## 3 An Overview of GenAI-Driven CV Frameworks
### A Paradigm Shift from Traditional CV to Generative AI  
GenAI refers to advanced computational frameworks capable of autonomously synthesising high-fidelity data--such as images, videos, or 3D representations--through self-supervised learning paradigms [72]. Core methodologies, including diffusion models [41], GANs [42], and transformer-based architectures [73], are characterised by their ability to learn latent data distributions and generate contextually coherent outputs. Their key strengths lie in their scalability, annotation efficiency, and capacity to model complex, non-linear relationships within data, often augmented by large-scale pretraining on multimodal datasets [74]. Recent advancements have propelled GenAI beyond theoretical frameworks, enabling applications in synthetic dataset generation [75], domain adaptation [76], and physics-informed neural rendering, which incorporates physical laws into neural networks to create photorealistic and consistent 3D representations of objects and scenes [77]. These breakthroughs solidify its role as a cornerstone of next-generation AI solutions.  
These capabilities enable GenAI to overcome the fundamental limitations of traditional CV systems by addressing two critical bottlenecks: data scarcity and domain generalisation.  
Conventional CV systems, dependent on handcrafted feature engineering and scenario-specific labelled datasets, are not robust to changing environmental conditions (such as different lighting or weather), and the cost to annotate data for them is unsustainably high [34]. GenAI circumvents these issues through photorealistic agricultural image synthesis, spanning diverse crop phenotypes [78; 79], growth stages [80; 81], and meteorological extremes [82], leveraging conditional diffusion models [41]. Synthetic datasets emulating rare disease manifestations or occluded plant structures, for instance, enhance model generalisability without resource-intensive field data collection [83; 84].  
Style-transfer mechanisms help maintain consistent annotations between synthetic and real-world datasets by transforming the visual characteristics of synthetic images to more closely match those found in real-world scenarios. This reduces the domain gap and ensures that models trained on synthetic data generalise better when applied to real-world agricultural tasks [85]. Meanwhile, latent space interpolation refers to the process of blending representations within the neural network's latent space--essentially, the abstract feature space learned during training--which allows for smooth adaptation between different domains. For example, by interpolating between the features of datasets from different regions, models can be fine-tuned to specific soil compositions or local microclimates, thereby improving cross-domain adaptability [86].  
Furthermore, integrating physics-aware neural rendering with agro-environmental simulations enables GenAI to generate training data that remains robust even under conditions of occlusion--such as when crops are partially hidden by machinery or foliage--a challenge that conventional computer vision pipelines struggle to address [78; 87; 88]. These innovations not only reduce the dependence on labour-intensive manual annotation but also enhance computational efficiency by leveraging knowledge distillation techniques. This approach compresses large-scale models into lightweight versions suitable for deployment on edge devices, making advanced AI solutions more accessible in resource-constrained agricultural settings.