M-Vet

The Mobile Vet system (M-Vet) is designed to revolutionize livestock health through data and AI, empowering stakeholders across Uganda's agriculture sector. By digitizing animal health and production tracking, facilitating informed decision-making for farmers, frontline veterinarians, and national livestock agencies.

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Project Objective

M-Vet emerged as a transformative solution to the fragmented landscape of livestock health management, where challenges in data collection, analysis, and dissemination hindered effective monitoring and response to diseases. By streamlining these processes, M-Vet empowers stakeholders—farmers, veterinarians, and regulatory bodies—with accurate and timely information. Through digital tracking, point-of-care diagnostics, and syndromic surveillance, M-Vet facilitats informed decision-making and precision interventions, ultimately enhancing livestock health outcomes and promoting sustainable agriculture practices.

Farms

512+

↑ 15%

Animal Officers

200+

↑ 6%

Field Reports

50+

↑ 10%

Animal Samples

2000+

↑ 8%

Project Overview

MVet is a project that is curating datasests on livestock health and test. The datasets include geotagged images labelled with signs and symptoms tags. This work is supported by Lacuna Fund and partners will unlock opportunities in developing data-driven solutions to improve livestock health in Uganda and beyond. We use AI and real-time data to help veterinarians, researchers, and farmers monitor, diagnose, and respond to diseases in livestock. Our goal is to improve livestock health outcomes and transform agriculture in Uganda.

Our beneficiaries

Livestock Farmers

Digital animal health and production tracking using mobile app.

Frontline Veterinarians and Animal Production Extensionists

Point-of-Care Diagnostics: Our datasets contribute to building robust AI-powered mobile ELISA readers, facilitating massive animal screenings.

National Livestock Agencies

Syndromic Surveillance: Our labeled geotagged syndromic image data supports the transition from passive to active disease monitoring. This data is integrated into national livestock agencies' surveillance systems, enabling real-time monitoring of emerging incidents and facilitating spatiotemporal modeling of livestock diseases.

Project Achievements

M-Vet Project Team developing the platform

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M-Vet Project Team developing the platform

Team working on the platform.

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M-Vet Project Team developing the platform

Team working on the platform.

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Collaborative meetings

Meeting with senior veterinarians.

How M-Vet Empowers

Rapid Point-of-Care Diagnostics

Accessible Technology: Our mobile-based ELISA image reader enhances diagnostic capabilities in low-resource settings, enabling timely interventions and reducing the burden on veterinarians.

Image-Based Disease Diagnosis

Real-Time Monitoring: Our syndromic surveillance system provides national agencies with real-time data on livestock health, facilitating early warning and response systems to mitigate disease outbreaks and protect animal populations.

National Livestock Syndromic Surveillance

Real-Time Monitoring: Our syndromic surveillance system provides national agencies with real-time data on livestock health, facilitating early warning and response systems to mitigate disease outbreaks and protect animal populations.

online course

Supported By

Lacuna Fund

Lacuna Fund - Datasets for Agriculture

Our Trusted Partners

M-Vet succeeds thanks to valuable partnerships. We collaborate with top institutions in agriculture, tech, and research, including the Makerere Artificial Intelligence Lab, the Research Consortium on African Swine Fever, the National Livestock Research & Resources Institute, and Veterinarians Without Borders. Through these collaborations, we harness expertise, resources, and networks to innovate and make an impact in livestock health. Together, we're committed to transforming livestock management with data-driven solutions and advanced AI technologies.

Makerere AI Research Lab logo

Makerere AI Research Lab

Veterinarians logo

Veterinarians

Ministry of Agriculture logo

Ministry of Agriculture

UVAs logo

UVAs

NARRO logo

NALRRI

Meet Our Team

Daniel Mutembesa's avatar

Daniel Mutembesa

Project Lead

Daniel is the driving force behind the project, leveraging his extensive experience in project management and leadership to ensure successful outcomes.

Dr Susan Kerfua's avatar

Dr Susan Kerfua

Senior Research Scientist - NALRRI

Dr. Suzan leads the livestock epidemiology lab at National Livestock Resources and Research Institute-NARO

Lilian Nabukera's avatar

Lilian Nabukera

Project Admin

Lilian ensures smooth operations and effective project management with her exceptional leadership skills.

Hellen Nammulinda's avatar

Hellen Nammulinda

Data & ML Engineer

Hellen leverages her software development experience to create intelligent AI systems.

Chodrine Mutebi's avatar

Chodrine Mutebi

Data & ML Engineer

Chodrine specializes in creating cutting-edge data solutions and solving complex problems with machine learning.

Mubarak Banadda's avatar

Mubarak Banadda

Data & ML Engineer

Mubarak develops innovative data solutions and drives data-driven projects with his passion for machine learning.

Tobius Saul's avatar

Tobius Saul

Data & ML Engineer

Tobius excels in developing high-quality machine learning models and driving impactful data initiatives.

Hewitt Tusiime's avatar

Hewitt Tusiime

Deployment Engineer and User Engagement

Hewit ensures seamless deployment and engages users with effective, user-friendly solutions.

Joel Ssematimba's avatar

Joel Ssematimba

Hardware Engineer

Joel designs and implements robust hardware systems that integrate with our software.