book chapter

Remote sensing and machine learning for food crop production data in Africa post-COVID-19

by Racine Ly,
Khadim Dia and
Mariam A. Diallo
Publisher(s): AKADEMIYA2063international food policy research institute (ifpri)
Open Access | CC BY-NC-ND-4.0
Citation
Ly, Racine; Dia, Khadim; and Diallo, Mariam A. 2021. Remote sensing and machine learning for food crop production data in Africa post-COVID-19. In 2021 Annual Trends and Outlook Report: Building Resilient African Food Systems After COVID-19, eds. John M. Ulimwengu, Mark A. Constas, and Éliane Ubalijoro. Chapter 9, Pp. 128-154. Kigali, Rwanda; and Washington, DC: AKADEMIYA2063; and International Food Policy Research Institute (IFPRI). https://ebrary.ifpri.org/digital/collection/p15738coll2/id/134750

The world is experiencing an unprecedented health crisis during the spread of COVID-19 (SARS-CoV-2, or Severe Acute Respiratory Syndrome Coronavirus 2). While the pandemic appears to be less severe on the African continent than in other geographic regions (Global Change Data Lab 2021), its economic impact is significantly more pronounced. COVID-19 is upending livelihoods, damaging business and government balance sheets, and threatening to reverse development gains and growth prospects for years to come in Africa south of the Sahara (IFC 2020). The World Bank forecasts that Africa south of the Sahara will go into recession in 2020 and that COVID-19 will cost the region between $37 billion and $79 billion in output losses in 2020 alone. The informal sector, a significant source of income and employment, will be the hardest hit.

Full Book [download]