Publications
2025
Journal: BMC Artificial Intelligence
This study presents PaliGemma-CXR, a multi-task multimodal model designed to address the challenges of TB diagnosis using chest X-rays. TB is a global health issue, and while X-rays are standard for screening, the shortage of radiologists is a major concern. The model aims to automate TB diagnosis, object detection, segmentation, report generation, and visual question answering (VQA). Using a multimodal dataset, the model was fine-tuned and data sampling methods were applied to improve performance. The results show impressive performance across tasks: 90.32% accuracy on TB diagnosis, 98.95% on close-ended VQA, 41.3 BLEU score for report generation, and mAP scores of 19.4 and 16.0 for object detection and segmentation, respectively. PaliGemma-CXR demonstrates the effective use of multi-task learning to improve image interpretation for TB detection.
Journal: arXiv preprint arXiv:2506.08400
Large Language models (LLMs) have demonstrated impressive performance on a wide range of tasks, including in multimodal settings such as speech. However, their evaluation is often limited to English and a few high-resource languages. For low-resource languages, there is no standardized evaluation benchmark. In this paper, we address this gap by introducing mSTEB, a new benchmark to evaluate the performance of LLMs on a wide range of tasks covering language identification, text classification, question answering, and translation tasks on both speech and text modalities. We evaluated the performance of leading LLMs such as Gemini 2.0 Flash and GPT-4o (Audio) and state-of-the-art open models such as Qwen 2 Audio and Gemma 3 27B. Our evaluation shows a wide gap in performance between high-resource and low-resource languages, especially for languages spoken in Africa and Americas/Oceania. Our findings show that more investment is needed to address their under-representation in LLMs coverage.
Journal: IEEE Access
Physicians rely on various data sources when diagnosing Tuberculosis (TB). This includes the patient’s historical data, demographic data, clinical laboratory results, and imaging data. Traditionally, the application of machine learning and deep learning in detecting TB has focused more on using single modes of data. This constrains the capabilities of the artificial intelligence (AI) techniques to replicate the clinical practice of incorporating multiple sources of information in decision-making. Recent advancements in deep learning and machine learning have enabled the integration of multimodal data which has led to the development of applications that more accurately reflect the clinician’s approach. However, the operations of deep learning techniques are still blackbox in nature, which makes it hard to understand their internal work mechanisms. As a result, it is necessary to incorporate explainable AI techniques to
Journal: Scientific African
Mismanaged plastics have been persistent in the natural environment for extended periods of time, threatening the health of aquatic and terrestrial ecosystems. Milestones have been achieved in exploratory efforts to use remote sensing for monitoring plastics in marine environments, but little has been done in freshwater systems. We present a systematic assessment of the various remote sensing platforms, sensor characteristics, and detection algorithms for plastic litter monitoring in freshwater environments. The literature search yielded twenty-eight studies published between January 2010 and March 2024. These studies were largely conducted on rivers in Asia, Europe, and North America, showing a lack of research in Africa, Australia, and South America. This underscores the need for region-specific research to support monitoring of plastic litter in freshwater environments.
Journal: Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 2
Generative AI (GenAI) presents societal and ethical challenges related to equity, academic integrity, bias, and data provenance. This working group will consider the ethical and societal impacts of GenAI in higher computing education. In this paper, we outline the goals, methodology and expected deliverables of the working group. In particular, we will carry out a systematic literature review to address a wide set of issues and topics covering the rapidly emerging technology of GenAI from the perspective of its ethical and social impacts, we will provide an evaluation of university policies on the adoption and guidelines for use of GenAI for computing education and develop a framework to outline the ethical and societal impacts of GenAI in computing education. This work synthesizes existing research and considers the implications for educational and professional codes of ethics.
