1. Improved Weather Prediction for Sustainable Agriculture
In order to improve agricultural production in Africa, it is important to improve the accuracy of weather forecasts available to small scale farmers. Agriculture in Africa is mainly rain fed and accurate rainfall prediction is likely to improve yields by allowing farmers to appropriately time activities such as planting and also help them select appropriate crops to grow.
Goal: In this project we will leverage weather data collected by large networks of sensors to improve weather prediction by linking these data with new artificial intelligence based weather prediction models running on powerful computers. The key goal is to provide accurate medium to long range localised precipitation forecasts.
Partners: This work will bring together
Data Science Africa (DSA), the
Centre for Data Science and Artificial Intelligence (DSAIL) at Dedan Kimathi University of Technology in Kenya,
Amini, a Kenyan climate startup and
Communal Shamba Coffee.
2. Early diagnosis of plant diseases through spectroscopy
About 40% of the global crop production is lost to pests. Sub-Saharan Africa is most vulnerable to the increasing risks of pests and diseases spreading in agriculture. The current methods of disease identification and diagnosis involve experts traveling to disparate parts of the country and visually scoring the plants by looking at the disease symptoms manifested on the leaves.
Goal: The research project investigates a 3-D printed smartphone add-on spectrometer that determines the state of disease in plants before it is visibly symptomatic. Portable devices for the early detection of crop diseases are needed to support the farmers and experts working in the field. The output of this tool is integrated into a smartphone in the form of an app, making it accessible for use in the field in real applications.
Partners: This project will be executed by
Data Science Africa (DSA), and
ETH Zurich.
3. Ethical AI for Humanitarian Action - Developing Tailored LLMs
The International Committee of the Red Cross (ICRC) seeks to leverage Large Language Models (LLMs) to enhance its humanitarian work. Challenges like the bias of existing models, the underrepresentation of humanitarian contexts in commercial AI training sets, and the sensitivity of data related to conflicts limit the adoption of off-the-shelf AI models.
Goal: To address these constraints, the ICRC will investigate the responsible development of LLMs specifically tailored to its mandate on international humanitarian law and in line with its protection work in favor of people affected by armed conflicts. Ethical and accountability considerations will be at the center of this project, with the development of tools to assess if developed models are safe to be used for humanitarian applications.
Partners: This project will be executed by
ICRC (DSA), and
ETH Zurich.