P344 - 44. GIL-Jahrestagung 2023 - Fokus: Biodiversität fördern durch digitale Landwirtschaft
Auflistung P344 - 44. GIL-Jahrestagung 2023 - Fokus: Biodiversität fördern durch digitale Landwirtschaft nach Erscheinungsdatum
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- KonferenzbeitragDeep 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, CorneliaEco-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.
- KonferenzbeitragImage-based activity monitoring of pigs(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Jan-Hendrik Witte, Jorge Marx GómezIn modern pig livestock farming, animal well-being is of paramount importance. Monitoring activity is crucial for early detection of potential health or behavioral anomalies. Traditional object tracking methods such as DeepSort often falter due to the pigs' similar appearances, frequent overlaps, and close-proximity movements, making consistent long-term tracking challenging. To address this, our study presents a novel methodology that eliminates the need for conventional tracking to capture activity on pen-level. Instead, we segment video frames into predefined sectors, where pig postures are determined using YOLOv8 for pig detection and EfficientNetV2 for posture classification. Activity levels are then assessed by comparing sector counts between consecutive frames. Preliminary results indicate discernible variations in pig activity throughout the day, highlighting the efficacy of our method in capturing activity patterns. While promising, this approach remains a proof of concept, and its practical implications for real-world agricultural settings warrant further investigation.
- KonferenzbeitragDigitale Experimentierfelder zur Vernetzung in Technik und Wissen für eine digitale Landwirtschaft auf Zukunftsbetrieben in Baden-Württemberg(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Hauke Delfs, Annabell CankayaIm Rahmen des Projektes TechKnowNet soll die Etablierung digitaler Systeme auf landwirtschaftlichen Betrieben vorangebracht und entsprechende Lehrformate für den Unterricht an den Fachschulen für Landwirtschaft erstellt werden. Auf zehn landwirtschaftlichen Betrieben in Baden-Württemberg wird der Status quo der aktuell eingesetzten digitalen Technologien erhoben sowie deren zukünftiger Einsatz praxisnah begleitet. Die daraus hervorgehenden Erkenntnisse werden in Empfehlungen für die landwirtschaftliche Praxis sowie in Lehrformate an Fachschulen und in die Beratung übertragen. Digitale Technologien sind in einigen Bereichen bereits gut etabliert, jedoch ist der Bedarf an Unterstützung in Beschaffung und Beratung hoch.
- KonferenzbeitragExplainable AI in grassland monitoring: Enhancing model performance and domain adaptability(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Shanghua Liu, Anna HedströmGrasslands are known for their high biodiversity and ability to provide multiple ecosystem services. Challenges in automating the identification of indicator plants are key obstacles to large-scale grassland monitoring. These challenges stem from the scarcity of extensive datasets, the distributional shifts between generic and grassland-specific datasets, and the inherent opacity of deep learning models. This paper delves into the latter two challenges, with a specific focus on transfer learning and eXplainable Artificial Intelligence (XAI) approaches to grassland monitoring, highlighting the novelty of XAI in this domain. We analyze various transfer learning methods to bridge the distributional gaps between generic and grassland-specific datasets. Additionally, we showcase how explainable AI techniques can unveil the model's domain adaptation capabilities, employing quantitative assessments to evaluate the model's proficiency in accurately centering relevant input features around the object of interest. This research contributes valuable insights for enhancing model performance through transfer learning and measuring domain adaptability with explainable AI, showing significant promise for broader applications within the agricultural community.
