Despite notable progress in reducing global poverty and hunger in recent decades, about one out of nine people in the world suffers from hunger and malnutrition. Stakeholders charged with making decisions pertaining to agricultural production, development priorities, and policies at a region-to-country scale require quantitative and up-to-date information on the types of crops being cultivated, the acreage under cultivation, and crop yields. However, many low- and middle-income countries lack the infrastructure and resources for frequent and extensive agricultural field surveys to obtain this information.
Technology supports a change of paradigm. Traditional methods of obtaining agricultural information through field surveys are increasingly being augmented by images of the Earth acquired through sensors placed on satellites. The continued improvement in the resolution of satellite images, the establishment of open-access infrastructure for processing of the images, and the recent revolutionary progress in artificial intelligence make it feasible to obtain the information at low cost and in near-to-real time.
In this brief, we discuss the use of satellite images to provide information about agricultural production in low-income countries, and we comment on research challenges and opportunities. We highlight the near-term potential of the methodology in the context of Rwanda, a country in sub-Saharan Africa whose government has recognized early the value of information technology in its strategic planning for food security and sustainability.
The view from above
By Dorota S. Temple, Jason S. Polly, Meghan Hegarty-Craver, James I. Rineer, Daniel Lapidus, Kemen Austin, Katherine P. Woodward, Robert H. Beach.
August 2019 Open Access Peer Reviewed
DOI: 10.3768/rtipress.2019.rb.0021.1908
Key Points
- Images acquired by sensors placed on satellites provide valuable information on crop acreage, health, and yields.
- Recent progress in artificial intelligence and high-performance computing makes it possible to obtain agricultural information at low cost and in near-to-real time.
- Unmanned aerial vehicles lessen the burden for ground-truth data collection required for satellite image classification.
- Continuous public and commercial support for improvements in the satellite infrastructure and in the development of robust models for information extraction will pay dividends for food security and sustainability.
Abstract
© 2023 RTI International. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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