What a million Indian farmers say?: A crowdsourcing-based method for pest surveillance

TL;DR

This paper introduces a scalable, cost-effective pest surveillance method using crowdsourced farmer queries via telephone, leveraging data analysis to predict pest outbreaks and aid decision-making for farmers and policymakers.

Contribution

It presents a novel crowdsourcing approach for pest monitoring that overcomes scalability and cost issues of traditional methods, with data-driven insights for pest prediction.

Findings

High accuracy in pest detection through crowdsourcing

Economical and scalable coverage of large agricultural areas

Enhanced decision-making for farmers and policymakers

Abstract

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.