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