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Auflistung nach Schlagwort "biodiversity monitoring"

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  • Konferenzbeitrag
    Deep Learning-based UAV-assisted grassland monitoring to facilitate Eco-scheme 5 realization
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Basavegowda, Deepak H.; Höhne, Marina M.-C.; Weltzien, Cornelia
    Eco-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.
  • Konferenzbeitrag
    The FAIR-Device – an AI image recognition-based non-lethal and generalist monitoring system for insect biodiversity in agriculture
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Chiavassa, Juan A.; Kraft, Martin; Noack, Patrick; Walther, Simon; Kirse, Ameli; Scherber, Christoph
    Agriculture is influenced by pest insects, but also has a considerable impact on general insect biodiversity. Insect field monitoring is essential for understanding their abundance, diversity, and dynamics in ecosystems, including pest distribution, control measures, and prediction of pest outbreaks. However, traditional monitoring systems can present difficulties leading to a limited temporal and spatial resolution of the information obtained. To resolve these limitations, automatic insect monitoring traps have been developed. However, most of them address only agricultural pests and are not suitable for monitoring a generalist population of insects. This limits their use for assessing the respective impact of different crop management practices. The Field Automatic Insect Recognition (FAIR)-Device is a novel generalist field device that provides high-resolution data for evaluating insect diversity. Proof of concept tests demonstrated the potential of the FAIR-Device as a low-cost, non-lethal tool for insect monitoring.