Journal: Applied AI Letters
Text‐to‐speech (TTS) models have expanded the scope of digital inclusivity by becoming a basis for assistive communication technologies for visually impaired people, facilitating language learning, and allowing for digital textual content consumption in audio form across various sectors. Despite these benefits, the full potential of TTS models is often not realized for the majority of low‐resourced African languages because they have traditionally required large amounts of high‐quality single‐speaker recordings, which are financially costly and time‐consuming to obtain. In this paper, we demonstrate that crowdsourced recordings can help overcome the lack of single‐speaker data by compensating with data from other speakers of similar intonation (how the voice rises and falls in speech). We fine‐tuned an English variational inference with adversarial learning for an end‐to‐end text‐to‐speech (VITS) model on …
Journal: HIV research & clinical practice
Virological failure (VF) significantly threatens the efficacy of antiretroviral therapy (ART) programs in East Africa. This systematic review and meta-analysis assess the prevalence and predictors of VF among individuals living with HIV.
Methods
We searched PubMed, Web of Science, African Journals Online, and EMBASE for relevant studies. Heterogeneity was assessed using the I2 statistic, and random-effects models addressed between-study variability. Publication bias was examined through funnel plots, Egger’s regression, and Begg’s tests. Subgroup analyses and meta-regression explored heterogeneity sources and potential VF predictors. Analyses were conducted using MedCalc version 20.010, adhering to PRISMA 2020 guidelines.
Results
Twenty-five records were included, with a sample size of 29,829 people living with HIV on ART. The pooled prevalence of VF in East Africa was 19.4% (95 …
Journal: Data in Brief
This data article describes a curated, crowdsourced speech dataset in Luganda and Kiswahili, created to support text-to-speech (TTS) development in low-resource settings. The dataset is derived from Mozilla’s Common Voice corpus and includes only validated utterances from female speakers. A multi-step curation process was used to enhance the consistency and quality of the data. Speakers were first manually selected based on similarities in intonation, pitch, and rhythm, then validated using acoustic clustering with pitch features and mel-frequency cepstral coefficients (MFCCs). Audio files were preprocessed to remove leading and trailing silences using WebRTC voice activity detection, denoised with a causal waveform-based DEMUCS model, and filtered using WV-MOS, an automatic speech quality scoring tool. Only clips with a predicted MOS score of 3.5 or higher were retained. The final dataset contains …
Journal: Nature Communications
This commentary discusses health data challenges in Africa, focusing on digitization, standardization, and harmonization as key solutions. It highlights how addressing these foundational issues can enable AI and data science to transform healthcare systems across the continent.
Journal: Machine Learning with Applications
Maize leaf diseases significantly threaten crop yields, and there is need for accurate, and accessible diagnostic tools. This research addresses this need by developing and evaluating deep learning (DL) and machine learning (ML) models for in-field classification and detection of four critical maize diseases: Maize Leaf Blight, Maize Lethal Necrosis, Maize Streak Virus, and Fall Armyworm damage. Utilizing field imagery captured via digital cameras and smartphones across Uganda, Tanzania, Ghana, and Namibia, we developed and compared custom Convolutional Neural Networks (CNNs), transfer learning (MobileNetV2, InceptionResNetV2), Vision Transformers (ViT), and classical ML models. For detection, a transformer-enhanced YOLOv10 architecture was implemented. Explainable AI (XAI) techniques (Grad-CAM, LIME) were incorporated to ensure model transparency. MobileNetV2 achieved the highest …
Journal: arXiv preprint arXiv:2504.14105
Current AI models often fail to account for local context and language, given the predominance of English and Western internet content in their training data. This hinders the global relevance, usefulness, and safety of these models as they gain more users around the globe. Amplify Initiative, a data platform and methodology, leverages expert communities to collect diverse, high-quality data to address the limitations of these models. The platform is designed to enable co-creation of datasets, provide access to high-quality multilingual datasets, and offer recognition to data authors. This paper presents the approach to co-creating datasets with domain experts (e.g., health workers, teachers) through a pilot conducted in Sub-Saharan Africa (Ghana, Kenya, Malawi, Nigeria, and Uganda). In partnership with local researchers situated in these countries, the pilot demonstrated an end-to-end approach to co-creating data with 155 experts in sensitive domains (e.g., physicians, bankers, anthropologists, human and civil rights advocates). This approach, implemented with an Android app, resulted in an annotated dataset of 8,091 adversarial queries in seven languages (e.g., Luganda, Swahili, Chichewa), capturing nuanced and contextual information related to key themes such as misinformation and public interest topics. This dataset in turn can be used to evaluate models for their safety and cultural relevance within the context of these languages.
