| Machine learning for plant disease incidence and severity measurements from leaf images G Owomugisha, E Mwebaze 2016 15th IEEE international conference on machine learning and applications …, 2016 | 108 | 2016 |
| Divergence-based classification in learning vector quantization E Mwebaze, P Schneider, FM Schleif, JR Aduwo, JA Quinn, S Haase, ... Neurocomputing 74 (9), 1429-1435, 2011 | 74 | 2011 |
| Automated vision-based diagnosis of banana bacterial wilt disease and black sigatoka disease G Owomugisha, JA Quinn, E Mwebaze, J Lwasa International conference on the use of mobile ICT in Africa, 1-5, 2014 | 50 | 2014 |
| iCassava 2019 fine-grained visual categorization challenge E Mwebaze, T Gebru, A Frome, S Nsumba, J Tusubira arXiv preprint arXiv:1908.02900, 2019 | 47 | 2019 |
| Modeling and monitoring crop disease in developing countries J Quinn, K Leyton-Brown, E Mwebaze Proceedings of the AAAI Conference on Artificial Intelligence 25 (1), 1390-1395, 2011 | 39 | 2011 |
| Automated Vision-Based Diagnosis of Cassava Mosaic Disease. JR Aduwo, E Mwebaze, JA Quinn ICDM (Workshops), 114-122, 2010 | 37 | 2010 |
| A land use regression model using machine learning and locally developed low cost particulate matter sensors in Uganda ES Coker, AK Amegah, E Mwebaze, J Ssematimba, E Bainomugisha Environmental Research 199, 111352, 2021 | 25 | 2021 |
| Prototype-based classification for image analysis and its application to crop disease diagnosis E Mwebaze, M Biehl Advances in Self-Organizing Maps and Learning Vector Quantization …, 2016 | 21 | 2016 |
| Divergence based learning vector quantization E Mwebaze, P Schneider, FM Schleif, S Haase, T Villmann, M Biehl learning 1 (2), 3-4, 2010 | 20 | 2010 |
| Ontology boosted deep learning for disease name extraction from Twitter messages MA Magumba, P Nabende, E Mwebaze Journal of Big Data 5, 1-19, 2018 | 18 | 2018 |
| An image-based diagnosis of virus and bacterial skin infections F Tushabe, E Mwebaze, F Kiwanuka The International Conference on Complications in Interventional Radiology, 1-7, 2011 | 17 | 2011 |
| A new approach for microscopic diagnosis of malaria parasites in thick blood smears using pre-trained deep learning models R Nakasi, E Mwebaze, A Zawedde, J Tusubira, B Akera, G Maiga SN Applied Sciences 2, 1-7, 2020 | 16 | 2020 |
| Machine learning for diagnosis of disease in plants using spectral data G Owomugisha, F Melchert, E Mwebaze, JA Quinn, M Biehl International Conference on Artificial Intelligence (ICAI), 9-15, 2018 | 16 | 2018 |
| Crowdsourcing real-time viral disease and pest information: A case of nation-wide cassava disease surveillance in a developing country D Mutembesa, C Omongo, E Mwebaze Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 6 …, 2018 | 15 | 2018 |
| Combining dissimilarity measures for prototype-based classification. E Mwebaze, G Bearda, M Biehl, D Zühlke ESANN, 2015 | 14 | 2015 |
| Causal structure learning for famine prediction E Mwebaze, W Okori, JA Quinn 2010 AAAI Spring Symposium Series, 2010 | 14 | 2010 |
| Mobile-aware deep learning algorithms for malaria parasites and white blood cells localization in thick blood smears R Nakasi, E Mwebaze, A Zawedde Algorithms 14 (1), 17, 2021 | 11 | 2021 |
| Matrix relevance learning from spectral data for diagnosing cassava diseases G Owomugisha, F Melchert, E Mwebaze, JA Quinn, M Biehl IEEE Access 9, 83355-83363, 2021 | 9 | 2021 |
| Early detection of plant diseases using spectral data G Owomugisha, E Nuwamanya, JA Quinn, M Biehl, E Mwebaze Proceedings of the 3rd International Conference on Applications of …, 2020 | 9 | 2020 |
| Improving in-field cassava whitefly pest surveillance with machine learning JF Tusubira, S Nsumba, F Ninsiima, B Akera, G Acellam, J Nakatumba, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 7 | 2020 |
