ArticlePeerJ2018
An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping.
Article in PeerJ, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Article
- Time course measurements of leaf elevation angles during shade avoidance response inmicroPublication biology · 2025Article
- Application of Image-Based Phenotyping for QTL Identification of Tiller Angle in Rice (Plants (Basel, Switzerland) · 2024Article
- Visualizing Plant Responses: Novel Insights Possible Through Affordable Imaging Techniques in the Greenhouse.Sensors (Basel, Switzerland) · 2024Article
- Continental-scale associations of Arabidopsis thaliana phyllosphere members with host genotype and drought.Nature microbiology · 2024Article
- Quantifying physiological trait variation with automated hyperspectral imaging in rice.Frontiers in plant science · 2023Article
- Identification of beneficial and detrimental bacteria impacting sorghum responses to drought using multi-scale and multi-system microbiome comparisons.The ISME journal · 2022Article
- A comparison of ImageJ and machine learning based image analysis methods to measure cassava bacterial blight disease severity.Plant methods · 2022Article
- Advances in field-based high-throughput photosynthetic phenotyping.Journal of experimental botany · 2022Review
- Article
- Genomic impact of stress-induced transposable element mobility in Arabidopsis.Nucleic acids research · 2021Article
- Article
- Assessing Rice Salinity Tolerance: From Phenomics to Association Mapping.Methods in molecular biology (Clifton, N.J.) · 2021Article
- High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network.PloS one · 2021Article
- A data-driven perspective on the colours of metal-organic frameworks.Chemical science · 2020Article
- Monitoring Plant Status and Fertilization Strategy through Multispectral Images.Sensors (Basel, Switzerland) · 2020Article
- Affordable remote monitoring of plant growth in facilities using Raspberry Pi computers.Applications in plant sciences · 2019Article
- A Robust Automated Image-Based Phenotyping Method for Rapid Vegetative Screening of Wheat Germplasm for Nitrogen Use Efficiency.Frontiers in plant science · 2019Article
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 has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.
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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.