Mobile community sensing with smallholder farmers in a developing nation; A scaled pilot for crop health monitoring

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.

[1]  Ramachandran Ramjee,et al.  Nericell: rich monitoring of road and traffic conditions using mobile smartphones , 2008, SenSys '08.

[2]  Panagiotis G. Ipeirotis,et al.  Running Experiments on Amazon Mechanical Turk , 2010, Judgment and Decision Making.

[3]  M. Stockbridge Competitive Commercial Agriculture in Sub – Saharan Africa ( CCAA ) Study ALL-AFRICA REVIEW OF EXPERIENCES WITH COMMERCIAL AGRICULTURE Environmental Impacts , 2008 .

[4]  Aniket Kittur,et al.  Organizing without formal organization: group identification, goal setting and social modeling in directing online production , 2012, CSCW.

[5]  Wen Hu,et al.  Ear-phone: an end-to-end participatory urban noise mapping system , 2010, IPSN '10.

[6]  Ernest Mwebaze,et al.  Crowdsourcing Real-Time Viral Disease and Pest Information: A Case of Nation-Wide Cassava Disease Surveillance in a Developing Country , 2019, HCOMP.

[7]  Benjamin B. Bederson,et al.  Human computation: a survey and taxonomy of a growing field , 2011, CHI.

[8]  Erik T. Mueller,et al.  Open Mind Common Sense: Knowledge Acquisition from the General Public , 2002, OTM.

[9]  Timothy Chklovski,et al.  Learner: a system for acquiring commonsense knowledge by analogy , 2003, K-CAP '03.

[10]  Yolanda Gil,et al.  Towards Managing Knowledge Collection from Volunteer Contributors , 2005, AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors.

[11]  Jaime Teevan,et al.  Crowdsourcing in the Field: A Case Study Using Local Crowds for Event Reporting , 2015, HCOMP.

[12]  Peter Norvig,et al.  Can Distributed Volunteers Accomplish Massive Data Analysis Tasks , 2001 .

[13]  M. Haklay Citizen Science and Volunteered Geographic Information: Overview and Typology of Participation , 2013 .

[14]  Chris Van Pelt,et al.  Designing a scalable crowdsourcing platform , 2012, SIGMOD Conference.

[15]  John A. Quinn,et al.  A mobile market for agricultural trade in Uganda , 2013, ACM DEV-4 '13.

[16]  J. Silvertown A new dawn for citizen science. , 2009, Trends in ecology & evolution.

[17]  Sivan Toledo,et al.  VTrack: accurate, energy-aware road traffic delay estimation using mobile phones , 2009, SenSys '09.

[18]  Y. Baguma,et al.  The current pandemic of cassava mosaic virus disease in East Africa and its control. , 2000 .

[19]  Michael S. Bernstein,et al.  The future of crowd work , 2013, CSCW.

[20]  Ellie D'Hondt,et al.  Crowdsourcing of Pollution Data using Smartphones , 2010 .