A presentation by Dr. Racine Ly, Director, Data Management, Digital Products and Technology at AKADEMIYA2063

1. Food Crop Production Forecasting in Times of Crisis
The Role of Remote Sensing and Artificial Intelligence
Racine Ly, PhD.
Director, Data Management, Digital Products and Technology
AKADEMIYA2063 | The Expertise We Need. The Africa We Want.
Kicukiro/Niboye KK 360 St 8, P.O. box 4729, Kigali-Rwanda
AKADEMIYA2063 Covid-19 Webinar Series
Racine Ly - AKADEMIYA2063 Covid-19 Webinar Series, September 10th, 2020

2. Racine Ly - AKADEMIYA2063 Covid-19 Webinar Series, September 10th, 2020
Outline
1. Introduction and Context
2. Remote Sensing Products and Third-party maps
3. Machine Learning Predictive Modeling Framework
4. Results – The case of DRC, Senegal, and Burkina Faso
5. Conclusion and Perspectives
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3. Racine Ly - AKADEMIYA2063 Covid-19 Webinar Series, September 10th, 2020
1. Introduction and Context
Covid-19 Shock and Potential Risks in Agriculture
• Covid-19 is second to none in terms of crisis with significant impacts on the global economy.
• While official reports position Africa as less impacted in terms of cases, the consequences in
the economy suggest another narrative.
• In the agricultural sector, the fear is that the outbreak could morph into a food crisis due to
disruptions in the food value chain.
• High potential risks include food protectionism from exporting countries, potential labor
scarcity, late reception and use of imported seeds and fertilizers, among others.
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4. Racine Ly - AKADEMIYA2063 Covid-19 Webinar Series, September 10th, 2020
1. Introduction and Context
Climate-related Shocks and Food Crop Production
• We are witnessing climate-related disruptions such as heavy rain in several western African
countries: Nigeria, Niger, Cameroon, and Senegal.
• Two main potential issues for food crop production:
- Heavy rains can be as equally harmful to crops as droughts;
- Flooded human settlements can draw farmers’ attention from cropping, to tackle
more “urgent” matters.
• Crop calendars are – or could soon be – irrelevant and that could yield little to no agricultural
productivity.
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5. Racine Ly - AKADEMIYA2063 Covid-19 Webinar Series, September 10th, 2020
1. Introduction and Context
Demographic Surge Pressure on Agriculture
• Demographic forecasts suggest Africa to be home of approx. 1.68 billion people by 2030.
• The general rationale to increase agricultural productivity is usually derived from the
misalignment between food crop production and the increasing population.
• In this presentation, the debate is not whether Agriculture has the capacity to provide the
amount of food needed nor the ability of farmers to produce it.
• The overarching question is our capacity to combine knowledge-led agriculture and data-
driven approaches to improve and sustain agricultural productivity.
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6. Racine Ly - AKADEMIYA2063 Covid-19 Webinar Series, September 10th, 2020
1. Introduction and Context
Key word: UNCERTAINTY
• There is a need for better anticipation at each decision level: from farmers to decision and
policy-makers.
• There is a need for better spatial and temporal overview of trend patterns.
• There is a need to make the invisible, visible.
• Current technological advances can make that possible.
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7. Racine Ly - AKADEMIYA2063 Covid-19 Webinar Series, September 10th, 2020
1. Introduction and Context
Remotely Sensed Data and Machine Learning Techniques
• Satellite images allow us to scrutinize features on earth such as agricultural lands with high
spatial and temporal resolutions.
• Machine learning techniques (supervised) are suitable to “learn” historical datasets and to
predict future outcomes.
• Our team of Data Scientists combined both remote sensing products retrieved from satellite
images and machine learning techniques to forecast food crop production in several African
countries.
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8. Racine Ly - AKADEMIYA2063 Covid-19 Webinar Series, September 10th, 2020
2. Remote Sensing Products and Third-party Maps
Input layers (NDVI, Land Temperature, Rainfall, Harvested Areas)
• To be as close as possible to crop growing
inputs, we considered biophysical
and climate-related parameters.
• Each map has been filtered with crop masks
to target specific crops’ locations (IFPRI,
MapSPAM).
• Production maps at pixel level were retrieved
from MapSPAM portal, and used as response
variable for the model.
