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At least 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Performance of the AstroPix Prototype Module for the Barrel Imaging Calorimeter at the ePIC Detector and in Space-Based Payloads

AstroPix is a high-voltage CMOS (HV-CMOS) monolithic active pixel sensor originally developed to enable precision gamma-ray imaging and spectroscopy in the medium-energy regime (~100 keV–100 MeV) based on the groundwork laid by ATLASpix and MuPix. It features a 500 μm pixel pitch, in-pixel amplification and digitization, and low power consumption (~3-4 mW/cm2), making it scalable for large-area, multilayer telescope detector planes. The detectors have a designed dynamic range of 25 keV to 700 keV. With these features, AstroPix meets the requirements of future space-based high-energy telescopes and the imaging layers of the Barrel Imaging Calorimeter (BIC) in the Electron-Proton/Ion Collider (ePIC) detector at the future Electron-Ion Collider (EIC). For the space-based payload, AstroPix is being integrated into sounding rocket and balloon payloads to demonstrate the technical readiness of the devices. For BIC, AstroPix-based imaging layers interleaved within the lead/scintillating-fiber (Pb/SciFi) sampling calorimeter provide granular shower imaging, enabling key performance features such as electron/pion or gamma/neutral pion separation. As part of the ongoing detector R&D efforts, we have been testing various AstroPix_v3 configurations: the single chip, a quad-chip assembly, a three-layer stack of quad chips, and a 9-chip module that represents the smallest prototype unit of the BIC imaging layer. This presentation will highlight recent performance test results from these AstroPix detector configurations.

Kim, Bobae (ORCID:0000000295396815)↗
DOE OSTI2021

SLOPE daily and 250 m gross primary productivity (GPP) for the CONUS, 2000-2019, Carbon Monitoring System (CMS)

The SatelLite Only Photosynthesis Estimation (SLOPE) GPP product is a daily, 250 m GPP dataset covering the Contiguous United States (CONUS) from 2000 to present with 1 day latency. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time, and observation-based GPP products. This product has been developed to address this critical gap, leveraging a number of MODIS land and atmosphere products. There are three distinct features of the SLOPE GPP production algorithm. (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRv (SANIRv) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Crop Data Layer (CDL) product to predict dynamic C4 crop fraction. PAR, SANIRv and C4 fraction are used to drive a parsimonious model with only two slope parameters to estimate GPP along with a quantitative uncertainty on a per-pixel and daily basis. The slope GPP product has a R2 = 0.84 and a root-mean-square error (RMSE) of 1.65 gC m-2 d-1, evaluated against from 50 AmeriFlux eddy covariance sites (332 site-years). Archived data from 2000 to 2019 are publicly available at the NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). Data from 2020 are available from the authors upon request. All data are projected in the standard MODIS Land Integerized Sinusoidal tile map projection. Each processing tile is in size of 4800 pixels by 4800 pixels, representing approximately 1200 km by 1200 km land region. In addition to the GPP product, SLOPE PAR, SANIRV, and C4 fraction, along with their uncertainties, are also released.

Jiang, Chongya↗
DOE OSTI2017

Diffraction-Based Optical Switching with MEMS

In this article, we are presenting an overview of MEMS-based (Micro-Electro-Mechanical System) optical switch technology starting from the reflective two-dimensional (2D) and three-dimensional (3D) MEMS implementations. To further increase the speed of the MEMS from these devices, the mirror size needs to be reduced. Small mirror size prevents efficient reflection but favors a diffraction-based approach. Two implementations have been demonstrated, one using the Texas Instruments DLP (Digital Light Processing), and the other an LCoS-based (Liquid Crystal on Silicon) SLM (Spatial Light Modulator). These switches demonstrated the benefit of diffraction, by independently achieving high speed, efficiency, and high number of ports. We also demonstrated for the first time that PSK (Phase Shift Keying) modulation format can be used with diffraction-based devices. To be truly effective in diffraction mode, the MEMS pixels should modulate the phase of the incident light. We are presenting our past and current efforts to manufacture a new type of MEMS where the pixels are moving in the vertical direction. The original structure is a 32 x 32 phase modulator array with high contrast grating pixels, and we are introducing a new sub-wavelength linear array capable of a 310 kHz modulation rate

