New technology helps combat food insecurity worldwide
Researchers from North Carolina State University have developed a pioneering method combining satellite imagery with machine learning to improve rice crop monitoring. The innovation addresses food security concerns and ensures efficient resource use globally.
Focused on Bangladesh, the study tackles traditional challenges in rice yield estimation, such as time-consuming fieldwork and inaccuracies. “It is a time-consuming and labor-intensive process,” explained lead researcher Varun Tiwari. “They do not have this information in time to make decisions on exports, imports, or crop pricing.”
Using time-series satellite data, the researchers tracked vegetation health, water content, and soil conditions. The trained machine learning model delivered yield predictions with an accuracy rate of 90-92%, providing timely data for decision-makers to improve storage, transportation, and crop management
Bangladesh, where rice contributes significantly to the economy and diet, was an ideal starting point. Tiwari emphasized, “If we can get similar data sets from other regions, we can apply this same framework there.”
The research involves collaboration between NC State, USDA, and international organizations. Funded by the Gates Foundation and USAID, it marks a critical step in addressing global food security. Findings were published in PLOS ONE.