Practical Considerations and Challenges for Adaptation and Transferability of UAV and AI-based Precision Viticulture

In recent years, automation and optimization in the domain of Precision Viticulture (PV) have motivated extensive research on AI-based UAV-supported methods for tasks such as yield estimation, disease detection, field monitoring, and spraying. The literature includes evidence from simulation and real-world deployments that demonstrate the benefits of PV solutions. However, the transferability across sites and generalization of the proposed solutions still remain open challenges. Among others, even though numerous public datasets are available and methods have been identified to address key problems, PV systems are usually targeted at a single deployment. While existing review papers primarily focus on technological developments, application scenarios, and methodological advances in PV, UAV systems, and AI, comparatively less attention has been devoted to the practical factors affecting the transferability of these solutions across different vineyard environments. To address this gap, this paper adopts a deployment-oriented perspective, examining the key factors and considerations that influence the transferability and generalization of AI-based UAV solutions in PV. Rather than reviewing individual methods solely in terms of their performance, the contribution focuses on identifying practical challenges and considerations relevant to assessing and improving their applicability across different deployment scenarios.