This presentation was delivered by Dr. Racine Ly, Director, Data Management, Digital Products and Technology

1. Predicting Crop Production in West and
Central Africa Using Remote Sensing and
Machine Learning Techniques
AKADEMIYA2063-CORAF Virtual Regional Learning Event
July 6, 2021
Agricultural Production Systems in West and Central Africa: Insights to
enhance Data and Technological Advancement
Racine Ly,
Director, Data Management, Digital Products and Technology

2. Key Messages
• African agricultural sector is facing several threats (Climate change, socio-economic,
pandemics); There is a need to increase the level of preparedness for good planning.
• The combination of shocks and the many African diversities are likely to make the
one-size-fits-all approach in solving the issues inadequate.
• A community-wise targeted approach would be more suitable; But for that, timely,
accurate, and disaggregated data are critical.
• The type of data characteristics (quality, quantity, and frequency) that are needed for
analysis, monitoring and evaluation, policy formulation is not reached yet.
• Emerging technologies such as remote sensing and machine learning can help to
reduce that data gap in the African agricultural sector.

3. Outline
1. The Data Issue
2. Remote Sensing to reduce the Data gap
3. Machine Learning for Predictions
4. Results for the West and Central Africa Regions
5. Directions of Research

4. 1. The Data Issue
Figure
1.
Data
availability
indicator
for
African
countries.
Open
Knowledge
Foundation,
Global
Open
Data
Index,
2018.
Figure
2.
Data
capability
indicator
for
African
countries.
UN
e-government
index,
2018.
• Most African countries have a low data
availability index.
• With conventional capabilities or traditional
techniques, we are unlikely to solve the data
scarcity issue.
• The data availability and capability gap suggest
barriers that need to be solved, and emerging
technologies can help reduce the gap.

5. 2. Remote Sensing to reduce the Data gap
Figure
3.
Main
image:
RGB
satellite
image
(Sentinel
2)
with
its
red
band.
Source:
R.Ly
Figure
4.
Normalized
Difference
Vegetation
Index
(NDVI)
processed
for
the
same
ROI
(cf.
fig
3).
Source:
R.Ly
• Remote sensing data can cover a large area with
one image acquisition.
• Each image give us detailed specific information
about each object.
• Each layer measure earth objects’ reflectance in
a specific range of the electromagnetic
spectrum.
• Each area of data acquisition is revisited several
times a year allowing to monitor change on the
ground.

6. 2. Remote Sensing to reduce the Data gap
Figure 5. Reflectance of Soil, Green vegetation, and Water. Landsat Satellite Images
Figure 6. Vegetation Index, Land Surface Temperature, Rainfall anomalies map for
Africa. Source: Racine Ly, Khadim Dia & Mariam Diallo.
• Soil, Vegetation, and Water can be
derived from a specific combination of
remote sensing layers
• We collect data about vegetation index,
land surface temperature, rainfall, and
evapotranspiration.

7. 2. Remote Sensing to reduce the Data gap
Figure 7. Illustration of Multispectral images as a combination of
spectral layers. Each spectral layer embed the reflectance of objects
on earth on the corresponding wavelength range.
• Remote sensing allows us to access massive,
specific, and detailed data.
• The need to extract and exploit that amount
of data cannot be performed with traditional
means.
• Machine learning allows to learn how
dynamics on the ground relate to one
another, how they evolve.

8. 3. Machine Learning for Predictions
Figure 8: Machine Learning techniques and applications - source: https://www.cognub.com/index.php/cognitive-platform/

9. 3. Machine Learning for Predictions
Inputs 𝑥
Labels 𝑦
Machine Learning
Algorithm
𝑦 = 𝑓(𝑥; 𝜃)
Predictions
∆:
Residuals
ℒ 𝑦, ො
𝑦 to update
the parameters 𝜃
New input
Data
Model Predictions
Training
Phase
Predictions
The Africa Crop Production (AfCP) Model
Figure 9. Supervised machine learning technique for the African Crop Production (AfCP) model. Source: Author

10. 4. Result for the WCA region
Production quantities for cassava in the
west Africa region is expected to increase
compared to 2017 by 4.2%.
Figure 10. The 2020 predicted cassava production as a share of
the 2017 production for western African countries. Source:
Racine Ly, Khadim Dia, and Mariam Diallo, 2021.

11. 4. Result for the WCA region
The sharpest decline in production quantities in the western African region is expected for rice
with a decrease close to 12%, while maize production is expected to decline by close to 5%
(Comparison between 2017 and 2020).
Figure 11. The 2020 predicted rice production as a share of the 2017 production
for western African countries. Source: Racine Ly, Khadim Dia, and Mariam Diallo,
2021..
Figure 12. The 2020 predicted maize production as a share of the 2017
production for western African countries. Source: Racine Ly, Khadim Dia, and
Mariam Diallo, 2021..

12. 4. Result for the WCA region
• The six central African countries’ aggregated
cassava production was around 47 million
metric tons in 2017.
• The Democratic Republic of Congo (66.6%),
followed by Angola (17.9%) and Cameroon
(10.2%).
• The AfCP suggests a total cassava production
of close to 60 million metric tons for the same
countries in 2020, which corresponds to an
increase of 28% compared to 2017.
• In 2020, the distribution of total production
across individual countries is expected to
remain the same for Angola while Cameroon
shares roughly decreased by half (5.6%). The
Democratic Republic of Congo has a share
increase of near +10%.
Figure 13. The 2020 predicted cassava production as a share of the 2017
production for central African countries. Source: Racine Ly, Khadim Dia, and
Mariam Diallo, 2021..

13. www.aagwa.org 13

14. AAgWa Conceptual Framework
14
The Africa Agriculture Watch (AAgWa)
Raw
Data
(Remote
sensing
and
third-party
data)
Knowledge
(Analytics,
Interpretable
maps
and
data)
Dissemination
Decision-Making
• Data centric web
application
(accessibility).
• Data visualization
(interpretability).
• Maintenance
(sustainability).
• Spectral bands
processing and
cleaning.
• Time-series analysis.
• Thematic mapping.
• Predictions using
machine learning.
• Computer modeling.
www.aagwa.org

15. 5. Direction of Future Research
• Upgrade the AfCP spatial disaggregation to 10m/20m; A pixel will be a 100 to 400
squared meters.
• Build a crop type classifier for major crops in Africa; The African Crop Type Classifier
(AfCTC) model.
• Identify crops diversity across the continent
• Build an updated cropland map for Africa
• Identify crop growing conditions through crop phenology metrics

16. Thank You
www.akademiya2063.org
www.aagwa.org