Active Learning with Gaussian Processes for High Throughput Phenotyping - Robotics Institute Carnegie Mellon University

Active Learning with Gaussian Processes for High Throughput Phenotyping

Conference Paper, Proceedings of 18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS '19), pp. 2078 - 2080, May, 2019

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.

BibTeX

@conference{Kumar-2019-117853,
author = {Sumit Kumar and Wenhao Luo and George Kantor and Katia Sycara},
title = {Active Learning with Gaussian Processes for High Throughput Phenotyping},
booktitle = {Proceedings of 18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS '19)},
year = {2019},
month = {May},
pages = {2078 - 2080},
}