Proceedings
Authors
| Filter results3 paper(s) found. |
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1. Democratizing Prescriptive Agronomy: Quality-Preserving Edge AI for Sugar BeetsThe global sugar beet sector faces a critical production paradox where agronomic interventions designed to maximize root yield often compromise sucrose concentration and processing quality. While precision agriculture aims to navigate this delicate balance, current methodologies have reached a methodological impasse. Existing solutions are bifurcated between descriptive data-intensive machine learning (ML), which struggles to generalize across heterogeneous fields, and physiological Process-Based... A. Tabbassi, S. Henkler |
2. Democratizing Prescriptive Agronomy: Quality-Preserving Edge AI for Sugar BeetsThe global sugar beet sector faces a critical production paradox where agronomic interventions designed to maximize root yield often compromise sucrose concentration and processing quality. While precision agriculture aims to navigate this delicate balance, current methodologies have reached a methodological impasse. Existing solutions are bifurcated between descriptive data-intensive machine learning (ML), which struggles to generalize across heterogeneous fields, and physiological Process-Based... A. Tabbassi, S. Henkler, A. Zakhary, K. Rother |
3. Universal Dataset Constructor & Preprocessing Framework for Earth Observation AI Tasks in Digital AgricultureThe rapid advancement of Artificial Intelligence (AI) in digital agriculture is increasingly dependent on the ability to fuse heterogeneous data sources. While Earth Observation (EO) data from Sentinel and Landsat missions provides a backbone for monitoring, high-performance models for yield prediction and land management require a more holistic approach. This paper presents a Universal Dataset Constructor & Preprocessing Framework designed to automate the generation of combined, multimodal... V. Sorokina, I. Klinkov, D. Yablonski, S. Henkler, A. Zakhary |