Journal: Applied AI Letters
Across numerous households in Sub‐Saharan Africa, agriculture plays a crucial role. One solution that can effectively bridge the support gap for farmers in the local community is a question–answer system based on agricultural expertise and agro‐information. The more recent advancements in question answering research involve the use of large language models that are trained on an extensive amount of data. Due to this, conventional fine‐tuning approaches have demonstrated a significant decline in performance when using a significantly smaller amount of data. One proposed alternative to address this decline is to use prompt‐based fine‐tuning, which allows the model to be fine‐tuned with only a few examples, thus addressing the disparities between the objectives of pretraining and fine‐tuning. Extensive research has been done on these methods, specifically on text classification and not question …
Journal: arXiv preprint arXiv:2511.15768
Generative AI (GenAI) presents societal and ethical challenges related to equity, academic integrity, bias, and data provenance. In this paper, we outline the goals, methodology and deliverables of their collaborative research, considering the ethical and societal impacts of GenAI in higher computing education. A systematic literature review that addresses a wide set of issues and topics covering the rapidly emerging technology of GenAI from the perspective of its ethical and societal impacts is presented. This paper then presents an evaluation of a broad international review of a set of university adoption, guidelines, and policies related to the use of GenAI and the implications for computing education. The Ethical and Societal Impacts-Framework (ESI-Framework), derived from the literature and policy review and evaluation, outlines the ethical and societal impacts of GenAI in computing education. This work synthesizes existing research and considers the implications for computing higher education. Educators, computing professionals and policy makers facing dilemmas related to the integration of GenAI in their respective contexts may use this framework to guide decision-making in the age of GenAI.
Journal: ACM Computing Surveys
With recent advancements in speech recognition, it is crucial to ensure that automatic speech recognition (ASR) systems do not exhibit systematic biases, such as those related to gender, age, accent, and dialect. Although research has extensively examined systematic biases such as those related to gender, age, accent, and dialect, for high-resource languages, research on low-resource African languages remains limited. This systematic literature review synthesizes evidence on bias evaluation and mitigation in ASR models for African languages, adhering to the PRISMA reporting guidelines. Our analysis reveals that most biases stem from data imbalances and limited linguistic diversity in training datasets, resulting in disproportionately high error rates for underrepresented speaker groups. Mitigation strategies in African contexts have primarily focused on data-centric methods, including dataset expansion …
Journal: Proceedings of the ACM Global on Computing Education Conference 2025 Vol 2
As new technological developments rapidly come to market, increased participation by Black women in computing education research is imperative to an inclusive future society. Whether those researchers focus on K-12, higher education, or industry, the inclusion of Black women across the globe helps to broaden our understanding of how to dismantle structures that maintain economic and social disparities through a lack of access and engagement. By bringing together Black women from across the diaspora, commonalities in experience, perspective, and research approach can be highlighted, and strategies for developing a more inclusive community can be discussed. In this panel, we explore ideas of Lineage and Legacy through a discussion of our experiences as Black women in computing education research. Drawing on scholarship that examines the lived realities of Black women in computing education …
Journal: arXiv e-prints
