A Machine Learning Approach for Estimating sweetpotato Cultivation Areas in Uganda

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Date Issued

2024-12

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en
Type
Brief

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Open Access Open Access

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CC-BY-4.0
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Citation

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

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

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Abstract/Description

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
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
Uganda
Organizations Affiliated to the Authors
International Potato Center
CGIAR Action Areas
Resilient Agrifood Systems; Genetic Innovation
CGIAR Impact Areas
Climate adaptation and mitigation; Nutrition, health and food security
CGIAR Initiatives
Seed Equal

Collections

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