## 4 Applications of GenAI-Driven CV in Smart Agriculture
### Crop Health Monitoring and Pest Detection  
Traditional CV techniques have long been employed for crop health monitoring and pest detection, relying predominantly on handcrafted feature extraction and classical machine learning classifiers. Early methodologies, such as colour-based segmentation (often employing clustering algorithms like k-means) and statistical feature extraction, were used to identify pest-infested areas on plant leaves [92]. Similarly, thresholding and morphological operations have been applied to detect fungal infections on rice leaves [93]. However, these techniques are highly sensitive to illumination variance and background clutter, limiting their scalability in heterogeneous field environments. The reliance on manually engineered features also necessitates extensive labelled datasets for scenario-specific training, which is labour-intensive and impractical for large-scale agricultural applications. Building on these early techniques, the advent of deep learning, particularly convolutional neural networks (CNNs), has significantly advanced the field of crop health monitoring. CNNs, such as ResNet, VGGNet, Inception, and YOLO variants like YOLOv8 for detecting Phytophthorahora blight in chili [94] or, for instance, segmenting lotus seedpods [95], have demonstrated superior accuracy in diagnosing plant diseases from leaf images [96; 97; 98; 99], and CV systems have also been applied for specific conditions like Citrus Huanglongbing detection based on leaf characteristics [100]. Hybrid models that combine handcrafted features with CNNs have also been developed to enhance disease recognition in maize leaves [101], and transformer-embedded CNNs have shown promise for identifying field crop diseases [102]. Additionally, single 3D-CNN, such as 3D-ResNet, have been utilised for the early detection of specific plant diseases, like downy mildew in kimchi cabbage, leveraging hyperspectral imaging data [103]. Similarly, for cucumber downy mildew, a CNN-LSTM approach has been developed for disease prediction [104]. However, these deep learning models typically require vast labelled datasets for training, and their performance can degrade when exposed to unseen conditions or rare disease classes [105]. This limitation has spurred the need for more advanced techniques to address data scarcity and improve model generalisation. To address these challenges, GANs have emerged as a transformative solution to address data scarcity and class imbalance in agricultural datasets. As illustrated in Figure 4, GAN architectures, such as DCGAN and CycleGAN, have been used to generate realistic  
Figure 3: Conceptual framework of GenAI-driven CV for smart agriculture applications. leaf images with various disease symptoms, thereby enriching training datasets without the need for additional field collection [106; 107; 108; 109]. This synthetic augmentation has proven particularly effective for improving CNN performance on minority classes, such as detecting early-stage infections that are often underrepresented in real-world data [109]. Furthermore, GANs have shown potential in unsupervised anomaly detection, where models learn normal healthy patterns and flag deviations suggestive of disease or pest attacks [110; 111]. These advancements have led to improved model generalisation and enhanced recognition of minority classes. To combat the domain shift problem, generative models offer sophisticated adaptation techniques. For example, CycleGANs can translate visual characteristics between domains, such as applying disease patterns from one crop species to the healthy leaves of another, thereby improving a model's robustness and adaptability to new agricultural settings [112]. Spatiotemporal GANs even allow for modelling disease progression over time, synthesising future crop health states under various conditions [113; 114]. Modern crop health monitoring systems increasingly integrate multimodal data streams, including hyperspectral, thermal, and RGB imagery.