ASR has achieved remarkable global progress, yet African low-resource languages remain rigorously underrepresented, producing barriers to digital inclusion across the continent with more than +2000 languages. This systematic literature review (SLR) explores research on ASR for African languages with a focus on datasets, models and training methods, evaluation techniques, challenges, and recommends future directions. We employ the PRISMA 2020 procedures and search DBLP, ACM Digital Library, Google Scholar, Semantic Scholar, and arXiv for studies published between January 2020 and July 2025. We include studies related to ASR datasets, models or metrics for African languages, while excluding non-African, duplicates, and low-quality studies (score <3/5). We screen 71 out of 2,062 records and we record a total of 74 datasets across 111 languages, encompassing approximately 11,206 hours of speech. Fewer than 15% of research provided reproducible materials, and dataset licensing is not clear. Self-supervised and transfer learning techniques are promising, but are hindered by limited pre-training data, inadequate coverage of dialects, and the availability of resources. Most of the researchers use Word Error Rate (WER), with very minimal use of linguistically informed scores such as Character Error Rate (CER) or Diacritic Error Rate (DER), and thus with limited application in tonal and morphologically rich languages. The existing evidence on ASR systems is inconsistent, hindered by issues like dataset availability, poor annotations, licensing uncertainties, and limited benchmarking. Nevertheless, the rise of community …
2024
Journal: arXiv preprint
This research explores various approaches for crop classification using Artificial Intelligence (AI), particularly in the agricultural sector. Four techniques were evaluated: traditional machine learning with handcrafted feature extraction (SIFT, ORB, and Color Histogram), custom-designed CNN and deep learning architectures like AlexNet, transfer learning with pre-trained models (EfficientNetV2, ResNet152V2, Xception, Inception-ResNetV2, MobileNetV3), and cutting-edge models like YOLOv8 and DINOv2. Among them, Xception outperformed others, achieving 98% accuracy with a model size of 80.03 MB and a prediction time of 0.0633 seconds. The research also emphasized the importance of model explainability using tools like LIME, SHAP, and GradCAM, ensuring transparency in AI predictions. The study highlights the significance of selecting the right model based on specific tasks and the role of explainability in improving AI-driven crop management strategies.
Journal: arXiv preprint
This paper addresses the limitations of text-to-speech (TTS) development for Luganda, a language with scarce high-quality, single-speaker recordings. Previous models using Luganda Common Voice recordings have generated intelligible but low-quality speech due to insufficient preprocessing, varying intonations, and background noise. The authors improve TTS quality by training on recordings from six female speakers with similar intonations, applying a pre-trained speech enhancement model to reduce noise, and filtering recordings with a high Mean Opinion Score (MOS) above 3.5. The resulting TTS model achieved a MOS of 3.55, significantly outperforming the existing model (2.5 MOS) and models trained on fewer speakers, demonstrating the effectiveness of using multiple speakers with close intonation to enhance TTS quality.
Journal: ACM Journal on Computing and Sustainable Societies
This study used machine learning to analyze English and Luganda radio broadcasts to understand public perceptions of the Ebola outbreak in Uganda. The analysis identified three main speaker categories: media personalities, community guests and listeners, and government officials, with the government playing the most significant role in public education. The findings revealed that the community was hesitant to use Ebola vaccines, citing concerns about their untested status in other populations, and expressed worries about COVID-19 lockdown measures. Additionally, differences were noted in the timing and content of conversations between male and female speakers. These insights can help inform population-specific policies for managing current and future pandemics.
Journal: Smart Agricultural Technology
This work proposes a deep learning-based approach to identify diseases in bean plants, specifically Angular Leaf Spot (ALS) and bean rust, which are common in Uganda. The study evaluates image classification and object detection models using Convolutional Neural Network (CNN) architectures. The Makerere University beans image dataset, consisting of 15,335 images (ALS, bean rust, and healthy), was used for training. The dataset was expanded with an additional “unknown class” of 2,800 images to account for unrelated images. Adversarial training and Out-of-Distribution (ODD) detection techniques improved model robustness. The custom CNN, BeanWatchNet, achieved 90% accuracy for the three target classes, while EfficientNet v2 B0 and BeanWatchNet performed at 91% and 90% accuracy for a four-class classification task. YOLO v8 was superior in object detection, achieving an mAP@50 of 87.6. The models were deployed on smartphones and Raspberry Pi for in-field disease detection, with the code and models available on GitHub.
