Arnica montana L. is a medicinal plant with significant conservation importance. It is crucial to monitor this species, ensuring its sustainable harvesting and management. The aim of this study is to develop a practical system that can effectively detect A. montana inflorescences utilizing unmanned aerial vehicles (UAVs) with RGB sensors (red–green–blue, visible light) to improve the monitoring of A. montana habitats during the harvest season. From a methodological point of view, a model was developed based on a convolutional neural network (CNN) ResNet101 architecture. The trained model offers quantitative and qualitative assessments of A. montana inflorescences detected in semi-natural grasslands using low-resolution imagery, with a correctable error rate. The developed prototype is applicable in monitoring a larger area in a short time by flying at a higher altitude, implicitly capturing lower-resolution images. Despite the challenges posed by shadow effects, fluctuating ground sampling distance (GSD), and overlapping vegetation, this approach revealed encouraging outcomes, particularly when the GSD value was less than 0.45 cm. This research highlights the importance of low-resolution image clarity, on the training data by the phenophase, and of the need for training across different photoperiods to enhance model flexibility. This innovative approach provides guidelines for mission planning in support of reaching sustainable management goals. The robustness of the model can be attributed to the fact that it has been trained with real-world imagery of semi-natural grassland, making it practical for fieldwork with accessible portable devices. This study confirms the potential of ResNet CNN models to transfer learning to new plant communities, contributing to the broader effort of using high-resolution RGB sensors, UAVs, and machine-learning technologies for sustainable management and biodiversity conservation.

Traditional farming systems still exist South-Eastern Europe, especially in Romania. These systems integrate agriculture, animal keeping, and grassland management. Since 2007, when Romania became member of the European Union, large structural changes of the economic and societal system were set off. On the landscape level, fertile and profitable land is subject to increasing intensification and specialization of production. Less productive or remote sites face abandonment and subsequent natural succession. Especially in rural regions receiving less allocations of direct payments for rural development, territorial disparities become apparent, leading to emigration from remote and mountainous rural regions. However, oligotrophic grassland in rural areas is extremely diverse and can be classified as high nature value (HNV) grassland, covering an area of about 2 million ha. Management of this grassland is essential for the conservation of cultural landscapes and their biodiversity. A successful example of integrating agricultural use of mountain oligotrophic grassland with the collection of medicinal plants, and specifically Arnica montana, can be found in the Apuseni Mountains in Romania. The model shows how incentives and capacities can be created for the conservation of species-rich, traditionally managed habitats and landscapes by sustainable production and trade of the Arnica montana flowers, adding economic value through local processing. Principles and lessons learnt from this case study can be transferred to other regions, other sites or other countries.