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Research

AI solutions built from Tanzanian problems

Our Healthcare and Agriculture units develop tools with the people who will use them — clinicians, farmers, veterinarians and regulators. Each entry below starts from a documented local problem.

Healthcare Unit

Disease prediction, diagnosis, risk assessment and health-data management — from maternal health to pharmacovigilance.

Agriculture Unit

Crop and livestock production: disease detection, yield prediction, dairy productivity and farm advisory systems.

Data & compute

Representative African datasets and shared GPU infrastructure — 16 researchers used Lab computing this year.

Responsible & inclusive AI

Fairness, privacy and accountability across the AI lifecycle, with women, youth and persons with disabilities at the centre.

  • Health care

    AKILI CHECK

    Protected by patent · TZ/P/2026/000125

    The problem
    The AI tool aims to improve awareness and early detection of mental health disorders in our Tanzanian community. Majority of Tanzanians are not aware of these disorders especially at their earliest stages. The AI tool helps people to learn, understand, interpret and detect different symptoms of different mental health disorders.
    Local context
    The current psychiatrist to population ratio is at 0.0625 for 100,000 people, while 0.0375 psychologists for a population of 100,000 individuals, and a huge burden of mental health care has been carried out by mental health nurses for many years. In Tanzania, an estimated 7 million people are living with mental disorders and substance abuse, and over 1.5 million living with depressive disorders, majority of whom being females. With a total expenditure of only 43 Tanzanian shillings allocated on mental health per person, and Disability-adjusted life years (DALYs) of 2,727 per 100,000 population, mental health needs are seriously unmet and thus improvement in financing is of crucial importance
    Technology
    The development of tool leveraged machine learning traditional techniques and natural language processing.
    Who uses it
    The primary users of the AI tool are the normal community members(citizens) such as youth and women.
  • Health care

    Automated Identification of Diabetic Retinopathy Using Deep Learning

    The problem
    The AI tool aims to solve the problem of efficient and accessible screening for Diabetic Retinopathy (DR), a leading cause of blindness in adults with diabetes. Early detection and timely intervention are crucial to prevent vision loss, but traditional screening methods often face challenges related to accessibility, cost, and the availability of trained specialists.
    Local context
    Tanzania faces a critical shortage of ophthalmologists, with only 69 serving a population of over 60 million, resulting in a ratio of approximately 0.8 ophthalmologists per million people (2019). This falls significantly short of the WHO recommendation of 1 per 250,000.
    Technology
    The tool utilizes deep learning, specifically convolutional neural networks (Convnets), for image analysis and classification.
    Who uses it
    Trained Technicians: To conduct initial screenings by uploading images and reviewing AI-generated results, potentially referring cases for validation. Ophthalmologists and Optometrists: To efficiently screen patients, prioritize complex cases, and perform validation of AI results
  • Agriculture

    Banana disease detection tool

    The problem
    The AI solution aim to solve the early detection of Black Sigatoka and Fusarium Wilt in Banana
    Local context
    In Tanzania, a total of 20 735 banana-growing smallholder farmers were reported to have banana disease occurrences in 2011.
    Technology
    Deep learning technology is used in the development of the tool.
    Who uses it
    Farmers are the primary users of the tool.
  • Agriculture

    bustaniBOT

    The problem
    The problem the AI tool aims to solve is inadequate extension services in Tanzania.
    Local context
    Only 7% of crop-growing households in Tanzania receive agricultural extension services. Shortage of extension officers. The ratio of extension officer to smallholder farmers is 1:2000.
    Technology
    The tool uses natural language processing techniques (machine reading comprehension) to understand the user queries and respond.
    Who uses it
    Smallholder farmers and extension officers.
  • Health care

    Early Detection of Maternal, Perinatal and Child Health Risks and their Timely Management using Artificial Intelligence

    Protected by patent · TZ/P/2026/000124

    The problem
    The AI tool aims to solve Tanzania's high maternal mortality rate (MMR)
    Local context
    Specific to Tanzania, which has one of the highest MMR rates
    Technology
    Uses text-based machine learning/LLM technology
    Who uses it
    Medical professionals/Doctors
  • Health care

    Improving Image Deblurring for Better Brain Diseases Diagnosis using Computer Vision Techniques

    The problem
    To solve the problem of motion blur in brain images.
    Technology
    Deep learning
    Who uses it
    Radiologists
  • Agriculture

    maziwaAI

    The problem
    The AI tool aims to solve the problem of low dairy production among smallholder farmers by providing tailored (personalized) recommendations that help them enhance their productivity. By leveraging (utilizing) strategies and insights from fellow farmers within the same cluster, the tool facilitates knowledge sharing and best practices, enabling farmers to implement effective techniques that can lead to increased milk yields and improved overall farm performance.
    Local context
    In Tanzania, the dairy sector is characterized by a reliance on cattle, which account for approximately 90% of the country's milk production. This production primarily occurs within low-input, low-yielding systems, with most of the milk being consumed by the farmers' families rather than sold in the market. In the Tanga region, the smallholder dairy sector comprises around 6,000 farmers, each typically selling about eight litres of milk daily to Tanga Fresh. Most of these farmers own one to two cows, which limits their production capacity. In contrast, larger operations like the ASAS Group manage a dairy ranch that spans over 4,000 acres and features well-established milking and processing systems. The ASAS Group also plays a significant role in the local dairy economy by purchasing milk from smallholder farmers, ensuring a consistent supply of quality milk for processing. This context highlights the challenges faced by smallholder dairy farmers in terms of production capacity, market access, and the need for improved practices to enhance their livelihoods and contribute to the overall growth of the dairy sector in Tanzania.
    Technology
    The development of the tool utilized a data mining approach to mine association rules from available secondary datasets. This process involves identifying common patterns, correlations, and links within datasets stored in various types of databases, including relational and transactional databases. The association rules were derived using the Apriori algorithm, and only the relevant rules were employed in training the recommendation model.
  • Agriculture

    Prediction of Peste des Petits Ruminants (PPR) diseases

    Technology
    The tool uses machine learning algorithms (supervised learning models) to analyze disease patterns and clinical data.
    Who uses it
    Veterinarians and farmers.