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.
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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.
Digital animal health and production tracking using mobile app.
Point-of-Care Diagnostics: Our datasets contribute to building robust AI-powered mobile ELISA readers, facilitating massive animal screenings.
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.
M-Vet Project Team developing the platform
Accessible Technology: Our mobile-based ELISA image reader enhances diagnostic capabilities in low-resource settings, enabling timely interventions and reducing the burden on veterinarians.
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.
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.
Lacuna Fund - Datasets for Agriculture
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
Veterinarians
Ministry of Agriculture
UVAs
NALRRI
Project Lead
Daniel is the driving force behind the project, leveraging his extensive experience in project management and leadership to ensure successful outcomes.
Senior Research Scientist - NALRRI
Dr. Suzan leads the livestock epidemiology lab at National Livestock Resources and Research Institute-NARO
Project Admin
Lilian ensures smooth operations and effective project management with her exceptional leadership skills.
Data & ML Engineer
Hellen leverages her software development experience to create intelligent AI systems.
Data & ML Engineer
Chodrine specializes in creating cutting-edge data solutions and solving complex problems with machine learning.
Data & ML Engineer
Mubarak develops innovative data solutions and drives data-driven projects with his passion for machine learning.
Data & ML Engineer
Tobius excels in developing high-quality machine learning models and driving impactful data initiatives.
Deployment Engineer and User Engagement
Hewit ensures seamless deployment and engages users with effective, user-friendly solutions.
Hardware Engineer
Joel designs and implements robust hardware systems that integrate with our software.