A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis
High-throughput phenotyping system has become more and more popular in plant science research. The data analysis for such a system typically involves two steps: plant feature extraction through image processing and statistical analysis for the extracted features. The current approach is to perform t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science (AAAS)
2020-01-01
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| Series: | Plant Phenomics |
| Online Access: | http://dx.doi.org/10.34133/2020/7481687 |
| _version_ | 1798018610434670592 |
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| author | Ronghao Wang Yumou Qiu Yuzhen Zhou Zhikai Liang James C. Schnable |
| author_facet | Ronghao Wang Yumou Qiu Yuzhen Zhou Zhikai Liang James C. Schnable |
| author_sort | Ronghao Wang |
| collection | DOAJ |
| description | High-throughput phenotyping system has become more and more popular in plant science research. The data analysis for such a system typically involves two steps: plant feature extraction through image processing and statistical analysis for the extracted features. The current approach is to perform those two steps on different platforms. We develop the package “implant” in R for both robust feature extraction and functional data analysis. For image processing, the “implant” package provides methods including thresholding, hidden Markov random field model, and morphological operations. For statistical analysis, this package can produce nonparametric curve fitting with its confidence region for plant growth. A functional ANOVA model to test for the treatment and genotype effects on the plant growth dynamics is also provided. |
| first_indexed | 2024-04-11T16:27:04Z |
| format | Article |
| id | doaj.art-85637d16ed2848eba83d2e7e19f91662 |
| institution | Directory Open Access Journal |
| issn | 2643-6515 |
| language | English |
| last_indexed | 2024-04-11T16:27:04Z |
| publishDate | 2020-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Plant Phenomics |
| spelling | doaj.art-85637d16ed2848eba83d2e7e19f916622022-12-22T04:14:08ZengAmerican Association for the Advancement of Science (AAAS)Plant Phenomics2643-65152020-01-01202010.34133/2020/7481687A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve AnalysisRonghao Wang0Yumou Qiu1Yuzhen Zhou2Zhikai Liang3James C. Schnable4Department of Statistics, University of Nebraska-Lincoln, Lincoln 68503, USADepartment of Statistics, Iowa State University, Ames 50011, USADepartment of Statistics, University of Nebraska-Lincoln, Lincoln 68503, USADepartment of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108, USACenter for Plant Science Innovation, Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln 68503, USAHigh-throughput phenotyping system has become more and more popular in plant science research. The data analysis for such a system typically involves two steps: plant feature extraction through image processing and statistical analysis for the extracted features. The current approach is to perform those two steps on different platforms. We develop the package “implant” in R for both robust feature extraction and functional data analysis. For image processing, the “implant” package provides methods including thresholding, hidden Markov random field model, and morphological operations. For statistical analysis, this package can produce nonparametric curve fitting with its confidence region for plant growth. A functional ANOVA model to test for the treatment and genotype effects on the plant growth dynamics is also provided.http://dx.doi.org/10.34133/2020/7481687 |
| spellingShingle | Ronghao Wang Yumou Qiu Yuzhen Zhou Zhikai Liang James C. Schnable A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis Plant Phenomics |
| title | A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis |
| title_full | A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis |
| title_fullStr | A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis |
| title_full_unstemmed | A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis |
| title_short | A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis |
| title_sort | high throughput phenotyping pipeline for image processing and functional growth curve analysis |
| url | http://dx.doi.org/10.34133/2020/7481687 |
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