###### Abstract  
The integration of computer vision (CV) and generative artificial intelligence (GenAI) into smart agriculture has revolutionised traditional farming practices by enabling real-time monitoring, automation, and data-driven decision-making. This review systematically examines the applications of CV in key agricultural domains, such as crop health monitoring, precision farming, harvesting automation, and livestock management, while highlighting the transformative role of GenAI in addressing data scarcity and enhancing model robustness. Advanced techniques, including convolutional neural networks (CNNs), YOLO variants, and transformer-based architectures, are analysed for their effectiveness in tasks like pest detection, fruit maturity classification, and field management. The survey reveals that generative models, such as generative adversarial networks (GANs) and diffusion models, significantly improve dataset diversity and model generalisation, particularly in low-resource scenarios. However, challenges persist, including environmental variability, edge deployment limitations, and the need for interpretable systems. Emerging trends, such as vision-language models and federated learning, offer promising avenues for future research. The study concludes that the synergy of CV and GenAI holds immense potential for advancing smart agriculture, though scalable, adaptive, and trustworthy solutions remain critical for widespread adoption. This comprehensive analysis provides valuable insights for researchers and practitioners aiming to harness AI-driven innovations in agricultural ecosystems. smart agriculture; computer vision; generative artificial intelligence; deep learning 2020 IEEEexample: accepted: date 10.1111/j.ccs.2020.11.1111/j.ccs.2020.1111/j.ccs.2020.1111/j.ccs.2020.1111/j.ccs.2020.1111/j.ccs.2020.1111/j.ccs.2020.111/j.ccs.2020.111/j.ccs.20  
Expanding on these technological foundations, smart agriculture increasingly depends on the integration of CV with autonomous systems and distributed computing frameworks [1]. This convergence now supports navigation systems in unmanned ground vehicles through stereo-vision mapping [22; 23; 24], enables drone-based crop surveillance via hyperspectral imaging [25; 26; 27], and facilitates real-time decision-making through Internet of Things (IoT)-based sensor fusion [28; 29; 30]. These technological integrations collectively establish an interconnected agricultural ecosystem where edge computing platforms process visual data from distributed sensors to coordinate automated machinery responses [31]. This closed-loop system effectively bridges the gap between data acquisition and field interventions, enabling more responsive and precise agricultural operations [32]. However, this technological progression also exposes critical limitations when deployed across diverse agricultural contexts [33]. Operational challenges arise from environmental variability, including fluctuating illumination and occluded crop structures, which compromise model generalisation [34]. Infrastructure constraints further exacerbate these technical limitations, particularly in resource-constrained settings lacking the computational capacity required for complex vision algorithms [35]. The fundamental bottleneck, however, remains data scarcity [36; 37; 38]. The prohibitive cost of acquiring labelled agricultural datasets across numerous crop phenotypes and growth stages restricts system scalability, ultimately limiting the practical deployment reliability of these technologies [39]. The recent generative artificial intelligence (GenAI) has emerged as a transformative approach to resolving long-standing challenges in agricultural technology through advanced synthetic data generation and adaptive model optimisation techniques [40]. Current implementations leverage diffusion models [41] and generative adversarial networks (GANs) [42] to synthesise photorealistic agricultural imagery encompassing rare crop phenotypes and extreme meteorological conditions [43; 44; 45].