ArticlePlant phenomics (Washington, D.C.)2020
A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis.
Article in Plant phenomics (Washington, D.C.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
7 citing papers in PubMed.
- Can Distributed Ledgers Help to Overcome the Need of Labeled Data for Agricultural Machine Learning Tasks?Plant phenomics (Washington, D.C.) · 2023Review
- Rapid online plant leaf area change detection with high-throughput plant image data.Journal of applied statistics · 2023Article
- High-Throughput Field Plant Phenotyping: A Self-Supervised Sequential CNN Method to Segment Overlapping Plants.Plant phenomics (Washington, D.C.) · 2023Article
- Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform.Plant phenomics (Washington, D.C.) · 2023Article
- From multivariate to functional data analysis: fundamentals, recent developments, and emerging areas.Journal of multivariate analysis · 2022Article
- A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data.Scientific reports · 2022Article
- KAT4IA:Plant phenomics (Washington, D.C.) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.