Faculty of Engineering and Technology

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4 pupils electrocuted as concerns grow over power theft in Sironko
(Daily Monitor, 2018-05-01) Kitunzi, Yahudu
Most victims either touch live wires or attempt to tap power directly from the high voltage lines overhead.
A field-based recommender system for crop disease detection using machine learning
(Frontiers in Artificial Intelligence, 2023) Omara, Jonathan; Talavera, Estefania; Otim, Daniel; Turcza, Dan; Ofumbi, Emmanuel; Owomugisha, Godliver
This study investigates crop disease monitoring with real-time information feedback to smallholder farmers. Proper crop disease diagnosis tools and information about agricultural practices are key to growth and development in the agricultural sector. The research was piloted in a rural community of smallholder farmers having 100 farmers participating in a system that performs diagnosis on cassava diseases and provides advisory recommendation services with real-time information. Here, we present a field-based recommendation system that provides real-time feedback on crop disease diagnosis. Our recommender system is based on question–answer pairs, and it is built using machine learning and natural language processing techniques. We study and experiment with various algorithms that are considered state-of-the-art in the field. The best performance is achieved with the sentence BERT model (RetBERT), which obtains a BLEU score of 50.8%, which we think is limited by the limited amount of available data. The application tool integrates both online and online services since farmers come from remote areas where internet is limited. Success in this study will result in a large trial to validate its applicability for use in alleviating the food security problem in sub-Saharan Africa. KEYWORDS Crop disease monitoring, recommendation systems, natural language processing, smart farming, question-answer pairs, food security
A labeled spectral dataset with cassava disease occurrences using virus titre determination protocol
(Elsevier, 2023) Owomugisha, Godliver; Nakatumba-Nabende, Joyce; Dhikusooka, Joshua Jeremy; Taravera, Estefania; Nuwamanya, Ephraim; Mwebaze, Ernest
In this work, we present a novel dataset composed of spectral data and images of cassava crops with and without diseases. Together with the description of the dataset, we describe the protocol to collect such data in a controlled environment and in an open field where pests are not controlled. Crop disease diagnosis has been done in the past through the analysis of plant images taken with a smartphone camera. However, in some cases, disease symptoms are not visible. Furthermore, for some cassava diseases, once symptoms have manifested on the aerial part of the plant, the root which is the edible part of the plant has been totally destroyed. The goal of collecting this multimodality of the crop disease is early intervention, following the hypothesis that diseased crops without visible symptoms can be detected using spectral information. We collected visible and near-infrared spectra captured from leaves infected with two common cassava diseases namely; Cassava Brown Streak Disease and Cassava Mosaic Disease, as well as from healthy plants. Together, we also captured leaf imagery data that corresponds to the spectral information. In our experiments, biochemical data is collected and taken as the ground truth. Finally, agricultural experts provided a disease score per plant leaf from 1 to 5, 1 representing healthy and 5 severely diseased. The process of disease monitoring and data collection took 19 and 15 consecutive weeks for screen house and open ﬁeld, respectively, until disease symptoms were visibly seen by the human eye. Keywords: Spectral data protocol, Cassava diseases, Crop diagnosis, Smart agriculture, Early disease detection
A light spectrometer device for crop disease monitoring
(ICLR, 2023) Dhikusooka, J Joshua; Nuwamanya, Ephraim; Talavera, Estefania; Owomugisha, Godliver
Portable devices for the early detection of crop diseases are needed to support the farmers working in the field. Spectrometers showed their potential in the detection of crop diseases. However, high interpretation skills are needed to use the currently available spectrometers. In this project, we propose a portable device that obtains a spectrum wavelength of 700 nanometers describing the information of the crop. The output of this tool is integrated into a smartphone in the form of an app, making it accessible for use in the field in real applications.
