This document summarizes a study that used remote sensing data and machine learning to model and forecast food crop production in six West African countries during the COVID-19 pandemic. Satellite imagery was analyzed to generate maps of normalized difference vegetation index (NDVI), land surface temperature, and rainfall over time. These inputs were fed into an artificial neural network model along with crop mask and production data to establish relationships and predict future crop outputs. The results showed forecasts of millet production in Senegal from 2005-2017 that could help planners address food insecurity risks from pandemic disruptions.

1. Food Crop Production during the COVID-19 Pandemic
The Case of Six Western African Countries
Côte d’Ivoire, Mali, Burkina Faso, Sierra Leone, The Gambia, and Senegal
ECOWAS Regional Learning Event
February 11th, 2021
Racine Ly, Director Data Management, Digital Products, and Technology
AKADEMIYA2063

2. Outline
1. Introduction & Context
2. Remotely Sensed Data
3. Machine Learning Framework
4. Food Crop Production Model
5. Results
Notice:
The shown boundaries and names, and the designations used on maps do not imply official endorsement or acceptance by AKADEMIYA2063.

3. 1. Introduction & Context
• Measures taken to mitigate the COVID-19 propagation put a heavy strain onto the
agricultural sector.
• Inadequate growing conditions can also push African countries at the blink of a
food crisis.
• From the production side, the interrelationship between food crop production and
the COVID-19 is not well established.
• In periods of uncertainties, forecasts can play an important role to reduce the cost
of bad decisions and allow to plan for the recovery process.
• We combined remotely sensed data and machine learning techniques to provide
maps of food crop production forecasts for several countries in Africa.

4. 1. Introduction & Context (Cont’d)
Better agricultural sector data through remote sensing and artificial intelligence
• The challenge of COVID-19 on food production systems is not only the likely extent
and complexity of the disruptions but also the difficulty to identify and track them
in real time.
• The propagation of the disease can be tracked through testing and tracing, while it
is impossible, even in normal times, to have accurate information on cropping
activities.
• The lack of information about growing conditions can be overcome by using today’s
digital technologies e.g., remote sensing data and machine learning techniques.
• The many weaknesses hampering the access to good quality agricultural statistics
can be overcome using the same digital technologies.

5. 2. Remotely Sensed Data
• Remotely sensed data through sat. images provide a wealth of information about
features on earth.
• Several advantages of using multispectral satellite images
• Vegetation, including crops, have a specific way to respond to light
Figure 1. (left) False RGB color scene of the North of Senegal with agricultural lands, bare soil, and water. (Right) The
same scene after an unsupervised classification with seven clusters using K-means and Landsat 8 spectral bands. Key messages
1. Features on earth react differently
to the electromagnetic spectrum.
2. Features on earth can be identified
from satellite images based on their
reflectance.

6. 2. Remotely Sensed Data (Cont’d)
Application to our Food Crop Production Model
• Vegetation (crops) only absorb specific wavelengths as energy for photosynthesis.
• What is not absorbed is considered as reflected by the leaves.
Figure 2. Reflectance of healthy and stressed plants across the visible
and infrared spectrum filter wavelengths. (McVeagh et al., 2012)
Figure 3. (top-left) 2017 NDVI map; (top-right) 2017 Rainfall
data (CHIRPS); (bottom-left) 2017 Daytime Land Surface
Temperature – Senegal. Source: Ly & Dia, 2020.
The 3 types of maps are
used as inputs.

7. Figure 4. Senegal Millet
Production (left) 2005;
Middle 2010; (Right) 2017).
Data Source: IFPRI, 2020,
Map Source: Ly et al., 2020.
3. Machine Learning Framework
• Machine Learning techniques are draining attention into the research community.
• Two main ways of training a machine learning: (Supervised) Building a relationship
between inputs and their corresponding examples; (Unsupervised) Identify
similarities within the dataset (without examples).
• In our case, we use artificial neural networks which are the supervised type.
Production values as examples

8. 4. Food Crop Production Model
Training Scheme
NDVI
LST
RAIN
2005
2010
2017
2005
2010
2017
2005
2010
2017
Crop Masks
2005
2010
2017
2005
2010
2017
2005
2010
2017
Neural Net.
Raw sat. Images Masked images Labels
(Examples)
Learning Process

9. 5. Results

10. 5. Results (Cont’d)

11. 5. Results (Cont’d)

12. THANK YOU
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