P344 - 44. GIL-Jahrestagung 2024 - Fokus: Biodiversität fördern durch digitale Landwirtschaft
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- KonferenzbeitragAdaptive 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, AnthonyCrop 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.
- KonferenzbeitragCompliance 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, JuliusSignificant 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.
- KonferenzbeitragExploring AI for interpolation of combine harvester yield data(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Johannsen, Lucas; Ramm, Sebastian; Reckleben, Yves; Doerfel, StephanIn 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.
- Konferenzbeitrag44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft - Komplettband(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024)
- KonferenzbeitragRobotic process control for multi-vegetable micro spot-farming using digital twin simulation(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Weber, Bettina; Chande, Sahil; Maike, Simon; Verbunt, Maarten; Lee, Ting Sheng; Becker, Rolf; Franko, JosefCurrent robotic approaches in smart farming are often limited to a specific task such as weeding or harvesting. Contrary to this, the AgriPV-Bot aims at sustainable and efficient micro spot full vegetable farming by focusing on mixed vegetable cultivation through automated horticultural processes. Such a holistic approach requires sophisticated robotic process control. This paper presents the development of the underlying state machine built in ROS SMACH to handle a variety of tasks within the system. All processes and interactions of sensors and actuators are first simulated on the digital twin software Gazebo before being deployed in the real environment. This allows for rapid iterations of software and reduces dependencies on season and crop availability regarding physical field tests.
- KonferenzbeitragSynthetic fields, real gains(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Wachter, Paul; Kruse, Niklas; Schöning, JuliusArtificial intelligence (AI) promises transformative impacts on society, industry, and agriculture, while being heavily reliant on diverse, quality data. The resource-intensive “data problem” has initialized a shift to synthetic data. One downside of synthetic data is known as the “reality gap”, a lack of realism. Hybrid data, combining synthetic and real data, addresses this. The paper examines terminological inconsistencies and proposes a unified taxonomy for real, synthetic, augmented, and hybrid data. It aims to enhance AI training datasets in smart agriculture, addressing the challenges in the agricultural data landscape. Utilizing hybrid data in AI models offers improved prediction performance and adaptability.
- KonferenzbeitragTeilflächenspezifische Aussaat von Körnermais: Potenziale und Limitationen(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Vinzent, Beat; Maidl, Franz-Xaver; Gandorfer, MarkusAuf 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.
- KonferenzbeitragOrganic 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, RalfThe 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.
- KonferenzbeitragAnalyse des Product Carbon Footprints im Produktions- und Verarbeitungsprozess von Topinambur (Helianthus tuberosus L.)(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Guerler, Hakan; Risius, Hilke; Albrecht, Richard; Rosenbaum, Julian; Röpert, Christin; Lienig, Frank; Kramer, EckartDer PCF bezieht sich auf die Bilanzierung der Treibhausgas (THG) - Emissionen und des THG-Abbaus entlang des gesamten Lebenszyklus eines Produkts. Ziel dieser Studie ist es, den Product Carbon Footprint (PCF) in der Wertschöpfungskette von Topinambur zu analysieren. Eine repräsentative Menge von 100 kg Topinambur wurde als Grundlage für die PCF-Berechnung im Anbau und in der Verarbeitung gewählt. Für die gesamte Verarbeitung wurden der Strom- und Wasserverbrauch sowie die menschliche Arbeitszeit gemessen. Die Ergebnisse zeigen die signifikante Rolle des Wasserverbrauchs bei den Treibhausgasemissionen, gefolgt vom Stromverbrauch und der menschlichen Arbeitszeit. Diese Ergebnisse unterstreichen die Dringlichkeit der Identifizierung von Emissionsminderungspotenzialen in der Topinambur-Verarbeitung und unterstreichen gleichzeitig die Bedeutung einer genauen Datenerfassung und realistischer Emissionsfaktoren für zukünftige Untersuchungen.
- KonferenzbeitragModel for the calculation of soil compaction on agricultural land(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Westerkamp, Clemens; Thünemann, Christian; Schaarschmidt, MarcoThe risk of soil compaction is a growing concern in agriculture as machinery becomes larger and larger. In this paper, a model is presented that generates a spatial estimation of the soil compaction based on soil survey mapping, soil moisture data and machinery data. The Soil Compaction Index describes the risk of harmful compaction of soil. Feasibility and deployment as an Agri-Gaia service were evaluated by an application for researchers and practitioners to predict areas with high soil compaction risk and adapt agricultural processes accordingly.