## 4 Applications of GenAI-Driven CV in Smart Agriculture
### Precision Agriculture and Field Management
Precision irrigation systems integrated with IoT networks and edge computing optimise water allocation based on real-time evapotranspiration, soil moisture, and meteorological data [177; 178; 179]. These systems employ predictive algorithms to dynamically adjust irrigation schedules, improving water-use efficiency without compromising yield. For example, vapour pressure deficit forecasting in semi-arid regions has enabled adaptive irrigation, reducing water consumption by 25% while maintaining crop health [177; 179]. Sprinkler systems embedded with CV modules further enhance precision by modulating spray patterns according to the canopy density and spatial distribution, achieving uniform water delivery across heterogeneous fields [178; 180]. _Plant Growth Tracking and Non-Invasive Phenotyping_. Non-invasive imaging methodologies, including smartphone-driven and camera-based systems, provide continuous insights into plant morphology and phenological stages [181; 182; 183; 184]. Recursive segmentation models, such as those developed for bok choy, enable real-time nutrient and irrigation adjustments in controlled environment agriculture (CEA), reducing labour costs while improving temporal resolution compared to manual assessments [181]. Hyperspectral imaging further augments phenotyping by quantifying biochemical indicators like chlorophyll degradation in grapes, bridging the gap between visual appearance and internal quality metrics [139]. _GenAI and Multimodal Data Fusion_. In precision agriculture, where collecting diverse, large-scale data from sources like hyperspectral sensors is costly, GenAI provides a critical advantage. GANs synthesise multispectral or normalised difference vegetation index (NDVI) images from RGB inputs or enhance existing multispectral satellite imagery via techniques like super-resolution, circumventing the cost and complexity of hyperspectral sensors [185; 186; 187]. For instance, GAN-based translations preserve spectral fidelity in synthetic datasets, enabling scalable vegetation monitoring with low-cost hardware [186]. Hybrid architectures combining transformers and GAN modules, such as Tranvolution Detection Networks, enhance disease diagnosis under limited data availability by leveraging style augmentation and transfer learning [188; 111]. Cloud-based platforms like Agroview centralise UAV-collected data, integrating deep learning pipelines with spatial analysis tools for comprehensive field assessments [161]. These systems fuse satellite, drone, and ground-level imagery to support multi-source data analysis, while IoT networks synchronise soil sensors and actuators for closed-loop control of irrigation and fertilisation systems [159; 189]. Challenges and Emerging SolutionsDespite advancements, domain generalisation remains constrained by environmental variability, including lighting fluctuations and soil heterogeneity [190; 165]. Semi-supervised frameworks, such as those applied to sunflower weed mapping via UAVs, mitigate these limitations by leveraging unlabelled data structures [174]. Explainability tools, including Grad-CAM and SHAP, are increasingly integrated to enhance user trust and regulatory compliance, particularly in high-stakes decision-making scenarios [191]. Efforts to democratise access include low-cost imaging platforms and open-source training pipelines, exemplified by smartphone-integrated CNNs for Zanthoxylum fruit quality assessment [155]. Future directions emphasise physics-informed synthetic data generation and VLMs, which offer actionable insights through natural language queries, bridging the gap between AI systems and agricultural stakeholders [114; 192]. In conclusion, the integration of CV and GAI has redefined precision agriculture, enabling data-driven resource optimisation and adaptive field management.