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Using Machine Learning to Assess Yield Impacts of Crop Rotation : Combining Satellite and Statistical Data for Ukraine

To overcome the constraints for policy and practice posed by limited availability of data on crop rotation, this paper applies machine learning to freely available satellite imagery to identify the rotational practices of more than 7,000 villages i...

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Bibliographic Details
Main Authors: Deininger, Klaus, Ali, Daniel Ayalew, Kussul, Nataliia, Lavreniuk, Mykola, Nivievskyi, Oleg
Language:English
Published: World Bank, Washington, DC 2020
Subjects:
MACHINE LEARNING
CROP ROTATION
AGRICULTURAL PRODUCTIVITY
SATELLITE IMAGERY
Online Access:http://documents.worldbank.org/curated/en/459481593442273789/Using-Machine-Learning-to-Assess-Yield-Impacts-of-Crop-Rotation-Combining-Satellite-and-Statistical-Data-for-Ukraine
http://hdl.handle.net/10986/34021

Internet

http://documents.worldbank.org/curated/en/459481593442273789/Using-Machine-Learning-to-Assess-Yield-Impacts-of-Crop-Rotation-Combining-Satellite-and-Statistical-Data-for-Ukraine
http://hdl.handle.net/10986/34021

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