Figure 1. Key inputs of the food crop production forecast model. (top-left): MODIS Normalized Difference Vegetation
Index; (top-right): MODIS Daytime Land Surface Temperature (LST); (bottom-left): CHIRPS Rainfall; (bottom-right):
Millet harvested areas in 2017 (IFPRI, MapSPAM). Pixels are of size 10 by 10 km. Map source: Ly & Dia, 2020.
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9. Racine Ly - AKADEMIYA2063 Covid-19 Webinar Series, September 10th, 2020
2. Remote Sensing Products and Third-party Maps
Data Pre-processing
• ASSEMBLE Tiles are mosaiced to cover a specific country for 2005, 2010, and 2017.
• CLEAN Unreliable or nodata pixels are deleted from features’ map
• AVERAGE Average pixels’ values (annual) is computed during the crop growing season
• FLATTEN Each averaged map for each feature is flattened (vector)
• CONCATENATE Flattened maps (vector) for 2005, 2010, and 2017 are concatenated
• STACK Concatenated vectors are stacked to form a matrix (each line is a scenario)
Example: Such process leads to 14,000 scenarios to be ”learned” as examples by the algorithm
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10. Racine Ly - AKADEMIYA2063 Covid-19 Webinar Series, September 10th, 2020
3. ML Predictive Modeling Framework
Training process
• The food crop production model was built with an artificial neural network to find a
relationship between explanatory and response variables.
• Two hidden layers with 50 neurons each were selected
• The dropout technique was used to mitigate overfitting
• The mean squared error was selected as loss function.
• The entire data processing and modeling work was handled with in-house algorithms built
with Python.
• The average Root Mean Square Error was o.o8.
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11. Racine Ly - AKADEMIYA2063 Covid-19 Webinar Series, September 10th, 2020
3. ML Predictive Modeling Framework
Predicting Inputs – Example with NDVI
• At the onset of the growing season, maps are usually not
available from remote sensing products providers.
• To avoid building the model at an advanced stage of the growing
season, we also predict inputs’ values by regression.
• Since historical features’ values are available, they were used as inputs
into a random forest regressor.
• The outcomes are then used as inputs to the food crop production
model for the year 2020.
Figure 2. (Top): Observed and predicted mean NDVI on a test
set for Senegal - A random pixel has been selected for the graph.
(Bottom): Historical mean NDVI values and predicted ones for
the 2020 season. Green areas correspond to empirical
confidence intervals at 95%.
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12. Racine Ly - AKADEMIYA2063 Covid-19 Webinar Series, September 10th, 2020
4. Results – The case of DRC, Senegal, and Burkina Faso
The Democratic Republic of Congo - Yam
• According to the FAO, yam production in DRC was about
91,000 metric tons in 2017. Our model predicts an
increase of 2% in 2020.
• 2020 yam production is on average higher than the 2017
production for most of the areas.
• The highest changes (increase) are expected in the
Northwest and southern regions of the country.
• The area of Kinshasa shows the lowest predicted
variation in production.
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13. Racine Ly - AKADEMIYA2063 Covid-19 Webinar Series, September 10th, 2020
4. Results – The case of DRC, Senegal, and Burkina Faso
Senegal - Millet
• Our model predicts a production level of 512,623 tons
for 2020 compared to 579,104.6 tons in 2017 (MapSPAM)
• Our predicted level of production shows a decrease
of nearly 12%, although still within the range of
average annual production.
• Most of the decline is anticipated in areas around the
groundnuts basin (orange color).
• The increase is in millet growing zones
(east and southern parts)
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14. Racine Ly - AKADEMIYA2063 Covid-19 Webinar Series, September 10th, 2020
4. Results – The case of DRC, Senegal, and Burkina Faso
Burkina Faso - Sorghum
• On average Sorghum production in Burkina Faso in 2020
will be slightly better than in 2017.
• Northwest, Central and Southern regions are the areas
where an increase in production is expected.
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15. Racine Ly - AKADEMIYA2063 Covid-19 Webinar Series, September 10th, 2020
5. Conclusion and Perspectives
• The greater spatial disaggregation makes it possible to devise more targeted policies for
increased impact in terms of protecting the most vulnerable communities in areas where the
sharpest decrease in production may be expected.
• The machine learning predictive modeling framework allows us to mine the data without any
initial hypotheses. Though greater attention should be payed to adding more features.
• Our aim is to extend the work for other African countries: the team is actively producing the
predicted maps along with growing conditions and yield shift maps.
• More agricultural-relevant layers are being considered to keep improving the model.
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16. Racine Ly - AKADEMIYA2063 Covid-19 Webinar Series, September 10th, 2020
THANK YOU