Fast and Efficient Root Phenotyping via Pose Estimation
Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant’s phenotype. Despite its prevalence, segmentation-based...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science (AAAS)
2024-01-01
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| Series: | Plant Phenomics |
| Online Access: | https://spj.science.org/doi/10.34133/plantphenomics.0175 |
| _version_ | 1797210147395731456 |
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| author | Elizabeth M. Berrigan Lin Wang Hannah Carrillo Kimberly Echegoyen Mikayla Kappes Jorge Torres Angel Ai-Perreira Erica McCoy Emily Shane Charles D. Copeland Lauren Ragel Charidimos Georgousakis Sanghwa Lee Dawn Reynolds Avery Talgo Juan Gonzalez Ling Zhang Ashish B. Rajurkar Michel Ruiz Erin Daniels Liezl Maree Shree Pariyar Wolfgang Busch Talmo D. Pereira |
| author_facet | Elizabeth M. Berrigan Lin Wang Hannah Carrillo Kimberly Echegoyen Mikayla Kappes Jorge Torres Angel Ai-Perreira Erica McCoy Emily Shane Charles D. Copeland Lauren Ragel Charidimos Georgousakis Sanghwa Lee Dawn Reynolds Avery Talgo Juan Gonzalez Ling Zhang Ashish B. Rajurkar Michel Ruiz Erin Daniels Liezl Maree Shree Pariyar Wolfgang Busch Talmo D. Pereira |
| author_sort | Elizabeth M. Berrigan |
| collection | DOAJ |
| description | Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant’s phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train) and error-prone (derived geometric features are sensitive to instance mask integrity). Here, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that pose-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/. |
| first_indexed | 2024-04-24T10:05:58Z |
| format | Article |
| id | doaj.art-1913d55f03c04426831e3224da0eae83 |
| institution | Directory Open Access Journal |
| issn | 2643-6515 |
| language | English |
| last_indexed | 2024-04-24T10:05:58Z |
| publishDate | 2024-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Plant Phenomics |
| spelling | doaj.art-1913d55f03c04426831e3224da0eae832024-04-12T22:56:33ZengAmerican Association for the Advancement of Science (AAAS)Plant Phenomics2643-65152024-01-01610.34133/plantphenomics.0175Fast and Efficient Root Phenotyping via Pose EstimationElizabeth M. Berrigan0Lin Wang1Hannah Carrillo2Kimberly Echegoyen3Mikayla Kappes4Jorge Torres5Angel Ai-Perreira6Erica McCoy7Emily Shane8Charles D. Copeland9Lauren Ragel10Charidimos Georgousakis11Sanghwa Lee12Dawn Reynolds13Avery Talgo14Juan Gonzalez15Ling Zhang16Ashish B. Rajurkar17Michel Ruiz18Erin Daniels19Liezl Maree20Shree Pariyar21Wolfgang Busch22Talmo D. Pereira23Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Salk Institute for Biological Studies, La Jolla, CA 92037, USA.Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant’s phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train) and error-prone (derived geometric features are sensitive to instance mask integrity). Here, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that pose-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.https://spj.science.org/doi/10.34133/plantphenomics.0175 |
| spellingShingle | Elizabeth M. Berrigan Lin Wang Hannah Carrillo Kimberly Echegoyen Mikayla Kappes Jorge Torres Angel Ai-Perreira Erica McCoy Emily Shane Charles D. Copeland Lauren Ragel Charidimos Georgousakis Sanghwa Lee Dawn Reynolds Avery Talgo Juan Gonzalez Ling Zhang Ashish B. Rajurkar Michel Ruiz Erin Daniels Liezl Maree Shree Pariyar Wolfgang Busch Talmo D. Pereira Fast and Efficient Root Phenotyping via Pose Estimation Plant Phenomics |
| title | Fast and Efficient Root Phenotyping via Pose Estimation |
| title_full | Fast and Efficient Root Phenotyping via Pose Estimation |
| title_fullStr | Fast and Efficient Root Phenotyping via Pose Estimation |
| title_full_unstemmed | Fast and Efficient Root Phenotyping via Pose Estimation |
| title_short | Fast and Efficient Root Phenotyping via Pose Estimation |
| title_sort | fast and efficient root phenotyping via pose estimation |
| url | https://spj.science.org/doi/10.34133/plantphenomics.0175 |
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