Active Learning with Gaussian Processes for High Throughput Phenotyping
This work addresses scalability issues for crop improvement programs by reducing data collection needs in large agricultural fields, though it is incremental as it builds on existing active learning and Gaussian process techniques.
The authors tackled the scalability problem in high-throughput plant phenotyping by proposing an active learning algorithm with Gaussian processes to select informative samples, achieving superior performance on sorghum data compared to exhaustive coverage methods.
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.