Journal: 3rd Workshop on African Natural Language Processing. 2022.
This paper addresses the lack of natural language processing resources for African languages and the challenges of obtaining high-quality speech and text data. It details the curation and annotation process for five East African languages—Luganda, Runyankore-Rukiga, Acholi, Lumasaba, and Swahili. Baseline models were developed for machine translation, topic modeling, classification, sentiment classification, and automatic speech recognition. The paper also highlights key experiences, challenges, and lessons learned in building these datasets.
2023
Journal: 7th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2023.
Triage in medicine prioritizes patients based on urgency, but traditional nurse-led evaluations are time-consuming and prone to human error. Mis-triage can delay critical care, while the absence of triage can overwhelm hospital resources. This research explores Explainable AI (XAI) for machine learning-based triage, using classifiers such as Decision Trees, Random Forest, XGBoost, and Histogram-Based Gradient Boosting. The best-performing model, Histogram-Based Gradient Boosting, achieved a 91% AUC score and 70% F1 score. XAI techniques like LIME and SHAP were applied to enhance model transparency and trustworthiness for intelligent healthcare.
Journal: RTBFoods
This paper highlights the use of computer vision technology, specifically the DigiEye system, for evaluating important crop traits to enhance breeding programs. The DigiEye system, which measures color and appearance, is a fast, non-destructive, high-throughput tool for acquiring crop traits on a large scale. It is particularly useful in capturing data related to color and texture, which are linked to the chemical composition and sensory properties of food. The paper outlines a Standard Operating Procedure (SOP) for using the DigiEye system to capture images of sweet potato and potato, and to predict color and mealiness, providing a step-by-step guide for replicating the process.
Journal: Smart Agricultural Technology.
In this paper, we present a machine learning-based approach to predicting sweetpotato sensory attributes, specifically flesh color and mealiness, to improve the breeding process. Traditional methods rely on trained human panels, which are costly and time-consuming, limiting throughput. Our approach uses image-based analysis with the DigiEye imaging system to capture and process sweetpotato cross-section images, extract features, and train predictive models. The Linear Regression and Random Forest Regression models achieved high accuracy for flesh color prediction (R² = 0.92 and 0.87, respectively), while the Random Forest and Gradient Boosting models performed well for mealiness prediction (R² = 0.85 and 0.80). The models were successfully tested by the sweetpotato breeding team at the International Potato Center in Uganda, demonstrating their potential to automate and accelerate the evaluation process. This method could enhance the selection of promising sweet potato varieties for breeding and increase adoption by consumers.
Journal: Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval. 2023.
In this paper, we address the challenge of Swahili news classification, despite Swahili being a well-resourced language, by leveraging classical machine learning (ML) models and deep neural networks (DNN). We employ various classification techniques, including Support Vector Machine (SVM), Logistic Regression, Random Forest, Gradient Boosting, and deep learning models such as CNN, LSTM, and Bi-LSTM with Attention. Our results show strong performance, with classical ML and DNN models achieving over 75% accuracy, while the CNN-Bi-LSTM + Attention model attained an AUC score of 97%. Additionally, we use LIME to provide explainability for model decisions. This research enhances Swahili natural language processing and lays the groundwork for future improvements using transformer-based models.
2022
Journal: The Electronic Journal of Information Systems in Developing Countries
This study assesses the impact of Adsurv, a mobile phone crowdsourcing tool used by smallholder farmers in Uganda for crop health surveillance and pest management. While previous research focused on the effects of mobile technologies on food security or livelihoods separately, this study provides a holistic evaluation of both aspects. Using the Sustainable Livelihood Framework (SLF) and the Analytic Hierarchy Process (AHP), the study found that Adsurv contributed more significantly to food availability rather than access or utilization. The main benefits were in enhancing human assets by empowering farmers with skills, which improved other livelihood assets. The findings suggest that further research is needed to promote the nutritional value of food in farming practices for long-term sustainable livelihoods.
