What a million Indian farmers say?: A crowdsourcing-based method for pest surveillance
This addresses pest detection for farmers and policymakers, offering a scalable solution to improve decision-making and food security.
The paper tackles pest surveillance in agriculture by developing a crowdsourcing method using real-time farmer phone queries, demonstrating it as an accurate and economical approach with high spatio-temporal coverage.
Many different technologies are used to detect pests in the crops, such as manual sampling, sensors, and radar. However, these methods have scalability issues as they fail to cover large areas, are uneconomical and complex. This paper proposes a crowdsourced based method utilising the real-time farmer queries gathered over telephones for pest surveillance. We developed data-driven strategies by aggregating and analyzing historical data to find patterns and get future insights into pest occurrence. We showed that it can be an accurate and economical method for pest surveillance capable of enveloping a large area with high spatio-temporal granularity. Forecasting the pest population will help farmers in making informed decisions at the right time. This will also help the government and policymakers to make the necessary preparations as and when required and may also ensure food security.