Proceedings :: International Society of Precision Agriculture

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Zakhary, A

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Authors

Tabbassi, A

Henkler, S

Zakhary, A

Rother, K

Sorokina, V

Klinkov, I

Yablonski, D

Henkler, S

Zakhary, A

Topics

Decision Support Systems, Cloud Platforms, and Open Data Solutions

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Type

Oral

Poster

Year

2026

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Filter results2 paper(s) found.
1. Democratizing Prescriptive Agronomy: Quality-Preserving Edge AI for Sugar Beets The 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 Download paper
2. Universal Dataset Constructor & Preprocessing Framework for Earth Observation AI Tasks in Digital Agriculture The 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 Download paper

Filter results2 paper(s) found.

1. Democratizing Prescriptive Agronomy: Quality-Preserving Edge AI for Sugar Beets

The 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

Download paper

2. Universal Dataset Constructor & Preprocessing Framework for Earth Observation AI Tasks in Digital Agriculture

The 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

Download paper