Journal: AfricaNLP workshop.
This paper discusses the use of topic modeling and classification techniques on Luganda text data to automatically extract functional themes and topics. The authors employed Non-negative Matrix Factorization (NMF) for topic modeling, an unsupervised algorithm that identifies hidden patterns in text, and various approaches for topic classification, including classical methods, neural networks, and pretrained algorithms. The Bidirectional Encoder Representations from Transformers (BERT) and Support Vector Machine (SVM) algorithms yielded the best results for topic classification. The study found that both topic modeling and classification produced similar results when trained on a balanced dataset.
Journal: 3rd Workshop on African Natural Language Processing
This case study focuses on creating resources for machine translation systems for underrepresented languages. A parallel text corpus, SALT, was developed for five Ugandan languages (Luganda, Runyankole, Acholi, Lugbara, and Ateso) to address the shortage of training and evaluation data. The study explored various methods to train and evaluate translation models, which proved effective for practical applications. The resulting models achieved a mean BLEU score of 26.2 for translations into English and 19.9 for translations from English. The SALT dataset and models are publicly available for use.
Journal: arXiv preprint
Building an automatic speech recognition (ASR) system for under-resourced languages is crucial, especially in societies where radio is the primary medium of communication. In Uganda, efforts to understand rural perspectives are hindered by the lack of transcribed speech datasets. To address this, the Makerere AI research lab has released a 155-hour Luganda radio speech corpus, the first publicly available radio dataset in sub-Saharan Africa. This paper details the corpus development and presents baseline ASR performance results using the Coqui STT toolkit.
Journal: Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas
In this paper, we explore the evaluation of gender bias in Luganda-English machine translation, an area that remains underexplored due to limited explicit text data. We build machine translation models using transfer learning with a pre-trained Marian MT model for English-Luganda and Luganda-English. To assess gender bias, we apply the Word Embeddings Fairness Evaluation Framework (WEFE), focusing on Luganda’s gender-neutral pronouns. A small set of trusted gendered examples is used to measure bias, with results validated through human evaluation. Additionally, we introduce a modified Translation Gender Bias Index (TGBI) to account for Luganda’s grammatical structure.
Journal: RTBFoods
This study aimed to develop and evaluate a color and mealiness classification model for sweetpotato roots using images. A total of 3018 images were collected from 950 samples across various sites in Uganda and Kenya between October 2021 and November 2022. Sensory panel data were used for calibration, with up to twelve cooked roots per genotype evaluated per session. Linear regression models showed strong performance, particularly for predicting orange color intensity (R² = 0.92, MSE = 0.58), indicating suitability for field application. The best model for mealiness showed a Mean Absolute Error (MAE) of 2.16 for mealiness-by-hand and 9.01 for positive area.
Journal: 7th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2023.
Triage in medicine prioritizes patients based on urgency, but traditional nurse-led evaluations are time-consuming and prone to human error. Mis-triage can delay critical care, while the absence of triage can overwhelm hospital resources. This research explores Explainable AI (XAI) for machine learning-based triage, using classifiers such as Decision Trees, Random Forest, XGBoost, and Histogram-Based Gradient Boosting. The best-performing model, Histogram-Based Gradient Boosting, achieved a 91% AUC score and 70% F1 score. XAI techniques like LIME and SHAP were applied to enhance model transparency and trustworthiness for intelligent healthcare.
Journal: RTBFoods
This paper highlights the use of computer vision technology, specifically the DigiEye system, for evaluating important crop traits to enhance breeding programs. The DigiEye system, which measures color and appearance, is a fast, non-destructive, high-throughput tool for acquiring crop traits on a large scale. It is particularly useful in capturing data related to color and texture, which are linked to the chemical composition and sensory properties of food. The paper outlines a Standard Operating Procedure (SOP) for using the DigiEye system to capture images of sweet potato and potato, and to predict color and mealiness, providing a step-by-step guide for replicating the process.
