  Previously, crowdsourcing experiments in surveillance of crop diseases and
pest have been trialed as small scale community sensing campaigns with select
cohort of smallholder farmers, extension and experts. While those pilots have
demonstrated the viability of community sensing with mobile phones to collect
massive amounts of real-time data all year round, to compliment low-resourced
agricultural expert surveys, they are limited in generalising ideas for scaled
implementations of a community sensing system with farmer communities. This
work presents a case of scaled deployment of the mobile ad hoc surveillance for
crowdsourcing real-time surveillance data on cassava from over 175 smallholder
farmers across Uganda. This paper describes a modified mobile ad hoc
surveillance ecosystem to suite smallholder farmer agents, a communication
model and data collection model designed to cover the spatial interests for the
scale of surveillance, a deployment plan, the training methodology and
incentives structure. The paper also presents very early results of
contributions from farmer agents, that could be usable in monitoring the
movement of planting materials between districts, mapping cassava varieties,
multiplication sites, and communities with little or no access to agricultural
extension services, and possibly guide precision expert surveys in areas of
high disease incidence.