A low-cost 3-D printed smartphone add-on spectrometer for diagnosis of crop diseases in field
(Association for Computing Machinery, 2020) Owomugisha, Godliver; Mugagga, K. B. Pius; Melchert, Friedrich; Mwebaze, Ernest; Quinn, A. John; Biehl, Michael
We present our initial proof of concept study towards the development of a low-cost 3-D printed smartphone add-on spectrometer. The study aimed at developing a cheap technology (less than 5USD) to be used for detection of crop diseases in the field using spectrometry. Previously, we experimented with the problem of disease diagnosis using an off-the-shelf and expensive spectrometer (approximately 1000 USD). However, in real world practice, this off-the-shelf device cannot be used by typical users (smallholder farmers). Therefore, the study presents a tool that is cheap and user friendly. We present preliminary results and identify requirements for a future version aiming at an accurate diagnostic technology to be used in the field before disease symptoms are visibly seen by the naked eye. Evaluation shows performance of the tool is better than random however below performance of an industry grade spectrometer. CCS CONCEPTS • Applied computing → Physical sciences and engineering; • Computing methodologies → Machine learning. KEYWORDS Low-cost, Spectrometery, Crop disease, Diagnosis, 3-D printed, Smartphone
Access to safe water improving.
(Daily monitor, 2018-05-23) Karunji, Zuurah
Water resources, bio-diversity, ecosystems and forests are what make up a great environment . in order to protect and preserve them, the NRM government put strong policies, laws and regulations on how people can manage them.
Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
(MDPI, 2022) Mafukidze, Dzingai Harry; Owomugisha, Godliver; Otim, Daniel; Nechibvute, Action; Nyamhere, Cloud; Mazunga, Felix
Convolutional neural networks (CNNs) are the gold standard in the machine learning (ML) community. As a result, most of the recent studies have relied on CNNs, which have achieved higher accuracies compared with traditional machine learning approaches. From prior research, we learned that multi-class image classification models can solve leaf disease identification problems, and multi-label image classification models can solve leaf disease quantification problems (severity analysis). Historically, maize leaf disease severity analysis or quantification has always relied on domain knowledge—that is, experts evaluate the images and train the CNN models based on their knowledge. Here, we propose a unique system that achieves the same objective while excluding input from specialists. This avoids bias and does not rely on a “human in the loop model” for disease quantification. The advantages of the proposed system are many. Notably, the conventional system of maize leaf disease quantification is labor intensive, time-consuming and prone to errors since it lacks standardized diagnosis guidelines. In this work, we present an approach to quantify maize leaf disease based on adaptive thresholding. The experimental work of our study is in three parts. First, we train a wide variety of well-known deep learning models for maize leaf disease classification, then we compare the performance of the deep learning models and finally extract the class activation heatmaps from the prediction layers of the CNN models. Second, we develop an adaptive thresholding technique that automatically extracts the regions of interest from the class activation maps without any prior knowledge. Lastly, we use these regions of interest to estimate image leaf disease severity. Experimental results show that transfer learning approaches can classify maize leaf diseases with up to 99% accuracy. With a high quantification accuracy, our proposed adaptive thresholding method for CNN class activation maps can be a valuable contribution to quantifying maize leaf diseases without relying on domain knowledge. Keywords: CNN; transfer learning; class activation heatmap; adaptive thresholding
Bugiri locals share water sources with animals
(Daily Monitor, 2018-05-01) Nasasira, D. Roland
The residents have no protected water sources, save for a few boreholes built by an individual.
Building apps propelled Kasujja into innovation
(Daily Monitor, 2018-02-08) Ngwomoya, Amos
When Imran Kasujja, 26, built his first MakApp about Makerere University's map in 2016, he never imagined it was the gateway into the world of technology.
Dairy Herd Monitor is a real deal
(Saturday Monitor, 2018-02-10) Owiny, Jolly Tobias
A dairy herd monitor helps farmers to accurately identify individual-cow milk production. The device also helps you better understand the performance of your herd and identify opportunities to adjust management areas on your farm.
