TY - CHAP A1 - Basavegowda, Deepak Hanike A1 - Mosebach, Paul A1 - Schleip, Inga A1 - Weltzien, Cornelia T1 - Indicator plant species detection in grassland using EfficientDet object detector T2 - 42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft N2 - Extensively used grasslands (meadows and pastures) are ecologically valuable areas in the agricultural landscape and part of the multifunctional agriculture. In Germany, the quality of these grasslands is assessed based on the occurrence of certain plant species known as indicator or character species, with indicators being defined at regional level. Therefore, the recognition of these indicators on a spatial level is a prerequisite for monitoring grassland biodiversity. The identification of indicator species for the status quo of grassland using traditional methods was found to be challenging and tedious. Deep learning-algorithms applied to high-resolution UAV imagery could be the key solution, where UAV with remote sensors can map a large area of grassland in comparison to manual or ground mapping methods and deep learning-algorithms can automate the detection process. In this research work, we use an EfficientDet based algorithm to train an object detection model capable of recognizing indicators on RGB data. The experimental results show that this approach is very promising in contrast to the difficult and time-consuming manual recognition methods. The model was trained with the momentum-SGD optimizer with a momentum value of 0.9 and a learning rate of 0.0001. The model was trained and tested on 1200 images and achieves 45.7 AP (and 85.7 AP50) on test data set. The dataset includes images of four distinct indicator plant species: Armeria maritima, Campanula patula, Cirsium oleraceum, and Daucus carota KW - digital agriculture; biodiversity in grassland; HNV farming; deep learning; object detection Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:eb1-opus-6656 SN - 978-3-88579-711-1 SN - 1617-5468 SP - 57 EP - 62 PB - Gesellschaft für Informatik e.V. CY - Bonn ER - TY - JOUR A1 - Mouratiadou, Ioanna A1 - Lemke, Nahleen A1 - Chen, Cheng A1 - Wartenberg, Ariani A1 - Bloch, Ralf A1 - Donat, Marco A1 - Gaiser, Thomas A1 - Hanike Basavegowda, Deepak A1 - Helming, Katharina A1 - Ali Hosseini Yekani, Seyed A1 - Krull, Marcos A1 - Lingemann, Kai A1 - Macpherson, Joseph A1 - Melzer, Marvin A1 - Nendel, Claas A1 - Piorr, Annette A1 - Shaaban, Mostafa A1 - Zander, Peter A1 - Weltzien, Cornelia A1 - Bellingrath-Kimura, Sonoko Dorothea T1 - The Digital Agricultural Knowledge and Information System (DAKIS): Employing digitalisation to encourage diversified and multifunctional agricultural systems JF - Environmental Science and Ecotechnology N2 - Multifunctional and diversified agriculture can address diverging pressures and demands by simultaneously enhancing productivity, biodiversity, and the provision of ecosystem services. The use of digital technologies can support this by designing and managing resource-efficient and context-specific agricultural systems. We present the Digital Agricultural Knowledge and Information System (DAKIS) to demonstrate an approach that employs digital technologies to enable decision-making towards diversified and sustainable agriculture. To develop the DAKIS, we specified, together with stakeholders, requirements for a knowledge-based decision-support tool and reviewed the literature to identify limitations in the current generation of tools. The results of the review point towards recurring challenges regarding the consideration of ecosystem services and biodiversity, the capacity to foster communication and cooperation between farmers and other actors, and the ability to link multiple spatiotemporal scales and sustainability levels. To overcome these challenges, the DAKIS provides a digital platform to support farmers' decision-making on land use and management via an integrative spatiotemporally explicit approach that analyses a wide range of data from various sources. The approach integrates remote and in situ sensors, artificial intelligence, modelling, stakeholder-stated demand for biodiversity and ecosystem services, and participatory sustainability impact assessment to address the diverse drivers affecting agricultural land use and management design, including natural and agronomic factors, economic and policy considerations, and socio-cultural preferences and settings. Ultimately, the DAKIS embeds the consideration of ecosystem services, biodiversity, and sustainability into farmers' decision-making and enables learning and progress towards site-adapted small-scale multifunctional and diversified agriculture while simultaneously supporting farmers' objectives and societal demands. KW - Digital agriculture KW - Ecosystem services KW - Decision support system KW - Farming practices KW - Biodiversity KW - Modelling KW - Small-scale management Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:eb1-opus-8012 SN - 2666-4984 VL - 16 PB - Elsevier ER -