36 MATERIALS SCIENCE↗
DOE OSTI2015

ENTROPY VS. ENERGY WAVEFORM PROCESSING: A COMPARISON ON THE HEAT EQUATION

Virtually all modern imaging devices function by collecting either electromagnetic or acoustic backscattered waves and using the energy carried by these waves to determine pixel values that build up what is basically an ”energy” picture. However, waves also carry ”informa- tion” that also may be used to compute the pixel values in an image. We have employed several measures of information, all of which are based on different forms of entropy. Numerous published studies have demonstrated the advantages of entropy, or “information imaging”, over conventional methods for materials characterization and medical imaging. Similar results also have been obtained with microwaves. The most sensitive information measure appears to be the joint entropy of the backscattered wave and a reference signal. A typical study is comprised of repeated acquisition of backscattered waves from a specimen that is changing slowing with acquisition time or location. The sensitivity of repeated experimental observations of such a slowly changing quantity may be defined as the mean variation (i.e., observed change) divided by mean variance (i.e., observed noise). We compute the sensitivity for joint entropy and signal energy measurements assuming that noise is Gaussian and using Wiener integration to compute the required mean values and variances. These can be written as solutions to the Heat equation, which permits estimation of their magnitudes. There always exists a reference such that joint entropy has larger variation and smaller variance than the corresponding quantities for signal energy, matching observations of several studies. Moreover, a general prescription for finding an “optimal” reference for the joint entropy emerges, which also has been validated in several studies.

Entropy↗
DOE OSTI2023

Automated Quantification of Wind Turbine Blade Leading Edge Erosion from Field Images

Wind turbine blade leading edge erosion is a major source of power production loss and early detection benefits optimization of repair strategies. Two machine learning (ML) models are developed and evaluated for automated quantification of the areal extent, morphology and nature (deep, shallow) of damage from field images. The supervised ML model employs convolutional neural networks (CNN) and learns features (specific types of damage) present in an annotated set of training images. The unsupervised approach aggregates pixel intensity thresholding with calculation of pixel-by-pixel shadow ratio (PTS) to independently identify features within images. The models are developed and tested using a dataset of 140 field images. The images sample across a range of blade orientation, aspect ratio, lighting and resolution. Each model (CNN v PTS) is applied to quantify the percent area of the visible blade that is damaged and classifies the damage into deep or shallow using only the images as input. Both models successfully identify approximately 65% of total damage area in the independent images, and both perform better at quantifying deep damage. The CNN is more successful at identifying shallow damage and exhibits better performance when applied to the images after they are preprocessed to a common blade orientation.

Aird, Jeanie A.↗
DOE OSTI2018

Event Centroiding Applied to Energy-Resolved Neutron Imaging at LANSCE

The energy-dependence of the neutron cross section provides vastly different contrast mechanisms than polychromatic neutron radiography if neutron energies can be selected for imaging applications. In recent years, energy-resolved neutron imaging (ERNI) with epi-thermal neutrons, utilizing neutron absorption resonances for contrast as well as for quantitative density measurements, was pioneered at the Flight Path 5 beam line at LANSCE and continues to be refined. In this work, we present event centroiding, i.e., the determination of the center-of-gravity of a detection event on an imaging detector to allow sub-pixel spatial resolution and apply it to the many frames collected for energy-resolved neutron imaging at a pulsed neutron source. While event centroiding was demonstrated at thermal neutron sources, it has not been applied to energy-resolved neutron imaging, where the energy resolution requires to be preserved, and we present a quantification of the possible resolution as a function of neutron energy. For the 55 μm pixel size of the detector used for this study, we found a resolution improvement from ~80 μm to ~22 μm using pixel centroiding while fully preserving the energy resolution.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Corn response to climate stress detected with satellite-based NDVI time series

