ANI-LINK: AI-Powered cattle foot-and-mouth disease detection and veterinary care in Uganda
| dc.contributor.author | David Buembo | |
| dc.contributor.author | Leon Matabi Kasingye | |
| dc.contributor.author | Teopista Najjuma | |
| dc.date.accessioned | 2026-09-02T09:30:54Z | |
| dc.date.available | 2026-09-02T09:30:54Z | |
| dc.date.issued | 2026-06-04 | |
| dc.description | Undergraduate | |
| dc.description.abstract | Livestock production plays a critical role in rural livelihoods across Uganda, providing income, food security, and economic stability for smallholder farmers. However, the sector continues to face significant challenges due to delayed disease detection, limited access to veterinary services, and fragmented livestock health management systems. These challenges contribute to substantial productivity losses, particularly from diseases such as Foot-and-Mouth Disease (FMD). This study presents AniLink, an artificial intelligence (AI)-enabled livestock health platform designed to support early disease detection, improve health record management, and enhance access to veterinary services. The system integrates a multi-task deep learning model based on MobileNetV3 for simultaneous cattle verification and FMD detection, alongside digital health records and a mobile- based service interface. A Design Science Research (DSR) approach was adopted to guide system development and evaluation. The model was trained on a combination of publicly available datasets and locally collected images from Gomba District, Uganda. Evaluation was conducted using classification metrics, system performance measures, and user-centred usability testing. Results demonstrate strong model performance, achieving high recall for both cattle detection and FMD classification, along with efficient real-time processing suitable for mobile deployment. Usability evaluation indicates that the platform is accessible to users with varying levels of digital literacy, with positive feedback on ease of use and clarity of outputs. The findings suggest that integrated AI-driven livestock health systems can significantly improve disease detection, decision-making, and access to veterinary support in resource-constrained environments. The study contributes a practical and scalable framework for applying artificial intelligence in agricultural health management. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12311/3610 | |
| dc.language.iso | en | |
| dc.publisher | Uganda Christian University | |
| dc.title | ANI-LINK: AI-Powered cattle foot-and-mouth disease detection and veterinary care in Uganda | |
| dc.type | Project Report |