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
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 |
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Subjects: | bioinformatics > genomics and proteomics > annotation bioinformatics bioinformatics > genomics and proteomics bioinformatics > computational biology > algorithms bioinformatics > computational biology bioinformatics > computational biology > algorithms > machine learning organs, tissues, organelles, cell types and functions bioinformatics > genomics and proteomics > annotation > phenotyping organism description > plant organs, tissues, organelles, cell types and functions > tissues types and functions > root organs, tissues, organelles, cell types and functions > tissues types and functions |
CSHL Authors: | |
Communities: | CSHL labs > Navlakha lab |
Depositing User: | Matthew Dunn |
Date: | 7 October 2019 |
Date Deposited: | 06 Nov 2019 17:25 |
Last Modified: | 05 Feb 2024 21:17 |
PMCID: | PMC6878014 |
Related URLs: | |
URI: | https://repository.cshl.edu/id/eprint/38642 |
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