Design review of an innovated single piston diesel engine in Uganda
(Journal of Applied Material Science & Engineering Research, 2023) Wafula, Simon Peter; Kirabira, John Baptist; Mukasa, Norbert; Kamulegeya, Obed; Nakimuli, Naqiyyah Kimuli
Engineering designs are important in engine production. This research aimed at incorporating engineering principles and practices into the developed single piston diesel engine at Kevoton. Tests on the already innovated engine were carried out to evaluate the performance of the existing engines in terms of power, torque, rpm and exhaust temperature. Designs of the major components were generated together with material selection, which helped in determining whether the used material was appropriate for engine production. Results showed that the engine runs at a speed of 1800 rpm, a torque of 5 Nm, and a power of 10 HP with 2.4 liters per hour of fuel. The major components of the engine include the engine piston, the crankshaft, connecting rod, cylinder head and the camshaft. The power obtained after carrying out the new design was 13 HP, with a 5 Nm torque as well as 1 liter of fuel consumed per hour. It was realized that some components were not meeting the design specifications and the design was below the minimum required power of 13 HP. Power produced was less than 13 HP that was required to handle the purpose of engine manufacture, the engine was designed to be a prime mover to do most of the local. It was identified that the need of the engine should be considered as a primary requirement when coming up with the designs, the choice of injectors affect the amount of exhaust and engine testing requires consideration while designing an engine.
Development of a bioactive gauze dressing functionalized with herb- loaded nanoparticles to facilitate wound healing
(Makerere University, 2025) Namuga, Catherine
Wound healing is crucial in healthcare due to its significant physiological and economic impacts on patients. Among wound dressings, gauze is the most widely used; however, it is associated with a high risk of bacterial wound infections that delay healing. Hence, enhancing its effectiveness remains critical. In Uganda, herbal medicines are still utilised in the treatment of wounds and other illnesses; however, they require high dosages and prolonged treatment, leading to poor patient compliance. Nanoencapsulation offers a promising solution by improving the drug's therapeutic effect through sustained release while minimising toxicity and dosage.
Development of electronic waste management framework at college of engineering, design, art, and technology
(Journal of Applied Material Science & Engineering Research, 2023) Nakeya, Zahara; Olupot, Peter; Wafula, Simon Peter; Kamulegeya, Obed; Nakimuli, Naqiyyah Kimuli
The worldwide use of information and communications technology (ICT) equipment and other electronic equipment is growing. There is growing amount of equipment that becomes waste after its time in use. This growth is expected to accelerate since equipment lifetime decreases with time and growing consumption. As a result, e-waste is one of the fastest-growing waste streams globally. The United Nations University (UNU) calculates in its second Global E-waste Monitor 44.7 million metric tonnes (Mt) of e-waste were generated globally in 2016. The objective of the study was to develop a framework for improving e-waste management at the College of Engineering, Design, Art, and Technology (CEDAT). This was achieved by breaking it down into specific objectives, and these included the establishment of the policy and procedures being used in e-waste management at CEDAT, the determination of the effectiveness of the e-waste management practices at CEDAT, the establishment of the critical challenges constraining e-waste management at the College, development of a framework for e-waste management. The study population was 80 respondents, from which a sample of 69 respondents was selected using simple and purposive sampling techniques. This research was carried out to investigate the problem of e-waste and come up with a framework to improve e-waste management. The study reviewed the e-waste regulatory framework used at the college and then collected data, which was used to come up with a framework. The study also established that weak policy and regulatory framework, lack of proper infrastructure, improper disposal of e-waste and a general lack of awareness of the e-waste and the magnitude of the problem are the critical challenges of e-waste management. In order to appropriately address the issue, the policy and regulatory framework should be updated, localized, and strengthened. It will be helpful to launch awareness campaigns, issues. The study suggests a framework for e-waste improvement Keywords E-Waste, Treatment, Disposal, Computers, Model, Management Policy and Guidelines.
