Streamlining Data Collection

Streamlining Ground-Truth Data Collection using Drones and Machine Learning

A key challenge for the crop analytics community is generating accurate ground-truth data rapidly and at a low cost.  This project will assess the potential for using drone imagery in Rwanda to create faster and less expensive ground-truth datasets for crop and yield estimate models without decreasing their accuracy. RTI and the team will generate ground-truth data in three ways: (1) using conventional methods to collect ground observations, (2) using drone imagery and a web-based viewer to generate label points, and (3) using ground-truth observations to densify the number of locations using computer labeling. The project will compare the three approaches for cost, speed, and model performance.


Marti Bruening, Research Triangle Institue (RTI)