YEESI Lab team from Sokoine University of Agriculture participated in the UmojaHack competition (https://umojahack.africa/). A student from YEESI Lab, Adam Jamali, a 3rd-year BSc. in Irrigation and Water Resources Engineering, placed third in the competition. The winners can be found at this link: https://zindi.africa/competitions/umojahack-africa-2023-beginner-challenge/leaderboard
Among other teams that we had, YEESI Lab had a team formed by three students (Mr Dickson Massawe (4th-year BSc in Agricultural Engineering), Mr. Fikiri Matatizo (2nd-year BSc with Education - Physics and IT) and Mr George Munishi (4th-year BSc. Agricultural Engineering). This team also did well with outstanding flying colours. Also, Stephano Mashauri, a 2nd-year BSc. Agricultural Engineering student placed seventh in the competition. Mr. Jacob Shimba, a third-year BSc. Agricultural Engineering student also participated well in the competition. The students were supervised closely by Mr. Deus Francis from the Department of Informatics and Information Technology. The YEESI Lab team did well in all the competitions (hackathons) attended by different students from universities in Tanzania as shown in the public leaderboard http://zindi-metabase-v1.azurewebsites.net/public/dashboard/b9115882-27e0-4654-9f40-31beece000da
Most of our students’ utilize YEESI Lab public shared Computing Node (http://yeesi.sua.ac.tz/) to train the models.
Congratulations to our hard-working students
The hackathon has different competitors from 30 countries in Africa, 300 universities and 3000 students. It was done online for two days from 18-19 March 2023. The students were hosted at the YEESI Lab premises at the Electronics and Precision Agriculture Lab, School of Engineering and Technology. The competition was done in form of challenge based learning. It was about a real-world problem on Carbon Dioxide Prediction.
The Description of the Challenge
The ability to accurately monitor carbon emissions is a critical step in the fight against climate change. Precise carbon readings allow researchers and governments to understand the sources and patterns of carbon mass output. While Europe and North America have extensive systems in place to monitor carbon emissions on the ground, few are available in Africa.
The objective of the challenge is to create machine learning or a deep learning model using open-source CO2 emissions data (from Sentinel-5P satellite observations) to predict carbon emissions. These solutions will enable governments, and other actors to estimate carbon emission levels across Africa, even in places where on-the-ground monitoring is not possible.
For more information, Visit: https://www.yeesi.org/news
Share this page
