## 4 Applications of GenAI-Driven CV in Smart Agriculture
### Crop Health Monitoring and Pest Detection
Generative models now play a crucial role in translating between these modalities. For instance, conditional GANs have been used to generate synthetic thermal maps from RGB inputs, enriching the input space and compensating for sensor limitations, especially in resource-constrained environments [115; 116]. This multimodal fusion not only enhances the feature representation but also improves the system's resilience to sensor failures and environmental variability. However, aligning heterogeneous data modalities remains a complex task, requiring sophisticated techniques to ensure the consistency and accuracy of the fused data. Despite the significant advancements in GAN-augmented CV systems, challenges remain in terms of interpretability and real-time deployment. GAN training is known for its instability and mode collapse, and synthetic outputs may lack semantic consistency if not well-regularised [117]. To address these limitations, hybrid approaches combining GANs with explainable AI (XAI) frameworks have been proposed. To provide more transparency, techniques such as SHAP and LIME have been applied to CNN models that were trained on GAN-augmented data. These methods offer interpretable, feature-based explanations for the model's predictions, which helps agronomists and farmers understand the results  
Figure 4: Original and synthetic diseased tomato leaf images generated by C-GAN [109]. and gain actionable insights [118]. Additionally, edge AI accelerators [119] and quantised GAN architectures [120] have been developed to enable real-time processing on edge devices, reducing computational overhead and latency. However, ensuring that models are stable and that the synthetic data they generate is semantically consistent remains a critical challenge. Overcoming this challenge is key for these technologies to be widely adopted. Looking ahead, integrating foundation models that have been pretrained on large agricultural datasets holds great promise for the future of crop health monitoring. These models can facilitate few-shot adaptation, enabling systems to learn from limited data and generalise across diverse agricultural scenarios [121; 122]. The emergence of vision-language models (VLMs), such as GPT-Vision, further opens the door to pest advisory systems that operate through dialogue. In these systems, an AI can provide real-time recommendations based on visual inputs and contextual information [123; 124]. Efforts to democraise access to these technologies through open-source datasets and lightweight edge deployment solutions are also gaining momentum [125]. These advancements are expected to make AI-driven crop health monitoring more accessible and scalable, ultimately enhancing agricultural productivity and sustainability. In summary, while traditional CV methods laid the foundation for automated crop health monitoring, the integration of deep learning and GANs has significantly enhanced the capabilities of these systems. From improving dataset diversity to enhancing model generalisation and resilience, GANs have become indispensable tools in modern precision agriculture. However, ensuring model stability, interpretability, and real-time deployment efficiency remain critical for the sustainable adoption of these technologies. Future directions point towards foundation models and human-centric AI, promising to further democraise access and improve the robustness of crop health monitoring systems. A comparison of these techniques is presented in Table 1.