Busitema AI & Interdisciplinary Research Group

Lead interdisciplinary research that utilises AI algorithms to solve day to day human problems

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About Us


Center of excellence for innovative AI Engineering research solutions


Lead interdisciplinary research that utilises AI algorithms to solve day to day human problems


Health care, agriculture and climate change, natural sciences, surveillance, computer vision, robotics, transportation among others.


Tools for Early Disease Detection & Monitoring

griculture is the backbone of many African economies, employing over 60% of the workforce and accounting for about 20% of the continent’s GDP.

IOT Applications for Remote Sensing

leverages smartphone, Internet of Things (IoT) and machine learning techniques

IOT for medical Healthcare Systems

Developing architecture/platform for IoT-based Healthcare Heterogeneous Traffic considering resource constraints in developing countries

Skin Disease Diagnosis

Developing algorithms for skin lesion analysis.



Godliver Owomugisha, Joyce Nakatumba-Nabende, Joshua Jeremy Dhikusooka, Estefania Taravera, Ephraim Nuwamanya and Ernest Mwebaze,

“A labeled spectral dataset with cassava disease occurrences using virus titre determination protocol” Data in Brief, 49, 109387, 2023, Paper


Joshua Jeremy Dhikussoka, Ephraim Nuwamanya, Estefania Talavera Martinez and Godliver Owomugisha*,

“A Light Spectrometer Device for Crop Disease Monitoring”, 11th International Conference on Learning Representations, Kigali Rwanda, 2023. Paper


J. Omara, E. Talavera, D. Otim, D. Turcza, E. Ofumbi, G. Owomugisha

“A field-based recommender system for crop disease detection using machine learning”, Frontiers in Artificial Intelligence, vol. 6, 2023. Paper


Owomugisha, Godliver and Nakatumba-Nabende, Joyce and Dhikusooka, Joshua Jeremy and Talavera, Estefanía and Nuwamanya, Ephraim and Mwebaze, Ernest,

A Labeled Spectral Dataset with Cassava Disease Occurrences Using Virus Titre Determination Protocol. Available at SSRN. Paper


Mafukidze HD, Owomugisha G, Otim D, Nechibvute A, Nyamhere C, Mazunga F.

Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation. Applied Sciences. 2022; 12(17):8412. https://doi.org/10.3390/app12178412. Paper


Owomugisha, G., Melchert, F., Mwebaze, E., Quinn, J. A., & Biehl, M. (2021).

Matrix relevance learning from spectral data for diagnosing cassava diseases. IEEE Access, 9, 83355-83363. Paper


Godliver Owomugisha, Ephraim Nuwamanya, John A. Quinn, Michael Biehl, and Ernest Mwebaze. 2020.

Early detection of plant diseases using spectral data. In Proceedings of the 3rd International Conference on Applications of Intelligent Systems (APPIS 2020). Association for Computing Machinery, New York, NY, USA, Article 26, 1–6. https://doi.org/10.1145/3378184.3378222. Paper


Godliver Owomugisha, Pius K. B. Mugagga, Friedrich Melchert, Ernest Mwebaze, John A. Quinn, and Michael Biehl. 2020.

Low-cost 3-D printed smartphone add-on spectrometer for diagnosis of crop diseases in field. In Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS ‘20). Association for Computing Machinery, New York, NY, USA, 331–332. https://doi.org/10.1145/3378393.3402252. Paper

Contact Us

Jinja - Malaba Road
P.O BOX 236,
Tororo - Uganda


+012 345 67890