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Food Crop Production during the COVID-19 Pandemic
The Case of Six Western African Countries
Côte d’Ivoire, Mali, Burkina F...
Outline
1. Introduction & Context
2. Remotely Sensed Data
3. Machine Learning Framework
4. Food Crop Production Model
5. R...
1. Introduction & Context
• Measures taken to mitigate the COVID-19 propagation put a heavy strain onto the
agricultural s...
1. Introduction & Context (Cont’d)
Better agricultural sector data through remote sensing and artificial intelligence
• Th...
2. Remotely Sensed Data
• Remotely sensed data through sat. images provide a wealth of information about
features on earth...
2. Remotely Sensed Data (Cont’d)
Application to our Food Crop Production Model
• Vegetation (crops) only absorb specific w...
Figure 4. Senegal Millet
Production (left) 2005;
Middle 2010; (Right) 2017).
Data Source: IFPRI, 2020,
Map Source: Ly et a...
4. Food Crop Production Model
Training Scheme
NDVI
LST
RAIN
2005
2010
2017
2005
2010
2017
2005
2010
2017
Crop Masks
2005
2...
5. Results
5. Results (Cont’d)
5. Results (Cont’d)
THANK YOU
AKADEMIYA2063 – Kicukiro / Niboye KK 360 St 8 I P.O. Box 4729 Kigali-Rwanda
FIND MORE COVID-19 RELATED WORK at
h...
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AKADEMIYA2063-Ecowas Regional Learning event: Food Crop Production during the COVID-19 Pandemic

The Case of Six Western African Countries: Cote d'Ivoire, Mali, Burkina Faso, Sierra Leone, The Gambia, and Senegal

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AKADEMIYA2063-Ecowas Regional Learning event: Food Crop Production during the COVID-19 Pandemic

  1. 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. 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. 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. 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. 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. 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. 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. 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. 9. 5. Results
  10. 10. 5. Results (Cont’d)
  11. 11. 5. Results (Cont’d)
  12. 12. THANK YOU AKADEMIYA2063 – Kicukiro / Niboye KK 360 St 8 I P.O. Box 4729 Kigali-Rwanda FIND MORE COVID-19 RELATED WORK at https://akademiya2063.org/covid-19.php

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