Visual Yield Mapping with Optimal and Generative Sampling Strategies
Project Head: Stephen T. Nuske
This research project aims to develop methods to automatically collect visual image data to infer, estimate and forecast crop yields — producing yield maps with high-resolution, across large scales and with accuracy. To achieve efficiency and accuracy, statistical sampling strategies are designed for human-robot teams that are optimal in the number of samples, location of samples, cost of sampling and accuracy of crop estimates.
