}  & & & & \\ \hline \multirow{2}{*}{[187]} & Satellite+CubeSat and Dual-GAN & \multirow{2}{*}{Vegetation mapping} & \multirow{2}{*}{Super-resolution} & High-res imagery for long-term analysis \\ \cline{1-1}  & & & & analysis \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._fication of typical crops [217]. Furthermore, the inversion of soil moisture in farmland areas can be achieved using SSA-CNN with multi-source remote sensing data [218]. Multimodal deep learning enables soybean phenotyping and winter wheat yield prediction [205; 219]. Hyperspectral imaging further supports species-level crop classification and biochemical analysis, such as chlorophyll degradation monitoring in grapes [139] and sugar accumulation in melons [220], with spectrally segmented-enhanced neural networks being developed for precise land cover object classification in such imagery [221]. Real-time anomaly detection systems, powered by spatiotemporal GANs, identify crop stress patterns and predict disease progression [110; 222], facilitating proactive interventions. _Autonomous Operations and Challenges_. UAVs increasingly support autonomous field operations, including navigation and obstacle avoidance. Autonomous operations rely on several key technologies. SLAM algorithms like YOLOD-SLAM2 enable precise localisation in orchards [223]. Lightweight algorithms such as YOLOv4-Tiny provide real-time plant detection, enhancing the UAV's ability to localise objects precisely [224]. Furthermore, collaborative drone networks improve the scalability of large-scale spraying and scouting missions [197]. However, operational challenges persist, including limited battery endurance, weather sensitivity, and computational constraints on edge devices. Lightweight models, such as YOLOv4-Tiny and pruned ResNet variants, mitigate these issues by balancing accuracy with resource efficiency [225; 226]. To further enhance model robustness, particularly when field data is sparse or difficult to obtain, GenAI is used to create synthetic datasets for tasks like seed maturity estimation or to perform image-to-image translation for mapping complex orchard environments [222; 227]. Emerging trends emphasise edge AI deployment and physics-informed synthetic data. Federated learning frameworks enable privacy-preserving model training across distributed farms [154], while neural rendering techniques simulate agro-environmental dynamics to enhance dataset realism [78; 87; 88]. Standardisation of sensor outputs and interoperability with farm management platforms remain critical for scalable adoption. Advances in miniaturised sensors and AI-driven compression will further democratise UAV technologies, particularly for smallholder farms [199; 228]. In summary, UAVs and remote sensing image analysis constitute a cornerstone of modern smart agriculture. By synergising high-resolution imaging, deep learning, and multi-source data fusion, these systems empower farmers with actionable insights, driving sustainable and efficient agricultural practices. Persistent challenges in hardware limitations and data processing underscore the need for continued innovation in lightweight algorithms and adaptive AI frameworks. Table 4 summarises the key methodologies, sensor types, and outcomes discussed in this section, providing a concise reference for UAV-based agricultural applications.