- KonferenzbeitragComparison of UAV- and mowing machine-mounted LiDAR for grassland canopy height estimation(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Bracke, Justus; Storch, Marcel; Bald, Janis; Jarmer, ThomasTowards autonomous process monitoring, canopy height estimation in grassland based on data from a mowing machine-mounted LiDAR and a UAV-LiDAR system is compared to manually measured ground truth heights. In a field trial, a LiDAR mounted on the cabin roof of the mowing machine recorded data during the mowing process, while two recording flights before and after the mowing were conducted with a UAV-LiDAR. The data from both systems were processed similarly and parameters such as height estimation method, spatial resolution and percentile filters were systematically varied to investigate their influence on height estimation accuracy. Statistical evaluation showed that canopy height estimates based on the UAV-LiDAR (R² = 0.89, RMSE = 0.05 m) were more accurate and precise than those based on the mowing machine-mounted LiDAR (R² = 0.51, RMSE = 0.08 m). The influence of the different investigated parameters varied.
- KonferenzbeitragPraxistest zum Einsatz von UHF-RFID-Transponderohrmarken in der Ferkelaufzucht(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Förschner, Adriana; Kapun, Anita; Gallmann, EvaDer Einsatz der elektronischen Tiererkennung in der landwirtschaftlichen Nutztierhaltung bietet viele Möglichkeiten im Bereich des Tiermonitorings. In diesem Beitrag werden in einem Projekt entwickelte elektronische Ohrmarken vorgestellt und getestet, die eine Pulkerfassung in Gruppen ermöglichen und dabei klein genug sind, um sie Aufzuchtferkeln einzuziehen. Hierfür wurden die Beschäftigungstürme in vier Buchten mit Antennen ausgestattet und insgesamt 96 Aufzuchtferkeln elektronische UHF-RFID-Transponderohrmarken eingezogen. Erste Auswertun-gen zeigen sehr gute Ergebnisse bezüglich der Erkennungsrate und der Lesereichweite der Ohrmarken. Weitere Auswertungen sind nötig, um die Wiederholbarkeit und Zuverlässigkeit der Funktionalität der Ohrmarken zu überprüfen.
- KonferenzbeitragTowards on-line monitoring and route re-planning in arable crop harvest(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Santiago Focke Martinez, Isaak IhorstRoute planning for farming machines can be used as a tool to improve the efficiency of arable farming operations. However, discrepancies between actual process parameters and the parameters used for route planning might result in the need to re-plan using updated/corrected planning parameters. This paper presents a concept for process monitoring and route re-planning in arable harvesting operations, together with updates on a previously presented route-planning tool developed to support re-planning during harvesting, and a set of monitoring components developed to generate field worked-area grid-maps and to monitor deviations between planned inner-field tracks and actual machine transit. The newly implemented re-planning features in the route planner and the monitoring components were tested under a simulated harvesting scenario.
- KonferenzbeitragVerbindung von Wissenschaft und Praxis: WiLaDi(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Pfaff, Sara Anna; Munz, JohannesMittlerweile stehen zahlreiche digitale Technologien in der Landwirtschaft zur Verfügung, welche positive Effekte auf die ökologische, ökonomische und soziale Nachhaltigkeit versprechen. Allerdings ist die aktive Nutzung digitaler Technologien z. T. verhalten, was sich vor allem mit finanziellen Barrieren begründen lässt. Aktuell gibt es nur bedingt Möglichkeiten für Landwirte, herstellerunabhängige Informationen und Entscheidungsunterstützungen zu nutzen. Daher wurde im Rahmen des DiWenkLa-Projektes in den Jahren 2021 und 2022 das ökonomisch basierte Online-Tool „WiLaDi“ (Wirtschaftlichkeitsrechner Landwirtschaft Digital) entwickelt. WiLaDi basiert auf einem ökonomischen Modell orientiert an der Leistungs-Kostenrechnung und berücksichtigt insgesamt 27 Technologievariationen im Ackerbau. Das Tool ermöglicht es Landwirten, die Kosten und Nutzen je Technologie betriebsindividuell zu errechnen sowie weiteres technologiespezifisches Informationsmaterial einzusehen. Zukünftig soll WiLaDi nicht nur auf der einzelbetrieblichen Ebene, sondern auch in Kooperation mit Bildungs- und Beratungsakteuren nutzbar sein.
- 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.
- 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.