Browsing by Author "Okello, Dorothy"
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Item A Deep Learning-based Detector for Brown Spot Disease in Passion Fruit Plant Leaves(arXiv preprint arXiv, 2020) Katumba, Andrew; Bomera, Moses; Mwikirize, Cosmas; Namulondo, Gorret; Ajeroy, Mary Gorret; Ramathaniy, Idd; Nakayima, Olivia; Nakabonge, Grace; Okello, Dorothy; Serugunda, JonathanPests and diseases pose a key challenge to passion fruit farmers across Uganda and East Africa in general. They lead to loss of investment as yields reduce and losses increases. As the majority of the farmers including passion fruit farmers, in the country are smallholder farmers from low-income households, they do not have sufficient information and means to combat these challenges. While, passion fruits have the potential to improve the well-being of these farmers given their short maturity period and high market value [1], without the required knowledge about the health of their crops, farmers can not intervene promptly to turn the situation around. For this work, we partnered with the Uganda National Crop Research Institute (NaCRRI) to develop a dataset of expertly labeled passion fruit plant leaves and fruits, both diseased and healthy. We made use of their extension service to collect images from five districts in Uganda to create the dataset. Using the dataset, we are applying state-of-the-art techniques in machine learning, specifically deep learning at scale for object detection and classification for accurate plant health status prediction. While deep learning techniques have been applied to various disease diagnosis contexts with varying degrees of success([2], [3], [4], [5], [6]), there has not been any significant effort, to the best of our knowledge, to create a dataset or apply machine learning techniques to passion fruits despite their obvious financial benefits. With this work, we hope to fill this gap by generating and making publically available an image dataset focusing on passion fruit plant diseases and pest damage and training the first generation of machine learning-based models for passion fruit plant disease identification using this dataset. The initial focus is on the locally prevalent woodiness (viral) and brown spot (fungal) diseases.Item Effect of Different Organic Substrates on Reproductive Biology, Growth and Offtake of the African Night Crawler Earthworm (Eudrilus eugeniae)(Saly Portudal, Senegal, 2018) Kabi, Fred; Kayima, Denis; Kigozi, Abasi; Mpingirika, Eric Zadok; Kayiwa, Ronald; Okello, DorothyRapid growth and high fecundity of Eudrilus eugeniae makes it a commercial vermicomposting agent. The worm is also a rich protein source (50-70%CP) in livestock diets. The major question, however, is how do we promote earthworm production as a strategy for ecological livestock intensification and integration with crops through earthworm domestication as a source of protein and vermicompost. Reproduction characteristics, growth and offtake of E. eugeniae were studied using four organic substrates including abattoir waste (AW), cattle manure (CM), soya bean crop residue (SBCR) and a mixture of cattle manure and soya bean crop residue (CM+SBCR) aged 15 days. Irrespective of the substrate, length and biomass of earthworms increased at a decreasing rate between the 1st and 8th weeks. Clitellum appearance was initiated at 31.5±2.4, 32.8±3.2, 33.7±3.3 and 35.5±2.4 days for AW, CM,CM+SBCR and BCR, respectively, while cocoon initiation was at 69.0±1.4 (AW), 54.9±2.3 (CM), 51.7±1.7 (CM+SBCR) and 60.0±2.4 (SBCR) days. Organic substrate used affected reproductive biology,Item Raising Awareness for Potential Sustainability Effects in Uganda: A Survey-based Empirical Study(CEUR-WS, 2020) Penzenstadler, Birgit; Duboc, Leticia; Hebig, Regina; Dearden, Andy; Kanagwa, Benjamin; Chaudron, Michel; Bainomugisha, Engineer; Umuhoza, Eric; Okello, DorothyIn July 2019, we ran the 3rd International BRIGHT summer school for Software Engineering and Information Systems at the Makerere University in Kampala, Uganda. The participants developed a group project over the course of the week, which included the application of the Sustainability Awareness Framework. The framework promotes discussion on the impact of software systems on sustainability based on a set of questions. In this paper, we present the educational evaluation of the Sustainability Awareness Framework in a country in Sub- Saharan Africa. The results indicate that the framework can provide supportive guidance of the societal and environmental challenges in the given context.Item Spatial Analysis of Cervical Cancer and Correlated Factors(J Remote Sensing & GIS, an open access journal, 2018) Bingi, Daniel; Gidudu, Anthony; Okello, Dorothy; Lutalo Mwesigwa, CatherineCervical cancer is a screen preventable disease, despite this fact majority of women in Uganda report to major health centers at advanced stage of the disease leasing high mortality of the disease. The applicability and use of GIS in epidemiology studies in Uganda is still lacking, GIS combined with methods of spatial statistics provide powerful new tools for understanding the epidemiology of diseases thus analysis currently being done lack the spatial component, to effectively enable making informed spatial knowledge into areas at risk where screening services should target the gap which results from screening. This study was aimed exploring GIS to analyze the spatial distribution of cervical cancer and the correlated factors to target the high risk areas in Uganda. This involved determining the distribution of cervical cancer, developing a factor hotspot map and determine its relationship to the distribution and exploring the relationship that the correlated factors relate to the distribution of cervical cancer. This study concluded that cervical cancer is at a very high risk in Uganda and immediate action need to be considered to target the high risk areas before a wide scale infection is realized where a large majority of women are at risk of developing cervical cancer.