The accelerating impacts of climate change continue to challenge global agriculture, increasing the need for adaptive and sustainable technological solutions. In this context, Unmanned Aerial Vehicles (UAVs) have evolved from experimental tools to key components of precision agriculture, offering advanced capabilities for monitoring, diagnosis, and targeted intervention.

This Research Topic explores how UAVs, combined with remote sensing, AI, and operational strategies, can advance climate-smart agriculture. The topic focuses on innovative technologies and UAV applications in precision agriculture for mitigating climate change, highlighting recent advancements.

The four contributions in this Research Topic illustrate UAVs’ potential to improve resource-use efficiency, enhance plant protection, enable early detection of biotic stress, and support scalable, data-driven decision-making. Together, these studies underline both the versatility of UAV technologies and the need for continued innovation to address environmental variability, operational challenges, and integration with complementary sensing platforms.

Regarding UAV for spraying, the operational variability and deposition efficiency, two studies offer detailed insights into UAV spraying systems, an increasingly relevant application as climate change alters pest and disease dynamics. Byers et al. evaluated spray behavior in two commercial UAAS (TTA M4E and DJI Agras T30) under varying nozzle types, flight speeds, and heights. They found that spray deposition was highly variable across the swath, with most coverage beneath the flight path. Finer droplets improved lateral distribution, yet acceptable uniformity (CV ≤ 25%) only occurred under a limited set of operational settings, generally at specific combinations of speed and height and for narrower swaths than those suggested by manufacturers. These findings highlight the need for refined calibration and platform-specific operational guidelines to improve precision and environmental safety. Complementarily, Li et al. analyzed optimal spraying parameters for two UAV models (T20P and T40) across three tobacco growth stages. Using a multi-index weighted approach, they identified height-speed-volume combinations that maximized deposition and uniformity. A spray volume of 50 L·ha^-¹ consistently improved performance, while optimal heights and speeds varied with crop stage and UAV. Their results underscore the importance of adaptive guidelines that consider crop structure and canopy density, moving beyond generic parameter recommendations. Such optimized protocols can enhance pesticide efficiency, reduce waste, and support climate-smart plant protection.

With regards to UAVs and remote sensing for disease detection and crop health monitoring, climate change is intensifying plant disease pressures, making early detection essential for resilient farming systems. Duncan et al. demonstrated how UAV and satellite imagery can monitor Phytophthora cinnamomi in avocado orchards at multiple spatial scales. By combining UAV RGB and multispectral data with high- and medium-resolution satellite imagery, they evaluated several vegetation indices (VIs) and canopy metrics. UAV multispectral imagery achieved the strongest correlations with disease severity (R² up to 0.80). Canopy management practices influenced detection accuracy, revealing how disease expression and remote sensing signatures are shaped by orchard structure. While satellite imagery was valuable for broader assessments, UAV data provided higher sensitivity. These findings reinforce UAVs as precise and scalable tools for detecting early physiological changes, offering a more objective alternative to traditional visual assessments.

Finally, integrating UAVs, satellite remote sensing, and machine learning has shown benefits. The review by Xing et al. highlights the synergy between UAVs, satellite remote sensing, and machine learning to generate scalable, climate-smart agricultural insights. Combining diverse imagery with convolutional neural networks (CNNs) and random forests (RFs) can enhance decision-making. This is applicable to yield prediction, irrigation, nutrient optimization, and disease detection. Case studies show large reductions in resource use (for example, irrigation savings of 20–25% and significant decreases in nitrogen application) without compromising yields. High-accuracy disease detection systems (>95% accuracy) prove the potential of AI to enhance crop resilience. The review also notes challenges, including data processing demands, computational requirements, and limited accessibility for smallholder farmers, emphasizing the need for supportive policies, open data, and affordable technologies.

Taken together, the contributions to this Research Topic provide a comprehensive view of how UAVs can transform precision agriculture through improved spraying efficiency, enhanced disease monitoring, and intelligent data integration. Common themes emerging from the studies include i) the need for context-specific operational guidelines tailored to crop type, growth stage, and equipment specifications, ii) the value of multi-scale sensing, combining UAV and satellite imagery to overcome the limitations of each platform, iii) the increasing relevance of AI and machine learning in translating raw data into actionable agronomic insights and iv) the importance of understanding environmental interactions, such as wind conditions, canopy structure, and phenological stage, which modulate sensor performance and spray behavior.

Looking ahead, future research should aim to expand the capabilities of UAV systems beyond the current state of practice. Development of autonomous, closed-loop spraying systems is promising. These systems can modify flight paths and droplet characteristics in real-time. They use onboard sensors to detect canopy structure, wind, and pest or disease hotspots. Advances in edge computing may allow UAVs to process multispectral or hyperspectral imagery directly on-board, enabling immediate, in-field agronomic decisions without relying on remote servers. Integrating UAVs into farm-level digital ecosystems (including IoT soil sensors, automated irrigation infrastructure, and climate forecasting models) will support predictive, adaptive management strategies. These strategies anticipate crop stress before it develops. Swarm UAV technologies offer another promising avenue. These involve coordinated fleets of small drones that perform complex tasks. Examples include synchronized spraying, multi-angle canopy imaging, or rapid disease surveillance over large areas. Strengthening accessibility through low-cost sensors, open-source analytical tools, and training initiatives will be essential to make sure that the benefits of UAV technologies reach smallholder farmers, who are among the most vulnerable to climate-related production risks.

This Research Topic highlights the transformative potential of UAV-based technologies to support climate-smart agriculture. We hope that the insights presented here will inspire further innovation and collaboration across engineering, agriculture, and data science, paving the way for a more resilient agricultural future.