Corn growth conditions and yield are closely dependent on climate variability. Leaf growth, measured as the leaf area index, can be used to identify changes in crop growth in response to climate stress. This research was conducted to capture patterns of spatial and temporal corn leaf growth under climate stress for the St. Joseph River watershed, in northeastern Indiana. Leaf growth is represented by the Normalized Difference Vegetative Index (NDVI) retrieved from multiple years (2000–2010) of Landsat 5 TM images. By comparing NDVI values for individual image dates with the derived normal curve, the response of crop growth to environmental factors is quantified as NDVI residuals. Regression analysis revealed a significant relationship between yield and NDVI residual during the pre-silking period, indicating that NDVI residuals reflect crop stress in the early growing period that impacts yield. Both the mean NDVI residuals and the percentage of image pixels where corn was under stress (risky pixel rate) are significantly correlated with water stress. Dry weather is prone to hamper potential crop growth, with stress affecting most of the observed corn pixels in the area. Oversupply of rainfall at the end of the growing season was not found to have a measurable effect on crop growth, while above normal precipitation earlier in the growing season reduces the risk of yield loss at the watershed scale. Furthermore, the spatial extent of stress is much lower when precipitation is above normal than under dry conditions, masking the impact of small areas of yield loss at the watershed scale.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Cleaning Images with Gaussian Process Regression

Many approaches to astronomical data reduction and analysis cannot tolerate missing data: corrupted pixels must first have their values imputed. This paper presents astrofix, a robust and flexible image imputation algorithm based on Gaussian process regression. Through an optimization process, astrofix chooses and applies a different interpolation kernel to each image, using a training set extracted automatically from that image. It naturally handles clusters of bad pixels and image edges and adapts to various instruments and image types. For bright pixels, the mean absolute error of astrofix is several times smaller than that of median replacement and interpolation by a Gaussian kernel. We demonstrate good performance on both imaging and spectroscopic data, including the SBIG 6303 0.4 m telescope and the FLOYDS spectrograph of Las Cumbres Observatory and the CHARIS integral-field spectrograph on the Subaru Telescope.

42 ENGINEERING↗
DOE OSTI2017

Topology of Large-scale Structures of Galaxies in Two Dimensions—Systematic Effects

We study the two-dimensional topology of galactic distribution when projected onto two-dimensional spherical shells. Using the latest Horizon Run 4 simulation data, we construct the genus of the two-dimensional field and consider how this statistic is affected by late-time nonlinear effects—principally gravitational collapse and redshift space distortion (RSD). We also consider systematic and numerical artifacts, such as shot noise, galaxy bias, and finite pixel effects. We model the systematics using a Hermite polynomial expansion and perform a comprehensive analysis of known effects on the two-dimensional genus, with a view toward using the statistic for cosmological parameter estimation. We find that the finite pixel effect is dominated by an amplitude drop and can be made less than 1% by adopting pixels smaller than 1/3 of the angular smoothing length. Nonlinear gravitational evolution introduces time-dependent coefficients of the zeroth, first, and second Hermite polynomials, but the genus amplitude changes by less than 1% between z = 1 and z = 0 for smoothing scales R{sub G}>9 Mpc/h. Non-zero terms are measured up to third order in the Hermite polynomial expansion when studying RSD. Differences in the shapes of the genus curves in real and redshift space are small when we adopt thick redshift shells, but the amplitude change remains a significant ∼O(10%) effect. The combined effects of galaxy biasing and shot noise produce systematic effects up to the second Hermite polynomial. It is shown that, when sampling, the use of galaxy mass cuts significantly reduces the effect of shot noise relative to random sampling.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Low-energy Electron-track Imaging for a Liquid Argon Time-projection-chamber Telescope Concept Using Probabilistic Deep Learning

The GammaTPC is an MeV-scale single-phase liquid argon time-projection-chamber gamma-ray telescope concept with a novel dual-scale pixel-based charge-readout system. It promises to enable a significant improvement in sensitivity to MeV-scale gamma rays over previous telescopes. The novel pixel-based charge readout allows for imaging of the tracks of electrons scattered by Compton interactions of incident gamma rays. The two primary contributors to the accuracy of a Compton telescope in reconstructing an incident gamma-ray’s original direction are its energy and position resolution. In this work, we focus on using deep learning to optimize the reconstruction of the initial position and direction of electrons scattered in Compton interactions, including using probabilistic models to estimate predictive uncertainty. We show that the deep-learning models are able to predict locations of Compton scatters of MeV-scale gamma rays from simulated 500 μm pixel-based data to better than 1 mm rms error and are sensitive to the initial direction of the scattered electron. We compare and contrast different deep-learning uncertainty estimation algorithms for reconstruction applications. Additionally, we show that event-by-event estimates of the uncertainty of the locations of the Compton scatters can be used to select those events that were reconstructed most accurately, leading to improvement in locating the origin of gamma-ray sources on the sky.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms

