## 4 Applications of GenAI-Driven CV in Smart Agriculture
### Precision Agriculture and Field Management  
Precision agriculture has revolutionised traditional farming by integrating sensor-based technologies, data analytics, and intelligent decision-making systems to optimise \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Methodology** & **Core Technology** & **Typical Applications** \\ \hline
[126, 127] & Classical CV & Colour thresholding, texture analysis, visual saliency, morphology & Citrus ripeness, coconut maturity assessment \\ \hline
[128, 129, 132, 133, 134, 135] & Deep Learning & CNNs, YOLO variants & Fruit detection (apple, strawberry, tomato), harvester feed prediction \\ \hline
[139, 140, 141] & Multimodal Sensing & Hyperspectral, thermal, and RGB data fusion; hyperspectral imaging & Grape/melon biochemical maturity (chlorophyll, sugar); robust sensing \\ \hline
[136] & Transformer-based Models & VFMs, transformers, attention & Overlapping fruit resolution, occlusion handling in dense clusters \\ \hline
[143, 144, 145] & Generative AI & Diffusion models, GANs & Annotated image generation, synthetic data augmentation, occluded berry reconstruction, domain adaptation \\ \hline
[146, 148, 149, 150, 152] & Robotic Harvesting & 3D vision, LiDAR, RL, O2 RNet, soft grippers, finger vision & Autonomous harvesting (apple, cucumber), selective picking, orchard automation \\ \hline
[123] & VLMs & Cross-modal VLMs, Conversational Agents (e.g., AgroGPT) & Image-based agronomic advice via NLP, field decision support \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of approaches for fruit and vegetable maturity detection and harvesting automation. agricultural inputs, enhance productivity, and minimise environmental impact [159; 160; 161]. At its core, precision agriculture leverages spatial and temporal variability analysis to enable site-specific management decisions, a paradigm significantly advanced by the convergence of CV and GenAL. This section examines the technological advancements, applications, and challenges in modern field management systems. _UAV-Based Monitoring and Weed Management_. Unmanned aerial vehicles (UAVs) equipped with multispectral, hyperspectral, and RGB sensors have become indispensable tools for large-scale agricultural monitoring. These platforms facilitate high-resolution data acquisition, enabling crop stress detection, weed infestation mapping, and nutrient deficiency analysis [162; 163; 164]. For instance, hyperspectral imaging has proven effective in identifying barnyard grass in paddy fields, enabling spatially targeted herbicide applications [163]. UAV-derived aerial perspectives further enhance canopy structure mapping and yield prediction by correlating spectral signatures with crop health metrics [160; 162]. Weed detection remains a critical challenge due to inter-class similarities, occlusions, and environmental variability. Deep learning architectures, particularly CNNs and GANs, have addressed these limitations through robust feature extraction and synthetic data augmentation [165; 166; 167; 168]. Recent models, such as the YOLOv8-based YOLO-CWD and YOLOv10n variants [167; 169], improved YOLOv5 networks like HAD-YOLO [170] and STBNA-YOLOv5 for rapeseed fields [171], and approaches using improved YOLOv4 for UAV imagery [172] demonstrate superior performance in distinguishing crops from weeds under cluttered field conditions, achieving accuracies above 90% in real-time analysis. Semi-supervised learning frameworks further reduce dependency on manual labelling, which is labour-intensive, while improving generalisation across diverse agroecological contexts [173; 174]. Innovations like region-based CNNs enable precise monocot/dicot weed classification in dense crop fields, forming the basis for autonomous precision sprayers that reduce herbicide usage by 30-40% [166; 175; 176]. _Resource Optimisation and Irrigation Systems_.