iff.org ), and MatLab functions controlled image acquisition, assessed image quality, and converted each RAW image file into tagged image file format (TIFF). Custom MatLab code checked that the appropriate camera settings for focal length, f‐number, exposure, and body tilt matched defined values. These scripts are available at: https://github.com/maizeumn/cold-phenotyping ( https://doi.org/10.5281/zenodo.1553411 ). If an image failed the quality and standardization checks, the script identified the problem and prompted the user to retake the image. Approved images in RAW format were automatically stored in a directory corresponding to the date of image acquisition. Sample tracking informatio
Open resource ↗https://github.com/maizeumn/cold-phenotyping · lines:45-53Unverified paper record
Classifying cold-stress responses of inbred maize seedlings using RGB imaging.
Plant direct · 2 Jan 2019 · 10.1002/pld3.104
Abstract
Increasing the tolerance of maize seedlings to low-temperature episodes could mitigate the effects of increasing climate variability on yield. To aid progress toward this goal, we established a growth chamber-based system for subjecting seedlings of 40 maize inbred genotypes to a defined, temporary cold stress while collecting digital profile images over a 9-daytime course. Image analysis performed with PlantCV software quantified shoot height, shoot area, 14 other morphological traits, and necrosis identified by color analysis. Hierarchical clustering of changes in growth rates of morphological traits and quantification of leaf necrosis over two time intervals resulted in three clusters of genotypes, which are characterized by unique responses to cold stress. For any given genotype, the set of traits with similar growth rates is unique. However, the patterns among traits are different between genotypes. Cold sensitivity was not correlated with the latitude where the inbred varieties were released suggesting potential further improvement for this trait. This work will serve as the basis for future experiments investigating the genetic basis of recovery to cold stress in maize seedlings.
Plant phenotyping relevance
RGB画像とPlantCVによる多形質・壊死の定量を中核とする植物表現型解析システムを構築・適用しており、冷ストレス応答の表現型抽出が主要目的である。
abstractwe established a growth chamber-based system for subjecting seedlings of 40 maize inbred genotypes to a defined, temporary cold stress while collecting digital profile images over a 9-daytime course.
abstractImage analysis performed with PlantCV software quantified shoot height, shoot area, 14 other morphological traits, and necrosis identified by color analysis.
Code and data availability
The paper's authors publicly deposited their image-acquisition, metadata/QR-code generation, and analysis scripts on GitHub (archived on Zenodo), and the input TIFF images used for phenotyping on Cyverse Data Commons. PlantCV (Zenodo 1408271) is a third-party tool, not a paper-specific asset, and the bioRxiv DOI is the
e on Cyverse Data Commons ( https://doi.org/10.7946/p2t63c ). Numerical outputs from PlantCV pipeline as merged .csv files and scripts including R code used to generate figures, Perl code to generate QR code and metadata sheets, and scripts for image acquisition are available here: https://github.com/maizeumn/cold-phenotyping ( https://doi.org/10.5281/zenodo.1553411 ). README files, both on Cyverse for image data and Github for scripts, provide short explanations and usage for each file provided. 2.5. Data analysis 2.5.1. Plant growth rates For experiments examining the effect of cold stresses of different durations on plant growth, points on line plots represented the mean of six plants p
Open resource ↗10.5281/zenodo.1553411 · lines:54-65four images for each plant analyzed, which captured various processing steps and documented the quality of plant segmentation (Supporting Information Figure S1 ). The individual .csv output files were merged using a python script, and the data were analyzed in R. Input TIFF format images are available on Cyverse Data Commons ( https://doi.org/10.7946/p2t63c ). Numerical outputs from PlantCV pipeline as merged .csv files and scripts including R code used to generate figures, Perl code to generate QR code and metadata sheets, and scripts for image acquisition are available here: https://github.com/maizeumn/cold-phenotyping ( https://doi.org/10.5281/zenodo.1553411 ). README files, bo
Open resource ↗10.7946/p2t63c · lines:54-65This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.