Journal: Smart Agricultural Technology.
In this paper, we present a machine learning-based approach to predicting sweetpotato sensory attributes, specifically flesh color and mealiness, to improve the breeding process. Traditional methods rely on trained human panels, which are costly and time-consuming, limiting throughput. Our approach uses image-based analysis with the DigiEye imaging system to capture and process sweetpotato cross-section images, extract features, and train predictive models. The Linear Regression and Random Forest Regression models achieved high accuracy for flesh color prediction (R² = 0.92 and 0.87, respectively), while the Random Forest and Gradient Boosting models performed well for mealiness prediction (R² = 0.85 and 0.80). The models were successfully tested by the sweetpotato breeding team at the International Potato Center in Uganda, demonstrating their potential to automate and accelerate the evaluation process. This method could enhance the selection of promising sweet potato varieties for breeding and increase adoption by consumers.
Journal: Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval. 2023.
In this paper, we address the challenge of Swahili news classification, despite Swahili being a well-resourced language, by leveraging classical machine learning (ML) models and deep neural networks (DNN). We employ various classification techniques, including Support Vector Machine (SVM), Logistic Regression, Random Forest, Gradient Boosting, and deep learning models such as CNN, LSTM, and Bi-LSTM with Attention. Our results show strong performance, with classical ML and DNN models achieving over 75% accuracy, while the CNN-Bi-LSTM + Attention model attained an AUC score of 97%. Additionally, we use LIME to provide explainability for model decisions. This research enhances Swahili natural language processing and lays the groundwork for future improvements using transformer-based models.
2021
Journal: arXiv preprint
In this paper, we address the lack of misinformation detection tools for Uganda’s 40 indigenous languages by developing a dataset and classification models for detecting misinformation in code-mixed Luganda-English social media messages. The dataset was sourced from Facebook and Twitter, and various machine learning methods were applied for classification. A 10-fold cross-validation experiment showed that the Discriminative Multinomial Naive Bayes (DMNB) model performed best, achieving an accuracy of 78.19% and an F-measure of 77.90%. Support Vector Machine and Bagging ensemble models also produced comparable results. These findings demonstrate the potential of machine learning-based approaches for misinformation detection in under-resourced languages using n-gram features.
2020
Journal: arXiv preprint
Cassava, a major food crop in Africa, is severely affected by Cassava Brown Streak Disease (CBSD), which causes necrosis in starch-bearing tissues. Breeders currently rely on subjective visual inspection for scoring necrosis. This paper presents an automated approach using deep convolutional neural networks with semantic segmentation. Our experiments show that the UNet model performs this task with high accuracy, achieving a mean Intersection over Union (IoU) of 0.90 on the test set. This method provides a means to use a quantitative measure for necrosis scoring on root cross-sections. This is done by segmenting and classifying the necrotized and non-necrotized pixels of cassava root cross-sections without any additional feature engineering. toolkit.
Journal: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2020.
Whiteflies are key vectors of cassava diseases, and monitoring their numbers is crucial for disease control. The current manual counting method is tedious and time-consuming. This paper proposes an automated approach using computer vision techniques. Images of infested cassava leaves were collected, and a detector was trained using Haar Cascade and Deep Learning methods to identify and count whiteflies. Results show that this approach achieves high precision. The method can also be adapted for similar object detection tasks with minor modifications.
2019
Journal: arXiv preprint
In this paper, we explore the use of machine learning-based speech keyword spotting techniques to analyze community radio data in rural Uganda, where radio remains a dominant means of communication. Unlike urban areas with widespread internet access, rural communities rely on radio talk shows for news and discussions. We develop models to identify keywords related to agriculture from radio audio streams, providing a cost-efficient method for monitoring food security concerns such as crop diseases, pests, drought, and famine. This approach supports early warning systems for policymakers and stakeholders, enhancing agricultural and economic resilience in rural areas.
