/Robust Plant Phenotyping via Model-based Optimization

Robust Plant Phenotyping via Model-based Optimization

Paloma Sodhi, Hanqi Sun, Barnabas Poczos and David Wettergreen
Conference Paper, IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS, October, 2018

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Abstract

Plant phenotyping is the measurement of observable plant traits. Current methods for phenotyping in the field are labour intensive and error prone. High throughput plant phenotyping in an automated and noninvasive manner is crucial to accelerating plant breeding methods. Occlusions and non-ideal sensing conditions is a major problem for high throughput plant phenotyping with most state-of-the-art 3D phenotyping algorithms relying heavily on heuristics or hand-tuned parameters. To address this problem, we present a novel model-based optimization approach for estimating plant physical traits from plant units called phytomers. The proposed approach involves sampling parameterized 3D plant models from an underlying probability distribution. It then optimizes, making the mass of this probability distribution approach true parameters of the model. Reformulating the phenotyping objective as a search in the space of plant models lets us reason about the plant structure in a holistic manner without having to rely on hand-tuned parameters. This makes our approach robust to noise and occlusions as frequently encountered in real world environments. We evaluate our approach for plant units taken across simulated, greenhouse and field environments. This work furthers field-based robotic phenotyping capabilities paving the way for plant biologists to study the coupled effect of genetics and environment on improving crop yields.

BibTeX Reference
@conference{Sodhi-2018-109380,
author = {Paloma Sodhi and Hanqi Sun and Barnabas Poczos and David Wettergreen},
title = {Robust Plant Phenotyping via Model-based Optimization},
booktitle = {IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS},
year = {2018},
month = {October},
keywords = {Plant Phenotyping, Computer Vision, 3D reconstruction, Stochastic Optimization, Importance Sampling},
}
2018-10-22T11:02:19+00:00