Ritvik Sahajpal of the University of Maryland College Park in the U.S. will develop an early warning system for low-income countries that predicts the threat to crops from pests and diseases by combining machine learning and crop pest modelling with freely available earth observation data. Existing monitoring systems allow farmers to share data on pest incidence to ensure the timely and limited use of treatments. This maximizes crop yield while minimizing cost and environmental damage. While effective, these systems are expensive and logistically challenging in low-resource settings, particularly as they require widespread coverage and monitoring of a range of pests and diseases. They have designed a new, low-cost early warning system to automatically predict a variety of different crop threats using freely available data and will first test it on maize and sorghum crops in Tanzania. They will use an ensemble-based machine learning model to estimate crop losses two to three months before harvest using earth observation datasets including vegetation indices, temperature, and soil moisture. They will then determine how much of these pre-harvest losses are caused by crop pests such as fungi and insects using the Environmental Policy Integrated Climate Model, which simulates their impact on plant health including growth. They plan to integrate their warning system with existing agricultural monitoring networks to improve accuracy. Note: This grant is funded by the Foundation for Food and Agriculture Research (FFAR).
More information about Tools and Technologies for Broad-Scale Pest and Disease Surveillance of Crop Plants in Low-Income Countries (Round 22)