Remote Sensing and Machine Learning for Food Crop Production Data in Africa Post-COVID-19

• URL: http://arxiv.org/abs/2108.10054v1
• Date: Wed, 14 Jul 2021 13:14:46 GMT
• Title: Remote Sensing and Machine Learning for Food Crop Production Data in Africa Post-COVID-19
• Authors: Racine Ly, Khadim Dia, Mariam Diallo
• Abstract summary: Travel bans and border closures, the late reception and the use of agricultural inputs could lead to poor food crop production performances. This chapter assesses food crop production levels in 2020 in all African regions and four staples such as maize, cassava, rice, and wheat. The production levels are predicted using the combination of biogeophysical remote sensing data retrieved from satellite images and machine learning artificial neural networks (ANNs) technique.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In the agricultural sector, the COVID-19 threatens to lead to a severe food security crisis in the region, with disruptions in the food supply chain and agricultural production expected to contract between 2.6% and 7%. From the food crop production side, the travel bans and border closures, the late reception and the use of agricultural inputs such as imported seeds, fertilizers, and pesticides could lead to poor food crop production performances. Another layer of disruption introduced by the mobility restriction measures is the scarcity of agricultural workers, mainly seasonal workers. The lockdown measures and border closures limit seasonal workers' availability to get to the farm on time for planting and harvesting activities. Moreover, most of the imported agricultural inputs travel by air, which the pandemic has heavily impacted. Such transportation disruptions can also negatively affect the food crop production system. This chapter assesses food crop production levels in 2020 -- before the harvesting period -- in all African regions and four staples such as maize, cassava, rice, and wheat. The production levels are predicted using the combination of biogeophysical remote sensing data retrieved from satellite images and machine learning artificial neural networks (ANNs) technique. The remote sensing products are used as input variables and the ANNs as the predictive modeling framework. The input remote sensing products are the Normalized Difference Vegetation Index (NDVI), the daytime Land Surface Temperature (LST), rainfall data, and agricultural lands' Evapotranspiration (ET). The output maps and data are made publicly available on a web-based platform, AAgWa (Africa Agriculture Watch, www.aagwa.org), to facilitate access to such information to policymakers, deciders, and other stakeholders.

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