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P344 - 44. GIL-Jahrestagung 2023 - Fokus: Biodiversität fördern durch digitale Landwirtschaft

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  • Konferenzbeitrag
    Exploring AI for interpolation of combine harvester yield data
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Johannsen, Lucas; Ramm, Sebastian; Reckleben, Yves; Doerfel, Stephan
    In the wake of eco-schemes introduced by the EU's Common Agricultural Policy, this study evaluates AI-based interpolation methods for generating yield maps as one component of a decision support system, aiding farmers in eco-scheme implementation. The research contrasts ordinary Kriging (OK) with AI techniques – Random Forest (RF) enhanced with spatial fea-tures (RFsp), covariates (RFspco) and DeepKriging (DK), utilizing combine harvester yield data. Performance metrics show AI, especially RF variants, surpassing OK. For a 0.7 split, R² were 0.6 (OK), 0.77 (RF), 0.81 (RFsp), 0.78 (DK); MSE were 0.6 (OK), 0.34 (RF), 0.28 (RFsp), 0.32 (DK). Spatial features boosted accuracy, while incorporating Terrain Models had no rele-vant impact on the results. These findings are crucial for an automated, accurate decision support system, facilitating eco-scheme adoption for farmers. The efficiency of AI methods underscores their potential in promoting sustainable, informed agricultural practices.
  • Konferenzbeitrag
    Compliance of agricultural AI systems – app-based legal verification throughout the development
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Kruse, Niklas; Wachter, Paul; Schöning, Julius
    Significant advances in artificial intelligence (AI) have been achieved; however, practical implementation in agriculture remains limited. Compliance with emerging regulations, such as the EU AI Act and GDPR, is now vital, even for non-critical AI systems. Developers need tools to assess legal compliance, which is complex, often requiring full legal advice. To address this issue, we are developing a support app that simplifies the legal aspects of AI system development, covering the entire lifecycle, from conception to distribution. The current app, which covers the key legal area of copyright and will soon include GDPR and the AI Act, aims to bridge the gap between AI research and agriculture. An evaluation of our app by experts from both the legal and the IT domains shows that the app assists the developers so that they make legally correct statements. Consequently, it promotes legal compliance and awareness among developers, contributing to the seamless integration of AI into agriculture. The need for compliant AI systems in various industries, including agriculture, will only increase as regulations evolve.
  • Konferenzbeitrag
    44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft - Komplettband
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024)
  • Konferenzbeitrag
    Adaptive real-time crop row detection through enhancing a traditional computer vision approach
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Hussaini, Mortesa; Voigt, Max; Stein, Anthony
    Crop row detection is important to enable precise management of fields and optimize the use of resources such as fertilizers and water. Autonomous machines need an effective but also robust real-time row detection system to be able to adapt to different field conditions. In this paper, we present an enhanced crop row detection approach which integrates traditional computer vision methods with further techniques such as k-means clustering or probabilistic Hough transformation. The resulting hybrid method allows for efficient and robust detection of straight and curved crop rows in image and video material. We validate our approach empirically on the crop row benchmark dataset (CRBD) and compare it with other state-of-the-art approaches. Furthermore, we demonstrate that our approach is designed to be adaptive and thus becomes straightforwardly transferable to other experimental setups. To corroborate that, we report on results when our approach is validated on representative corner cases which have been collected in the scope of a research project. Observations and current limitations of our approach are discussed along with possible solutions to overcome them in future work.
  • Konferenzbeitrag
    Teilflächenspezifische Aussaat von Körnermais: Potenziale und Limitationen
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Vinzent, Beat; Maidl, Franz-Xaver; Gandorfer, Markus
    Auf Basis mehrjähriger Feldversuchsdaten wurden in Parzellenversuchen unter süddeutschen Anbaubedingungen auf Standorten unterschiedlicher Ertragsfähigkeit die Auswirkungen einer Saatstärkenvariation bei Körnermais auf Kornerträge und saatgutkostenfreie Leistung analysiert. Die Variation der Saatstärke auf den Kornertrag war insgesamt nicht sehr ausgeprägt, die Saatgutkosten hingegen unterschieden sich deutlich. In zwei von drei Einzeljahren ergaben sich trotz des breiten ökonomischen Optimums in der ex-post-Betrachtung moderate Vorteile für eine teilflächenspezifische Saat, erstaunlicherweise lag das ökonomische Optimum der Saatstärke auf den getesteten Standort sehr hoch.