Abstract Superresolution (SR) aims to increase the resolution of images by recovering detail. Compared to standard interpolation, deep learning-based approaches learn features and their relationships to leverage prior knowledge of what low-resolution patterns look like in higher resolution. Deep neural networks can also perform image cross-calibration by learning the systematic properties of the target images. While SR for natural images aims to create perceptually convincing results, SR of scientific data requires careful quantitative evaluation. In this work, we demonstrate that deep learning can increase the resolution and calibrate solar imagers belonging to different instrumental generations. We convert solar magnetic field images taken by the Michelson Doppler Imager (resolution ∼2″ pixel −1 ; space based) and the Global Oscillation Network Group (resolution ∼2.″5 pixel −1 ; ground based) to the characteristics of the Helioseismic and Magnetic Imager (resolution ∼0.″5 pixel −1 ; space based). We also establish a set of performance measurements to benchmark deep-learning-based SR and calibration for scientific applications.

Muñoz-Jaramillo, Andrés (ORCID:0000000247160840)↗
DOE OSTI2022

PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study

Understanding a plant's root system architecture (RSA) is crucial for a variety of plant science problem domains including sustainability and climate adaptation. Minirhizotron (MR) technology is a widely-used approach for phenotyping RSA non-destructively by capturing root imagery over time. Precisely segmenting roots from the soil in MR imagery is a critical step in studying RSA features. In this paper, we introduce a large-scale dataset of plant root images captured by MR technology. In total, there are over 72K RGB root images across six different species including cotton, papaya, peanut, sesame, sunflower, and switchgrass in the dataset. The images span a variety of conditions including varied root age, root structures, soil types, and depths under the soil surface. All of the images have been annotated with weak image-level labels indicating whether each image contains roots or not. The image-level labels can be used to support weakly supervised learning in plant root segmentation tasks. In addition, 63K images have been manually annotated to generate pixel-level binary masks indicating whether each pixel corresponds to root or not. These pixel-level binary masks can be used as ground truth for supervised learning in semantic segmentation tasks. By introducing this dataset, we aim to facilitate the automatic segmentation of roots and the research of RSA with deep learning and other image analysis algorithms.

Xu, Weihuang↗
DOE OSTI2021

Inpainting radar missing data regions with deep learning

Abstract. Missing and low-quality data regions are a frequent problem for weather radars. They stem from a variety of sources: beam blockage, instrument failure, near-ground blind zones, and many others. Filling in missing data regions is often useful for estimating local atmospheric properties and the application of high-level data processing schemes without the need for preprocessing and error-handling steps – feature detection and tracking, for instance. Interpolation schemes are typically used for this task, though they tend to produce unrealistically spatially smoothed results that are not representative of the atmospheric turbulence and variability that are usually resolved by weather radars. Recently, generative adversarial networks (GANs) have achieved impressive results in the area of photo inpainting. Here, they are demonstrated as a tool for infilling radar missing data regions. These neural networks are capable of extending large-scale cloud and precipitation features that border missing data regions into the regions while hallucinating plausible small-scale variability. In other words, they can inpaint missing data with accurate large-scale features and plausible local small-scale features. This method is demonstrated on a scanning C-band and vertically pointing Ka-band radar that were deployed as part of the Cloud Aerosol and Complex Terrain Interactions (CACTI) field campaign. Three missing data scenarios are explored: infilling low-level blind zones and short outage periods for the Ka-band radar and infilling beam blockage areas for the C-band radar. Two deep-learning-based approaches are tested, a convolutional neural network (CNN) and a GAN that optimize pixel-level error or combined pixel-level error and adversarial loss respectively. Both deep-learning approaches significantly outperform traditional inpainting schemes under several pixel-level and perceptual quality metrics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

