Basavegowda, Deepak H.Höhne, Marina M.-C.Weltzien, Cornelia2024-04-082024-04-082024978-3-88579-738-82944-7682https://dl.gi.de/handle/20.500.12116/43872Eco-scheme 5 has been introduced to promote biodiversity in permanent grasslands through sustainable land management. While this scheme motivates farmers through result-based remuneration, it also entails a significant monitoring cost in terms of time and money to identify indicators manually. To overcome this burden and facilitate the realization of Eco-scheme 5, we developed an object detection model based on Deep Learning (DL) to automate the indicator species identification. First, we trained and evaluated the model on high-resolution Unmanned Aerial Vehicle (UAV) data. The model achieved an Average Precision (AP) rate of 80.8 AP50, but limited training data and the class imbalance problem among indicators affected the model performance. To address these problems, we enriched training data with proximal images of indicators, resulting in a performance gain from 80.8 AP50 to 95.3 AP50. Our results demonstrate the potential of DL and UAV applications in assisting result-based agri-environmental schemes (AES) such as Eco-scheme 5.enbiodiversity monitoringresult-based AESobject detectioncross-domain knowledge transferText/Conference Paper10.18420/giljt2024_291617-5468