Title: Active Learning with Gaussian Processes for High Throughput Phenotyping,
Abstract: A looming question that must be solved before robotic plant phenotyping
capabilities can have significant impact to crop improvement programs is
scalability. High Throughput Phenotyping (HTP) uses robotic technologies to
analyze crops in order to determine species with favorable traits, however, the
current practices rely on exhaustive coverage and data collection from the
entire crop field being monitored under the breeding experiment. This works
well in relatively small agricultural fields but can not be scaled to the
larger ones, thus limiting the progress of genetics research. In this work, we
propose an active learning algorithm to enable an autonomous system to collect
the most informative samples in order to accurately learn the distribution of
phenotypes in the field with the help of a Gaussian Process model. We
demonstrate the superior performance of our proposed algorithm compared to the
current practices on sorghum phenotype data collection.