A method for retrieving cloud optical depth ( τ c ) using a UCSD developed ground-based sky imager (USI) is presented. The radiance red–blue ratio (RRBR) method is motivated from the analysis of simulated images of various τ c produced by a radiative transfer model (RTM). From these images the basic parameters affecting the radiance and red–blue ratio (RBR) of a pixel are identified as the solar zenith angle ( θ 0 ), τ c , solar pixel angle/scattering angle ( θ s ), and pixel zenith angle/view angle ( θ z ). The effects of these parameters are described and the functions for radiance, I λ τ c , θ 0 , θ s , θ z , and RBR τ c , θ 0 , θ s , θ z are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τ c , where RBR increases with τ c up to about τ c = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured I λ meas θ s , θ z , in addition to RBR meas θ s , θ z , to obtain a unique solution for τ c . The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement (ARM) program site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min et al. (2003) method for overcast skies. τ c values ranged from 0 to 80 with values over 80, being capped and registered as 80. A τ c RMSE of 2.5 between the Min et al. (2003) method and the USI are observed. The MWR and USI have an RMSE of 2.2, which is well within the uncertainty of the MWR. In conclusion, the procedure developed here provides a foundation to test and develop other cloud detection algorithms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Abstract. Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Hyperspectral Image Classification Using Graph Clustering Methods

Hyperspectral imagery is a challenging modality due to the dimension of the pixels which can range from hundreds to over a thousand frequencies depending on the sensor. Most methods in the literature reduce the dimension of the data using a method such as principal component analysis, however this procedure can lose information. More recently methods have been developed to address classification of large datasets in high dimensions. This paper presents two classes of graph-based classification methods for hyperspectral imagery. Using the full dimensionality of the data, we consider a similarity graph based on pairwise comparisons of pixels. The graph is segmented using a pseudospectral algorithm for graph clustering that requires information about the eigenfunctions of the graph Laplacian but does not require computation of the full graph. We develop a parallel version of the Nyström extension method to randomly sample the graph to construct a low rank approximation of the graph Laplacian. With at most a few hundred eigenfunctions, we can implement the clustering method designed to solve a variational problem for a graph-cut-based semi-supervised or unsupervised classification problem. We implement OpenMP directive-based parallelism in our algorithms and show performance improvement and strong, almost ideal, scaling behavior. The method can handle very large datasets including a video sequence with over a million pixels, and the problem of segmenting a data set into a pre-determined number of classes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

IM3 Phase 2 Official Simulations: GCAM-Demeter-SELECT Annualized Land Use and Land Cover, Wood Harvest and Fertilization Data with Dynamic Urbanization Harmonized to CLM Land Definitions at 0.125 Degrees

Annualized land use land cover data, including wood harvest and fertilizer use data from the Global Change Analysis Model (GCAM) downscaled to 0.125 degrees for couping with the Community Land Model (CLM). GCAM here refers to GCAM-USA v5.3.im3 which has an enhanced electricity sector and an updated data system needed to represent regional to local scale dynamics. Data is also harmonized with future urbanization projections from the Spatially-Explicit, Long-term, Empirical City developmenT (SELECT) model. Projections/Data are generated using the demeter land use and land cover downscaling model. Original projections were generated at 0.05 degrees before being aggregared to 0.125 degrees. Projections are available for 8 alternative scenarios. Two versions of final data are included- one with managed forests or harvested forest area per pixel broken out and one with the same aggregated into total forests. Following folders are included: demeter_78_PFT_output:This is the final output of dynamic land use land cover change for 78 PFTs as required by CLM raw_outputs_incl_managed_forest: This is the final output but with managed forests broken out as a different PFT. Essentially a 79th PFT is added. wood_harvest_outputs: Wood harvest output per pixel in gC/m2 fertilization_outputs: Fertilizer use per pixel in gN/m2 Each NetCDF file in each folder represents a projection for a separate year, scenario. Land use outputs are organized as PFT level data saved as subdata. Link to GCAM version used- https://data.msdlive.org/records/yb23g-44274 Link to SELECT documentation -https://www.sciencedirect.com/science/article/pii/S1364815219301707 Link to CLM documentation- - https://www.cesm.ucar.edu/models/clm In case of questions contact- kanishka.narayan@pnnl.gov

GCAM-USA↗
DOE OSTI2018

An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping

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.

47 OTHER INSTRUMENTATION↗