Site-specific sunflower yield forecasting based on spatial analysis and machine learning
The study focuses on the development of an intelligent yield forecasting system using satellite data, geospatial data and climate indicators. The introduction of modern information technologies, in particular machine learning and big data analysis methods, provides agricultural professionals with st...
Saved in:
| Date: | 2025 |
|---|---|
| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
| Published: |
Видавничий дім "Академперіодика" НАН України
2025
|
| Series: | Доповіді НАН України |
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Journal Title: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Cite this: | Site-specific sunflower yield forecasting based on spatial analysis and machine learning / V.H. Hnatiienko, H.M. Hnatiienko, O.L. Zozulya, V.Ye. Snytyuk, V.V. Schwartau // Доповіді Національної академії наук України. — 2025. — № 4. — С. 17-26. — Бібліогр.: 14 назв. — англ. |
Institution
Digital Library of Periodicals of National Academy of Sciences of Ukraine| Summary: | The study focuses on the development of an intelligent yield forecasting system using satellite data, geospatial data and climate indicators. The introduction of modern information technologies, in particular machine learning and big data analysis methods, provides agricultural professionals with strategic advantages, reducing the risks of excessive pesticide use and promoting sustainable agricultural development. This study aims to optimize desiccant application in sunflower cultivation by modeling potential yield losses based on data obtained during the growing season. The use of digital solutions is relevant for crop production, as it increases the accuracy of forecasts and the efficiency of management decisions, while reducing costs and increasing the productivity of agrophytocenoses. |
|---|