Quality and Safety of Food and Feed

Aufsätze in referierten Fachzeitschriften [1 result]

• Babor, M.; Liu, S.; Arefi, A.; Olszewska-Widdrat, A.; Venus, J.; Sturm, B.; Höhne, M. (2025): Interpretable Domain Adaptation Enables Robust Lactic Acid Fermentation Monitoring from Waste. Results in Engineering. : p. 108477. Online: https://doi.org/10.1016/j.rineng.2025.108477

Beiträge zu Sammelwerken [3 Results]

• Hanike Basavegowda, D.; Liu, S.; Höhne, M.; Weltzien, C. (2025): Towards Reliable Deep Learning Models for Plant Species Identification in Diverse Real-World Scenarios. In: 82nd International Conference on Agricultural Engineering Land.Technik AgEng 2025. 82nd International Conference on Agricultural Engineering Land.Technik AgEng 2025. VDI, Düsseldorf, (0083-5560/978-3-18-092465-6), p. 155-160.
• Liu, S.; Babor, M.; Munyendo, L.; Hitzmann, B.; Sturm, B.; Höhne, M. (2024): Advancements in coffee authenticity: A spectroscopic feature compression approach using eXplainable AI and vision transformer. In: Katsoulas, N.(eds.): AgEng 2024 Proceedings Book. AgEng 2024 International Conference of EurAgEng. Hellenic Society of Agricultural Engineers, Athens, Greece, (978-618-82194-1-0), p. 436-442. Online: https://convin.gr/assets/files/misc/AgEng2024_Proceedings_ISBN.pdf
• Liu, S.; Hedström, A.; Hanike Basavegowda, D.; Weltzien, C.; Höhne, M. (2024): Explainable AI in grassland monitoring: Enhancing model performance and domain adaptability. In: Hoffmann, C.; Stein, A.; Gallmann, E.; Dörr, J.; Krupitzer, C.; Floto, H.(eds.): Informatik in der Land-, Forst- und Ernährungswirtschaft. Focus: Biodiversität fördern durch digitale Landwirtschaft: Welchen Beitrag leisten KI und Co?. 44. GIL-Jahrestagung - Biodiversität fördern durch digitale Landwirtschaft: Welchen Beitrag leisten KI und Co?. Gesellschaft für Informatik (GI), Bonn, (1617-5468/978-3-88579-738-8), p. 143-154. Online: https://gil-net.de/wp-content/uploads/2024/02/GI_Proceedings_344-3.f-1.pdf

Vorträge und Poster [8 Results]

• Liu, S.; Babor, M.; Verduyn, C.; Vandenberghe, B.; Parodi, B.; Weltzien, C.; Höhne, M. (2025): LeafTrackNet: A Deep Learning Framework for Robust Leaf Tracking in Top-Down Plant Phenotyping.
• Liu, S.; Babor, M.; Verduyn, C.; Vandenberghe, B.; Betoni Parodi, B.; Weltzien, C.; Höhne, M. (2025): LeafTrackNet: A Deep Learning Framework for Robust Leaf Tracking in Top-Down Plant Phenotyping.
• Liu, S.; Babor, M.; Verduyn, C.; Vandenberghe, B.; Betoni Parodi, B.; Weltzien, C.; Höhne, M. (2025): High-Resolution Leaf Tracking for Real-World Crop Phenotyping: Introducing CanolaTrack and LeafTrackNet.
• Liu, S. (2024): AI for Biodiversity Indicator Monitoring.
• Liu, S. (2024): Precision Agriculture and Data Science.
• Liu, S.; Babor, M.; Munyendo, L.; Hitzmann, B.; Sturm, B.; Höhne, M. (2024): Advancements in Coffee Authenticity: A Spectroscopic Feature Compression Approach Using eXplainable AI and Vision Transformer.
• Liu, S. (2024): Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability.
• Liu, S.; Hedström, A.; Hanike Basavegowda, D.; Weltzien, C.; Höhne, M. (2024): Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability.