African Data Science Platform Hosting US$8 000 Competition In Partnership With Microsoft

We’ve written about Zindi before, the platform puts up AI/Data science problems that they pay African’s for solving. It’s quite the platform and if you’re an AI professional (or fanatic), it’s something you need to closely follow.

The platform has partnered with Microsoft and Wazihub to advance the use of tech for agricultural resilience and adaptation in Africa through the Wazihub Soil Moisture Prediction Challenge which is now available on Zindi.

Zindi hosts a community of over 4,000 data scientists dedicated to solving Africa’s toughest business and social challenges through machine learning and artificial intelligence. In the face of climate change and food insecurity across the globe, we are thrilled to launch this partnership with Wazihub and Microsoft and make strides to more efficient use of our natural resources and local talent.

Zindi CEO – Celina Lee

Data scientists will compete to build machine learning models that accurately predict soil moisture levels of agricultural plots multiple days in advance. Submissions can be made until 20 October 2019.

Sensor-based irrigation technology, combined with machine learning algorithms, can help farmers efficiently manage their water usage. The data for this challenge was collected by Wazihub in maize and peanut farms in Senegal using low-cost internet of things (IoT) sensors.

We are working to create a Pan-African IoT startup ecosystem across 20 countries in Africa with a vision to solve local problems using local resources and skills, to keep the solution cost effective

Dr. Abdur Rahim – Managing Director of Wazihub

Can you participate if you’re in Zim?

The competition on Zindi is open to anyone based anywhere, however, in order to promote data science on the continent, the prizes will only be awarded to participants that are based in Africa. The top three winners will win cash prizes, and the top five winners will win IoT kits and a gateway (the same hardware used in this experiment).

Enter the Wazihub Soil Moisture Prediction Challenge

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