1. Leveraging Artificial Intelligence (AI) & Satellite Remote
Sensing Data for Decision-making in the African
Agricultural Sector
AFRICA AGRICULTURE WATCH
(AAgWa)
• Dr. Racine Ly
Director, Department of Data Management, Digital Products,
and Technology, AKADEMIYA2063

2. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
Key Messages
• The Africa agricultural sector is facing numerous threats, from conflicts, extreme climate events,
health crisis; Good planning and preparedness is needed to reduce uncertainties in decision-making.
• Conventional data gathering and analytics techniques might not be adequate to deliver the level of
details that is required for tailored and efficient intervention planning.
• Satellite remote sensing can help to reduce the data gap; And machine learning allows to learn hidden
patterns for predictions.
• The Africa Agriculture Watch combines both technologies to disseminate data and analytics to support
decision-making.

3. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
Outline
1. Anatomy of Decision-Making
2. Enriching the input Data
3. Leveraging Predictability
4. AAgWa Conceptual Framework
5. AAgWa main and upcoming Products

4. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
The AAgWa Overarching Goal
Predictions
Decision Making
Tools
Strategy
Society • Use predictive models to make the invisible visible
• Reduce uncertainties in decision making processes
• Embed such models in a web-based platform
• Policymakers and deciders can use it to strategize
• Have a greater impact in the society through policies

5. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
Anatomy of Decision-Making
Judgment
Input Data Predictions
Training
Action Outcome
Feedback
Figure 1. An Anatomy of decision-making. Adapted from Agrawal et al., 2018 – Prediction Machines: The
simple economics of artificial intelligence.
Forward
Backward
• The more comprehensive
the input data, the better
the process;
• Predictions are at the heart
of decision-making.
• Predictions can be improved
for decision-makers to add
value to them.

6. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
Enriching the input Data – The data issue.
Ly,
Racine.
“Machine
Learning
Challenges
and
Opportunities
in
the
African
Agricultural
Sector
--
A
General
Perspective.”
ArXiv:2107.05101
[Cs],
July
2021.
arXiv.org,
http://arxiv.org/abs/2107.05101
Data availability
Data capability
• Most African countries have a low data availability index.
In addition to the potential data accessibility issues.
• 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.

7. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
Enriching the input Data – Satellite images.
Figure
6.
True
color
(RGB)
Sentinel
2
scene
in
northern
Egypt.
Figure
7.
Unsupervised
classification
using
Sentinel
2
spectral
layers
on
the
same
scene.
Figure
4.
Main
image:
RGB
satellite
image
(Sentinel
2)
with
its
red
band.
Source:
R.Ly
Figure
5.
Sentinel
2
NDVI
of
the
scene
in
image
4.
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 area of data acquisition is revisited
several times a year allowing to monitor
change on the ground.

8. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
Enriching the input Data – Satellite images.
Figure
8.
Sildir,
Hasan
&
Aydin,
Erdal
&
Kavzoglu,
Taskin.
(2020).
Design
of
Feedforward
Neural
Networks
in
the
Classification
of
Hyperspectral
Imagery
Using
Superstructural
Optimization.
Remote
Sensing.
12.
10.3390/rs12060956.
• Every object on Earth has a specific
spectral signature that depends on its
characteristics.
• The derivation of objects’ signatures
allows to identify everywhere else and
monitor changes.

9. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
Enriching the input Data – The NDVI example.
For AAgWa, we use on Vegetation index,
Rainfall, and Land surface Temperature
as geo-biophysical variables.
Secondary use cases:
• Anomaly detection,
• Seasonality shift,
• Crop growth stage.
Location: Cropland in Sare Bidji - Kolda
2002
Location: Ngor – Dakar (Urban)
2003
2009
2010
2011
2017
2018
2019
Figure
9.
Normalized
Difference
Vegetation
Index
(NDVI)
sample
data
visualization
–
smooth
values
for
a
crop
lands
and
noisy
signal
for
Urban
areas.
Source:
Author
(2019)

10. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
Enriching the input Data – Satellite images.
Figure
10.
Data
Cube
representation
retrieved
from
Mahecha,
M
et
al.,:
Earth
system
data
cubes
unravel
global
multivariate
dynamics,
Earth
Syst.
Dynam.,
11,
201–234,
https://doi.org/10.5194/esd-11-201-2020,
2020.
• 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.

11. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
Leveraging predictability
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
Prediction
s
Training
Phase
Predictions
The Africa Crop Production (AfCP) Model
The AfCP
model
publication.

12. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
Leveraging predictability - Visualization
Figure
11.
2023
Cameroon
Maize
production
forecasts.
Source:
The
Africa
Agriculture
Watch
–
https://www.aagwa.org.
Remote sensing products can reduce the data gap by providing
timely, disaggregated, and accurate agricultural statistics.
• Still, satellite images use requires processing, cleaning, and
mapping expertise.
Machine learning can produce forecasts and knowledge and
reduce uncertainties in decision-making.
• Still, it requires field-level expertise: model choice, model
architecture, loss function choice, hyper-parameters
tunning, training, validation, …

13. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
AAgWa Conceptual Framework
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.

14. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
AAgWa main and upcoming products
Community level crop
production forecasts
Potential Agricultural
land estimates.
Ready-to-use VIs
maps and time-series
data.
Ready-to-use land
temperature maps
and time-series data.
GHGs emissions’
measurements above
croplands.
Thematic fast-
mapping tool (to be
launched).
Crop type mapping.
Publications
Historical data (20
years period
computations)

15. www.aagwa.org 15

16. Leveraging Artificial Intelligence (AI) & Satellite Remote
Sensing Data for Decision-making in the African
Agricultural Sector
AFRICA AGRICULTURE WATCH
(AAgWa)
THANK YOU!