The Use of Earth Observation for Food Crop Production Systems Transformation

1. Machine Learning Specialist, AKADEMIYA2063
The Use of Earth Observation for Food Crop
Production Systems Transformation.
The Case of Crop mapping for Rwanda, and Senegal
Dr. Jean Paul l. FAYE

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Outline
I. Introduction
II. Satellite Remote Sensing Data and Machine Learning for
Crop mapping
III. Maize Mapping: Case Nyagatare, Rwanda
IV. Groundnut Mapping: Case Groundnut Basin, Senegal
V. Conclusion

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Introduction
 Agriculture plays a crucial role in Africa for several by contributing
significantly to the continent's economy, food security,
employment, and overall development.
 However, agriculture is highly dependent on weather conditions,
and many African countries are vulnerable to the impacts of
climate change.
 Developing resilient and sustainable agricultural practices is
crucial for adapting to changing climate patterns and ensuring
long-term food security. Thus, innovation approaches are needed
for achieving sustainable and resilient agricultural systems in
Africa.
 The utilization of technologies such as machine learning and
earth observation has gained a lot of attention and are very
promising for powering the agriculture in the very near future.

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Satellite Remote Sensing Data and
Machine Learning for Crop mapping

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• Satellite uses sensors that collect
information about the Earth's surface
without actively emitting any signals
• Sensors passively record the sunlight
reflected or emitted by the Earth's
surface in various wavelengths
• Satellite uses sensors that actively emit
signals or energy towards the Earth's
surface and measure the reflected or
scattered signals
Satellite Remote Sensing Data

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Spectral bands refer to the specific ranges of EM radiation that sensors capture
Electromagnetic Spectral

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Spectral bands refer to the specific ranges of EM radiation that sensors capture
Coastal Blue Green
Red
NIR: vegetation
Panchromatic
Shortwave IR
Thermal IR
Shortwave IR
Spectral Bands

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Machine Learning: Supervised and Unsupervised Learning
Machine Learning is a complex of algorithms and methods that address
the problems of Classification, Clustering, and Forecasting.
Supervised Learning
• From training data set 𝑥𝑖, 𝑦𝑖 , we want
to learn 𝑓 such that 𝑦𝑖 = 𝑓 𝑥𝑖 .
• We want the model to generalize to
unseen inputs.
𝑓 𝑥𝑖
∗
= 𝑦𝑖
∗
for new data point 𝑥𝑖
∗
Unsupervised Learning
• From training data set 𝑥𝑖 , we want
to learn the structure of the data.
Ex. Clustering data in such that all data
belonging to the same group have the
same properties

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Random Forest Classifier
Maize
Soya
Support Vector Machine Classifiers
Machine Learning Models Used for Crop Mapping

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Dataset: Calculate the indices in different location
NDVI
NDVI
NDVI
NDVI
NDVI
NDVI
NDVI NDVI
NDVI
NDVI
Same for all the remaining indices
Polygone of cropland

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Machine Learning for Maize Mapping:
Case of Nyagatare, Rwanda

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Survey Data:

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Area of Interest and Data distribution

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Model Validation using the Test Dataset
20 percent of the generated
data are chosen for the
testing data
No Maize
Maize

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Using the Validated Model for pixel Classifications
• Zoom in an image with the correct
calculated band indices at the pixel
level.
• Pixel size is: 10𝑥10 = 100 𝑚2
• This image is given to the trained
model and the pixels are classified as
Groundnut or not.

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Maize mapping

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Maize mapping
Region of interest: circle of 25 km radius around
Nyagatare

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Machine Learning for Groundnut Mapping:
Case of Groundnut Basin, Senegal

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Data Collection
Senegal and its groundnut basin
Collected data distribution

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Groundnut mapping

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Survey vs Predicted Data distribution
Collected data distribution Predicted Probabilities data distribution

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Future Direction: Signature Transfer Between Countries
 Classify the signature of each specific crop into clusters for each country where data
has been collected and the machine model trained.
 The obtained database can be used for crop annotation in other countries:
• Collect remote sensing data and compute the same indices at the area of interest
• Do the clustering of indices
• Comparer with the database for annotation and pixel crop classification

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Conclusion
 We use an approach based on Satellite Remote Sensing Data and machine Learning
techniques for crop mapping
 Application of the model in different countries where data have been collected shows a
clear map of crops
 With more data collection in any country, we will be able, in any time of the year, to run
the model that will do the crop mapping for the entire country and tells us the crops and
the type of crops grown in that country
 The Crop Mapping output is one output, but the impact is multidimensional

24. THANK YOU