Classification of Fall Armyworm Damage, OKO Finance

Ari Salkow

Data Science Fellows February 2021 Cohort




The objective of the project was to build a machine learning classifier that could tell whether a specific piece of land in Mali had been eaten by a pest called The Fall Armyworm. The base model would be binary i.e. had been eaten or not. As a stretch target, the model should predict whether a piece of land had been eaten, and the severity of the damage. The data to be used was:

  • Appropriate freely available satellite imagery – a field called Remote Sensing.
  • Ground-truth data from the FAO – an organization that provides such information.

Challenges (at least two)

  1. To execute this. It was necessary to apply CV techniques that were a modified version of the ones we learnt at ITC. Through reading papers, watching videos, reading online and learning from co-workers, we greatly improved our understanding and ability for this sub-section of machine learning.
  2. There were quite a few libraries that we hadn’t encountered. Using the same ways as point 1., we learnt more about these libraries and approaches.

Achievements (according to KPIs)

  1. We understood this specific insurance industry and Remote Sensing much better by the end of the project.
  2. We built a segmentation model, albeit using different data, for multi-class image segmentation. This same approach could be applied with the libraries discussed above to build a model for this project, using the requisite data sources.
  3. Improved Python, data analysis and data science skills by working on the project and collaborating with the other team members from OKO and Omdena.


Future project development 

  • Various other models and modelling approaches should be tried to improve prediction accuracy.
  • Other data suppliers should be explored, to improve performance.
  • Perhaps additional data from existing OKO customers could be extracted.
  • More research papers, and similar, could be examined to get ideas for improvement.

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