With the growth of world population, much more effort and innovation will be urgently needed in order to sustainably increase agricultural production, improve the global supply chain, decrease food waste, and ensure access to nutritious food to all vulnerable people essentially in developing countries and Africa. Sustainable agriculture is related to The Sustainable Development Goal to “End hunger, achieve food security and improve nutrition and promote sustainable agriculture” (SDG2).
In this context, crop type mapping is a major challenge for agricultural and environmental policy makers. The recent growth of open Satellite imagery time series allows large scale crop mapping.
The objective of the challenge is to use Sentinel-2 multispectral time series to classify crops in Central Tunisia, specifically in the Kairouan agricultural region.
The ground truth reference data was collected in the field by the AGEOS Team. The challenge datasets were processed by IEEE Sup'com GRSS Chapter Members.
- Numpy
- Pandas
- matplotlib
- seaborn
- sklearn
- RandomForestClassifier
- Look for the team name : Randomize
- Rank : 9/77 🥉
- Competition link : Zind
| Name | Zindi ID |
|---|---|
| Salim Ben hammadi | milaSneB |
| Karim Omrane | mokhiferagh |