Vegetation phenology is the study of recurring plant life-cycle events such as leaf flush, photosynthesis, flowering, fruiting, and senescence. These processes are closely linked to wider ecosystem dynamics, influencing food availability, habitat quality, and seasonal patterns in wildlife behaviour. Phenological events are driven by environmental factors including temperature, precipitation, day length, insolation, humidity, and. Climate change is having a profound effect on vegetation phenology, and shifts in the timing of these events can lead to mismatches between plants and the wildlife species that depend on them. Monitoring phenology is therefore essential for understanding and managing ecosystems, wildlife populations, and forestry industries. A variety of remote sensing techniques are used to monitor vegetation phenology. Phenocams, static cameras positioned above the canopy, take repeated RGB images that allow researchers to track subtle changes in vegetation over time. These systems offer finer-scale monitoring than satellite imagery but can be limited by high upfront costs, significant power requirements, and the practical challenges of deploying them in remote or inaccessible locations. A substantial body of research exists on the use of digital repeat imagery for phenology monitoring, along with established frameworks for processing and interpreting such data. In recent years, several studies have explored the use of camera traps for monitoring vegetation phenology parameters such as green-up and leaf area. Camera traps are cheap, rugged sensors that have the advantage of existing widespread use across the world for wildlife ecology purposes. Although these studies have demonstrated potential for monitoring vegetation, most rely on manual image classification, which restricts their scalability. In addition, while camera traps are widely deployed for wildlife studies, their positioning is often optimised for animal detection rather than vegetation monitoring, which can limit their effectiveness for phenological analysis. Advances in computer vision, particularly deep learning (DL), offer promising opportunities to address these challenges. DL techniques excel in image classification, segmentation, object detection, and depth estimation. By applying these methods to camera trap imagery, it is possible to identify regions of interest and segment vegetation in ways that improve the accuracy and detail of phenological measurements. Beyond traditional time-series approaches, DL could enable a more comprehensive understanding of vegetation dynamics, including fine-scale spatial patterns and structural changes over time. This research aims to investigate the application of camera traps and deep learning methods to monitor vegetation phenology parameters across Europe, assessing their potential for scalable, low-cost, and high-resolution ecological monitoring.