  • Konferenzbeitrag
    A crowdsensing-based smartphone app for optimal food storage and real-time best-before dates
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Senge, Julia; Mielinger, Ellen; Wendt, Marie Catherine; Weinrich, Ramona; Krupitzer, Christian
    Private households are responsible for 59% of Germany’s 11 million tons of food waste. Consumers’ behavior significantly contributes to food waste, prompting our concept to develop a smartphone application aimed at diminishing uncertainties about food expiration and safety. Utilizing a Design Science approach, we developed a prototype for a smartphone app, integrating novel functionalities to minimize food waste at the consumer household level. We analyzed existing market applications and, as a result, introduced the Freshlimeter, a unique feature that estimates the real-time best-before date within our app using feedback from consumers. We also highlight the potential for innovative app features, such as integrating a chatbot with image recognition capabilities to enable freshness assessments, especially for unpackaged or opened food.
  • Konferenzbeitrag
    Soil moisture simulations for a sustainable irrigation management
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Wenzel, Jan Lukas; Conrad, Christopher; Pöhlitz, Julia
    Accurate estimations of crop water requirements accounting for spatial heterogeneous soil properties are recognized as a major contribution towards a sustainable agricultural irrigation management. Crop-specific irrigation demand estimations may be improved by physics-based soil moisture models, although spatially distributed soil moisture simulations strongly rely on profound assessments of the model accuracy and applicability under open-field conditions. Hence, this study aims to investigate simulated root-zone soil moisture dynamics on a variably irrigated potato field provided by the HYDRUS-1D model and its suitability for irrigation management purposes in terms of input parameter requirements and applicability on larger, heterogeneous sites. All simulations were highly accurate (RMSE = 0.018 m3 m-3), when compared to in-situ measurements, but varied stronger in topsoil than in subsoil layers. A pixel-based approach using aggregated soil properties, phenological characteristics and meteorological conditions enables appropriate trade-offs between simulation accuracy and the parameterization effort and applicability in irrigation management.
  • Konferenzbeitrag
    Organic sugar beet (Beta vulgaris L.) cultivation using the field robot Uckerbot as a system for sustainable farming
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Steinherr, Leonie; Belo, Miguel; Trappe, Rodja; Acosta-Ortiz, Dafne; Birkmann, Amanda; Krachunova, Tsvetelina; Bloch, Ralf
    The field robot Uckerbot is an autonomous mobile robot developed in a co-design process by farmers, industry and researchers for intra-row weed control in organic sugar beets. First on-farm results indicated better performance of the Uckerbot compared with common weed hoeing strategies. The robot showed 90% accuracy of sugar beet detection and 88% weed efficiency with a drill mechanism. Further development of the robot includes enabling it to work in a robot swarm for an increase in efficiency and working speed and enabling the image recognition system to distinguish between different weed types. This will allow the Uckerbot to skip tolerable wild field herbs for increased biodiversity.
  • Konferenzbeitrag
    Mapping invasive Lupine on grasslands using UAV images and deep learning
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Wijesingha, Jayan; Schulze-Brüninghoff, Damian; Wachendorf, Michael
    Semi-natural grasslands are threatened by invasive species. This study employs high-resolution images captured by an unmanned aerial vehicle (UAV) and deep learning techniques to map Lupine (Lupinus polyphyllus Lindl.) in grasslands, which is one of the most common invasive species in European grasslands. The methodology involves RGB image acquisition, structure from motion processing, canopy height modelling, and deep learning semantic segmentation model development. The resulting models were trained on RGB data, canopy surface height data, and their combination. The models demonstrate high accuracy and efficacy in identifying Lupine distribution. These models offer a valuable tool for continuously monitoring and managing invasive Lupine, with potential applications in similar environments without retraining. The method is beneficial for early-stage invasion detection, facilitating more targeted management efforts for ecologists.
  • Konferenzbeitrag
    Verteilung und Zusammensetzung von Abfall in ländlichen Gebieten
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Stoop, Ralph L.; Sax, Markus; Anken, Thomas
    Achtlos liegengelassene oder weggeworfene Gegenstände („Littering“) wie Verpackungen, Flaschen, Alu-Dosen usw. stellen nicht nur eine Gefahr für Ökosysteme dar, sondern auch für die Landwirtschaft. Um Littering in der ländlichen Schweiz besser zu verstehen, haben wir mit Drohnen und Smartphones RGB-Farbbilder entlang verschiedener Straßenabschnitte gesammelt. Unsere ersten Analysen deuten darauf hin, dass in Schweizer Landwirtschaftsgebieten die Abfalldichte relativ gering ist, im Bereich von ca. 0,02-0,11 Abfallgegenstände/Straßenmeter (Zigaretten ausgenommen). Außerdem finden wir ein starkes Abfallen der Abfalldichte nach den ersten zwei Metern orthogonal zum Straßenrand. Unsere vorläufigen Resultate, insbesondere die Dichte und räumliche Verteilung der Gegenstände, weisen darauf hin, dass in landwirtschaftlichen Gebieten ein automatisches Aufnehmen vor dem Grünschnitt des Seitenstreifens ausreichend ist.