Help rural farmers in Africa predict soil humidity uisng sensor data from IoT.
Wazihub and Microsoft are partnering with Zindi Africa to create the Wazihub Soil Moisture Prediction Challenge competition. The competition seeks to create a machine learning model capable of predicting soil humidity. Using past data from the soil and getting results in a very short time.
Find out more about the Wazihub challenge
In the face of climate change, the agricultural sector in Africa needs to adapt its practices. Being able to accurately measure and predict soil humidity in their fields will allow farmers to prepare their irrigation schedules optimally and efficiently.
Sensor-based irrigation and machine learning algorithms can provide farmers with a solution to manage water usage more efficiently. However, current machine learning algorithms built on sensor data require a lot of data for proper training. Stable sensor data is difficult to obtain in rural Africa where many problems arise such as accessibility, limited battery power, lack of internet, humidity/heat problem.
The objective of this Zindi competition is to create a machine learning model. Capable of predicting the humidity for a particular plot in the next few days, using data from the past. A part of the challenge is to design algorithms that are resilient and can be trained with incomplete data. (E.g. missing data points) and unclean data (e.g. lot of outliers).
This resulting model will enable farmers to anticipate water needs and prepare their irrigation schedules.
This competition is sponsored by Wazihub[webpage] and Microsoft.
Kindly visit competition page for details and rules.