## 4 Applications of GenAI-Driven CV in Smart Agriculture
### Agricultural Drones and Remote Sensing Image Analysis  
The integration of unmanned aerial vehicles (UAVs) and advanced remote sensing technologies has redefined precision agriculture by enabling high-resolution, real-time monitoring of crops, soil, and environmental conditions [195; 196; 197]. UAV platforms equipped with multispectral, hyperspectral, thermal, and RGB sensors provide unparalleled flexibility in data acquisition, supporting critical tasks such as yield estimation, disease detection, and resource management. Unlike satellite systems constrained by revisit intervals and cloud cover, drones offer on-demand imaging under customisable flight plans, making them indispensable for dynamic crop surveillance [198; 199]. _Sensor Integration and Data Acquisition_. Hyperspectral and multispectral imaging dominate UAV-based systems due to their capacity to capture subtle crop stress signals. For instance, hyperspectral sensors enable the early detection of diseases by analysing spectral anomalies, such as identifying citrus gummosis [200; 201] or tea leaf blight, even with lower-resolution UAV imagery [202]. In parallel, multispectral data facilitate fine-scale phenotyping, which includes tasks like seedling density estimation and canopy cover analysis [203; 204; 205; 206]. Recent advancements in lightweight LiDAR and thermal cameras further enhance UAV capabilities, enabling 3D crop modelling and heat stress assessment [207; 208]. These multimodal datasets form the foundation for AI-driven decision-making, bridging gaps between aerial observations and ground-level agricultural processes. _Deep Learning for Image Analysis._ Deep learning architectures, particularly CNNs and transformer-based models, have significantly improved the accuracy of UAV image processing. Hybrid frameworks like CCTNet, which integrates CNN and transformer modules, achieve superior crop segmentation across diverse lighting and growth conditions [209]. For disease detection, GAN-based models such as DSCONV-GAN enhance feature alignment in temporally varying datasets [201], and other machine learning approaches also leverage GANs with UAV technology in two-step processes for crop disease detection [210]. Attention-augmented networks like HSI-TransUNet improve the robustness of hyperspectral classification [211]. Further advancements in precise crop classification using hyperspectral images include methods like multi-branch feature fusion with dilation-based MLPs [212], spectral-spatial-scale attention networks such as S3 ANet for precise end-to-end crop classification based on UAV-borne hyperspectral imagery [213], and self-attention 3D convolutional neural network approaches for enhancing precision agriculture and land cover classification from hyperspectral data [214]. Semi-supervised learning frameworks address labelling bottlenecks by leveraging unlabelled UAV imagery, as demonstrated in sunflower weed mapping [174]. YOLO variants, optimised for real-time performance, excel in object detection tasks such as weed identification and boundary mapping [215]. _Multi-Source Data Fusion and Applications._ The fusion of UAV data with satellite imagery and ground sensors enhances model generalisability. For example, combining Sentinel-1/2 data with UAV-derived imagery improves land cover classification accuracy through DC-CNN architectures [216], and deep learning-based methods using dual-polarimetric synthetic aperture radar and high-resolution optical images are also employed for the identi