- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
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Mapping of soil suitability for medicinal plants using machine learning methods ; volume:14 ; number:1 ; day:14 ; month:2 ; year:2024 ; pages:1-16 ; date:12.2024
Scientific reports ; 14, Heft 1 (14.2.2024), 1-16, 12.2024
- Creator
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Roopashree, S.
Anitha, J.
Challa, Suryateja
Mahesh, T. R.
Venkatesan, Vinoth Kumar
Guluwadi, Suresh
- Contributor
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SpringerLink (Online service)
- DOI
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10.1038/s41598-024-54465-3
- URN
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urn:nbn:de:101:1-2404221145227.226648854537
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:48 AM CEST
Data provider
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Associated
- Roopashree, S.
- Anitha, J.
- Challa, Suryateja
- Mahesh, T. R.
- Venkatesan, Vinoth Kumar
- Guluwadi, Suresh
- SpringerLink (Online service)
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Sampling design and machine learning optimization for the application of soil sensing data in digital soil mapping
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Quantitative mapping of soil property based on laboratory and airborne hyperspectral data using machine learning
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Explainable machine learning in soil mapping : Peeking into the black box
CLIENT-SIDE WEB MAPPING SYSTEM FOR VINEYARD SUITABILITY ASSESSMENT
Agro-ecological suitability assessment of Chinese Medicinal Yam under future climate change
Predicting soil cone index and assessing suitability for wind and solar farm development in using machine learning techniques
Sampling design and machine learning optimization for the application of soil sensing data in digital soil mapping
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Extracting medicinal chemistry intuition via preference machine learning
Essays on the suitability of machine learning algorithms for financial forecasts
The suitability of remotely sensed soil moisture for improving operational flood forecasting
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