Visual Yield Mapping with Optimal and Generative Sampling Strategies
This Project is no longer active.
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.
