Participatory Surveillance
An innovative, community-driven approach to crop disease monitoring that places farmers at the heart of early detection and response systems across Africa.
What is Participatory Surveillance?
Participatory surveillance is a collaborative model of agricultural health monitoring that engages farmers, extension workers, and researchers in a shared effort to detect and report crop disease outbreaks at the earliest possible stage. Rather than relying solely on top-down inspections, this approach builds a grassroots network of trained community sentinels who can identify warning signs in their own fields and relay critical information to national authorities in real time.
WAVE Research has pioneered this model across multiple countries in sub-Saharan Africa, equipping thousands of smallholder farmers with the knowledge, tools, and digital reporting platforms needed to protect their harvests and contribute to regional food security.
Key Activities
Community Training
Hands-on workshops teach farmers to identify disease symptoms, use mobile reporting tools, and understand the importance of early notification.
Digital Reporting
A mobile-based reporting system allows farmers to submit geo-tagged photos and observations that flow directly to district and national plant protection dashboards.
Early Warning Alerts
When suspicious symptoms are reported, automated alerts notify agricultural officers who can trigger rapid field verification and sampling.
Awareness Campaigns
Community sensitisation campaigns, radio broadcasts, and field demonstrations raise awareness about emerging threats and best practices for prevention.
15,000+
Farmers Trained
8
Countries Covered
48h
Average Response Time
Participatory surveillance represents a paradigm shift in how plant health is monitored in Africa. By transforming every farmer into a sentinel and every mobile phone into a reporting station, WAVE Research has created a scalable, cost-effective, and community-owned system that protects crops and livelihoods alike.
The data generated through this network not only enables rapid response to immediate threats but also feeds into predictive models that help anticipate future outbreaks — closing the loop between surveillance, diagnostics, and forecasting.