AI Research applied to African problems
An AI & Data Science Research Group based at Makerere University, Uganda
ABOUT MAKERERE AI LAB
The AI and data science research group at Makerere University specialises in the application of artificial intelligence and data science -including, for example, methods from machine learning, computer vision and predictive analytics-to problems in the developing world.
Applications: natural language processing for under-resourced languages, automated diagnosis of both crop and human diseases, auction design for mobile commodity markets, analysis of traffic patterns in African cities, and of telecoms and remote sensing data for anticipating the spread of infectious diseases
VISION
Excellence in Artificial Intelligence research for accessible solutions.MISSION
To advance artificial intelligence research to solve real-world challenges.
Portfolio
The Varied projects of Makerere AI lab
- Machine Learning Datasets for crop Diseases: Imagery and Spectrometry Data
- Image phenotyping for necrosis in cassava roots
- RCROPS
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Machine Learning Datasets for crop Diseases: Imagery and Spectrometry Data
The project aims to deliver open, accessible, and quality machine learning datasets for crop
pests and disease diagnosis based on crop imagery and spectrometry data from Uganda,
Tanzania, Namibia, and Ghana. The development of beneficial and effective real-world
machine-learning applications require localized and labeled pest and disease datasets. This
project will provide these appropriate image datasets for food security crops grown in sub-
Saharan Africa: Cassava, Maize, Beans, Bananas, Pearl Millet, and Cocoa. In collaboration
with the national agricultural experts, this study will deliver on a two-way data set approach
for crop pests and diseases: (a) A field-level Geo-coded and time-stamped dataset of 145,000
images representing diseased and healthy cassava, maize, beans, bananas, pearl millet, and
cocoa crops. (b) A dataset of 8160 cassava spectra and 2000 spectra points of maize and pearl
millet representing disease manifestations before symptoms are visibly seen by the human eye.
Image phenotyping for necrosis in cassava roots
We automate necrosis phenotyping with more efficiency than current methods



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RCROPS
We use artificial intelligence to mine data from local village radio stations to generate timely data on crop pests and disease in sub-Saharan Africa. Crop loss due to pests and disease threatens the economic survival of smallholder farmers, and access to surveillance data is critically important yet often not affordable. Local radio shows are a powerful source of information flow in rural African villages: they cover topics including politics, policy, climate, and social circumstances, in addition to crop concerns. Collectively, this information provides a holistic representation of current events in these communities. They will analyze local broadcasts to generate crop surveillance data that is linked to the local community situation.Radio content will be collected at low cost through a collaboration with Pulse Labs Kampala, and they will build artificial intelligence models based on deep neural networks and keyword identification to mine the data.The results will be combined with photographs of diseased crops provided by local farmers and used to train machine learning models to ultimately extract radio information in multiple languages and with diverse accents. This project will provide near real-time crop surveillance data and allow for timely responses to threats.
OUR PEOPLE
WE ARE A COLLABORATIVE TRANSDISCIPLINARY, AND DIVERSE TEAM WITH A BIAS FOR APPLYING MACHINE LEARNING TO SOLVE PROBLEMS IN THE DEVELOPING WORLD.
JOYCE NABENDE (PhD)
Head of the Lab

MUTEMBESA DANIEL (PhD Student)
Research Scientist

GLORIA NAMANYA
Research and Administrative Assistant

CLAIRE BABIRYE
Research Assistant

SOLOMON NSUMBA
Research Software Engineer

JEREMY TUSUBIRA
Research Software Engineer

GODLIVER OWOMUGISHA
Research Scientist

JONATHAN MUKIIBI
Research Assistant

LILIAN NABUKEERA
Administrator

BENJAMIN AKERA
Research Assistant

EVENTS
UPCOMING EVENTS , SEMINARS AND WORKSHOPS
Topic: Lesion analysis in necrotic cassava roots
Presenter: Claire Babirye
Time: Feb 25, 2021 10:00 AM Nairobi
Join Zoom Meeting
Meeting ID: 962 2824 4771
Passcode: 729565

A Makerere University Initiative.