A Machine Learning Approach for Estimating sweetpotato Cultivation Areas in Uganda

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Authors

Date Issued

Metadata

Citation

Rajendran, S. 2024. A Machine Learning Approach for Estimating sweetpotato Cultivation Areas in Uganda. International Potato Center.

Permanent link to cite or share this item

https://hdl.handle.net/10568/172746

Author ORCID identifiers

S.Rajendran https://orcid.org/0000-0003-2769-2548

Contributes to SDGs

SDG 2 - Zero hunger

SDG 12 - Responsible consumption and production

SDG 13 - Climate action

SDG 9 - Industry, innovation and infrastructure

SDG 1 - No poverty

Organizations Affiliated to the Authors

International Potato Center

Investors/sponsors

CGIAR Trust Fund

CGIAR Action Areas

Resilient Agrifood Systems; Genetic Innovation

CGIAR Impact Areas

Climate adaptation and mitigation; Nutrition, health and food security

CGIAR Initiatives

Collections

CIP Briefs
CGIAR Initiative on Seed Equal
CIP Science Goal: Crop Improvement
CIP Science Goal: Regenerative Agriculture

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Language

en

AGROVOC Keywords

machine learning; sweet potatoes; seed systems; climate-smart agriculture

Subjects

SWEETPOTATOES; CROP AND SYSTEMS SCIENCES CSS; CLIMATE-SMART AGRICULTURE; FOOD SECURITY; BIGDATA

Countries

Access Rights

Open Access

Usage Rights

Attention