Machine learning approaches to improve three basic plant phenotyping tasks using 3D point clouds

Ziamtsov, I., Navlakha, S. (October 2019) Machine learning approaches to improve three basic plant phenotyping tasks using 3D point clouds. Plant Physiol. ISSN 0032-0889

URL: https://www.ncbi.nlm.nih.gov/pubmed/31591152
DOI: 10.1104/pp.19.00524

Abstract

Developing automated methods to efficiently process large volumes of point cloud data remains a challenge for 3D plant phenotyping applications. Here, we describe the development of machine learning methods to tackle three primary challenges in plant phenotyping: lamina/stem classification, lamina counting, and stem skeletonization. For classification, we assessed and validated the accuracy of our methods on a dataset of 54 3D shoot architectures, representing multiple growth conditions and developmental timepoints for two Solanaceous species, tomato (Solanum lycopersicum cv 75 m82D) and Nicotiana benthamiana. Using deep learning, we classified lamina versus stems with 97.8% accuracy. Critically, we also demonstrated the robustness of our method to growth conditions and species that have not been trained on, which is important in practical applications but is often untested. For lamina counting, we developed an enhanced region-growing algorithm to reduce oversegmentation; this method achieved 86.6% accuracy, outperforming prior methods developed for this problem. Finally, for stem skeletonization, we developed an enhanced tip detection technique, which ran an order of magnitude faster and generated more precise skeleton architectures than prior methods. Overall, our improvements enable higher throughput and accurate extraction of phenotypic properties from 3D point cloud data.

Item Type: Paper
Subjects: bioinformatics > computational biology > algorithms > machine learning
bioinformatics > genomics and proteomics > annotation > phenotyping
organism description > plant
CSHL Authors:
Communities: CSHL labs > Navlakha lab
Depositing User: Matthew Dunn
Date: 7 October 2019
Date Deposited: 06 Nov 2019 17:25
Last Modified: 06 Nov 2019 17:25
Related URLs:
URI: https://repository.cshl.edu/id/eprint/38642

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