Did you know? Farmers seek irrigation
(Saturday Monitor, 2018-02-10) Mukombozi, Rajab
On August 15 of every year was a day every farmer in South Western region looked up wit eagerness. It marked the beginning of the rain season.
Early detection of plant diseases using spectral data
(APPIS, 2020) Owomugisha, Godliver; Nuwamanya, Ephraim; Quinn, A. John; Biehl, Michael; Mwebaze, Ernest
Early detection of crop disease is an essential step in food security. Usually, the detection becomes possible in a stage where disease symptoms are already visible on the aerial part of the plant. However, once the disease has manifested in different parts of the plant, little can be done to salvage the situation. Here, we suggest that the use of visible and near infrared spectral information facilitates disease detection in cassava crops before symptoms can be seen by the human eye. To test this hypothesis, we grow cassava plants in a screen house where they are inoculated with disease viruses. We monitor the plants over time collecting both spectra and plant tissue for wet chemistry analysis. Our results demonstrate that suitably trained classifiers are indeed able to detect cassava diseases. Specifically, we consider Generalized Matrix Relevance Learning Vector Quantization (GMLVQ) applied to original spectra and, alternatively, in combination with dimension reduction by Principal Component Analysis (PCA). We show that successful detection is possible shortly after the infection can be confirmed by wet lab chemistry, several weeks before symptoms manifest on the plants.
Establishing a comprehensive digital evidence storage management framework for judiciary criminal investigation and prosecution : a case study of Arua city, Uganda
(Scientific and Practical Cyber Security Journal, 2025) Oli, Caroline Tabu; Ocen, Gilibrays Gilbert; Alunyu, Andrew Egwar; Mwase, Ali; Odongtoo, Godfrey; Kalinaki, Hussein; Matovu, Davis
The growing reliance on digital evidence in judicial proceedings highlights the urgent need for a robust and effective storage management framework. This study critically assesses the shortcomings of the current digital evidence storage system within the judiciary of Arua City, Uganda specifically issues related to loss, corruption, and inadmissibility of evidence, which threaten the delivery of justice and the protection of human rights. The study adopted a quantitative approach to explore the state of digital evidence management and design a suitable model for its improvement. This involved structured surveys aimed at generating measurable data on the technical, financial, and organizational challenges hindering effective implementation. Results showed that 87% of respondents identified technical barriers, 63% cited financial constraints, and 72% emphasized the need for organizational support and training. The study concludes that addressing these challenges through scalable, secure, and user-friendly systems alongside ongoing professional training and stakeholder engagement is vital. Regular evaluations are recommended to ensure system relevance and effectiveness in line with evolving digital technologies. KEYWORDS: Digital Evidence, Judiciary, Criminal Investigation, Evidence Storage Management, Arua City, Uganda
Floods cut off tourism roads, wreak havoc across country
(Daily Monitor, 2018-05-03) Monitor Team
Countrywide; Unrelenting torrential rains pounding different parts of the country have washed away half a dozen of bridges, cut off communities, killed livestock and inundated farm lands to post risk of food insecurity.
Governement orders on Shs 300bn national optic fibre
(Daily Monitor, 2018-05-01) Mugerwa, Yasiin; Kahungu, Thembo
President Museveni has given Uganda Telecom Limited (UTL) 'Unlimited access' to national optic fibre backbone infrastructure network and ordered for immediate cancellation of a multi-billion 'unfavourable' internet contract government officials signed with soliton Telmec, a Kenyan company.
Governement to connect 287 centres to power grid
(Daily Monitor, 2018-05-07) Draku, Franklin
The Rural Electrification Agency is in the final stages of rolling out connection of all the 287sub-county head quarters across the country to the main power grid.
Government must cut electricity price for benefit of citizens
(Daily Monitor, 2018-05-01) Natasha, Mariam
In Uganda the government has tried to ensure that every household has electricity through programmes like rural electrification yet many people still don't have it. Why?

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