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

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5727 ◽  
Author(s):  
Jeffrey C. Berry ◽  
Noah Fahlgren ◽  
Alexandria A. Pokorny ◽  
Rebecca S. Bart ◽  
Kira M. Veley
Keyword(s):  
High Throughput ◽  
Linear Models ◽  
Correction Method ◽  
Automated Image Analysis ◽  
Plant Phenotyping ◽  
Image Brightness ◽  
Automated Method ◽  
Color Profile ◽  
High Throughput Phenotyping ◽  
High Throughput Imaging

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.

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

2018 ◽  
Author(s):  
Jeffrey C. Berry ◽  
Noah Fahlgren ◽  
Alexandria A. Pokorny ◽  
Rebecca Bart ◽  
Kira M. Veley
Keyword(s):  
High Throughput ◽  
Linear Models ◽  
Correction Method ◽  
Automated Image Analysis ◽  
Plant Phenotyping ◽  
Image Brightness ◽  
Automated Method ◽  
Color Profile ◽  
High Throughput Phenotyping ◽  
High Throughput Imaging

ABSTRACTHigh-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.

scholarly journals Estimation of the Chlorophyll Concentration in Sorghum Using Three High Throughput Phenotyping Imaging Techniques

2021 ◽  
Author(s):  
Huichun Zhang ◽  
Yufeng Ge ◽  
Xinyan Xie ◽  
Abbas Atefi ◽  
Nuwan Wijewardane ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
High Throughput ◽  
Vegetation Index ◽  
Linear Models ◽  
Imaging Techniques ◽  
Chlorophyll Concentration ◽  
Imaging Data ◽  
Leaf Chlorophyll ◽  
High Throughput Phenotyping ◽  
High Throughput Imaging

Abstract BackgroundLeaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput phenotyping. High throughput imaging techniques are now widely used for nondestructive analysis of plant phenotypic traits. In this study three imaging modules, namely, RGB, hyperspectral, and fluorescence imaging, were used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and regression models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. ResultsModels that included two additional variables, DAS (day after sowing) and SLW (specific leaf weight), were also investigated to improve the prediction of chlorophyll. R2 for chlorophyll concentration for multiple linear models at various color components were 0.77 for R, 0.79 for G, 0.70 for B. To obtain additional spectral information, color component H, S, and I were calculated after color spaces being transformed. The result of HSI space showed that R2 for chlorophyll concentration for multiple linear models were 0.67 for H, 0.88 for S, 0.77 for I. The R2 values for different hyperspectral index like the ratio vegetation index (RVI), the normalized difference vegetation index (NDVI), modified chlorophyll absorption ratio index (MCARI) between 0.77 and 0.78. R2=0.79 was obtained with fluorescence image. Partial least squares regression (PLSR) was employed to using the selected vegetation indices computed from different imaging data to estimate the chlorophyll concentration for sorghum plants. Among all the imaging data, chlorophyll content was predicted with high accuracy (R2 from 0.84 to 2.92, RPD from 2.49 to 3.58). ConclusionAccording to the Akaike's Information Criterion (AIC) error function, the model was better fitted based on images, DAS and SLW than that based on images and DAS. This study indicated that the accuracy for chlorophyll estimation was increased by the image traits combined with DAS and SLW. High throughput imaging provides a simple, rapid, and nondestructive method to estimate the leaf chlorophyll concentration.

scholarly journals Predicting plant biomass accumulation from image-derived parameters

2016 ◽  
Author(s):  
Dijun Chen ◽  
Rongli Shi ◽  
Jean-Michel Pape ◽  
Christian Klukas
Keyword(s):  
High Throughput ◽  
Plant Biomass ◽  
Quantitative Relationship ◽  
Biomass Accumulation ◽  
Plant Phenotyping ◽  
Suitable Model ◽  
The European Union ◽  
Relative Contribution ◽  
Crop Plant Research ◽  
High Throughput Phenotyping

AbstractImage-based high-throughput phenotyping technologies have been rapidly developed in plant science recently and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologist. However, it is a great challenge to find a suitable model to predict plant biomass in the context of high-throughput phenotyping. In the present study, we constructed several models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to three consecutive barley experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model, in particular the cross-experiment performance, is promising to relieve the phenotyping bottleneck in biomass measurement in breeding applications. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of plant biomass outcome. What’s more, the methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass.One-sentence SummaryWe demonstrated that plant biomass can be accurately predicted from image-based parameters in the context of high-throughput phenotyping.FootnotesThis work was supported by the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), the Robert Bosch Stiftung (32.5.8003.0116.0) and the Federal Agency for Agriculture and Food (BEL, 15/12-13, 530-06.01-BiKo CHN) and the Federal Ministry of Education and Research (BMBF, 0315958A and 031A053B). This research was furthermore enabled with support of the European Plant Phenotyping Network (EPPN, grant agreement no. 284443) funded by the FP7 Research Infrastructures Programme of the European Union.

scholarly journals High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

2021 ◽  
Vol 8 ◽  
Author(s):  
Daoliang Li ◽  
Chaoqun Quan ◽  
Zhaoyang Song ◽  
Xiang Li ◽  
Guanghui Yu ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Agronomic Traits ◽  
Global Climate ◽  
Plant Phenotyping ◽  
Phenotypic Data ◽  
Operating Modes ◽  
Sensing Platform ◽  
Exploitation And Utilization ◽  
High Throughput Phenotyping

Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.

scholarly journals Conventional and hyperspectral time-series imaging of maize lines widely used in field trials

2017 ◽  
Author(s):  
Zhikai Liang ◽  
Piyush Pandey ◽  
Vincent Stoerger ◽  
Yuhang Xu ◽  
Yumou Qiu ◽  
...  
Keyword(s):  
High Throughput ◽  
Measurement Errors ◽  
Hyperspectral Image ◽  
Image Data ◽  
Field Trials ◽  
Plant Phenotyping ◽  
Wide Range ◽  
High Throughput Phenotyping ◽  
University Of Nebraska Lincoln ◽  
The Cost

ABSTRACTMaize (Zea mays ssp. mays) is one of three crops, along with rice and wheat, responsible for more than 1/2 of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping is currently the largest constraint on plant breeding efforts. Datasets linking new types of high throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision based tools. A set of maize inbreds – primarily recently off patent lines – were phenotyped using a high throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high density genotyping, and scored for a core set of 13 phenotypes in field trials across 13 North American states in two years by the Genomes to Fields consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released. Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors influencing yield plasticity.

scholarly journals Low-Cost Automated Vectors and Modular Environmental Sensors for Plant Phenotyping

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3319
Author(s):  
Stuart A. Bagley ◽  
Jonathan A. Atkinson ◽  
Henry Hunt ◽  
Michael H. Wilson ◽  
Tony P. Pridmore ◽  
...  
Keyword(s):  
High Throughput ◽  
Low Cost ◽  
Time Lapse ◽  
Plant Phenotyping ◽  
Environmental Sensors ◽  
Controlled Conditions ◽  
Sensor Platform ◽  
High Throughput Phenotyping ◽  
Controlled Environments ◽  
Time Lapse Imaging

High-throughput plant phenotyping in controlled environments (growth chambers and glasshouses) is often delivered via large, expensive installations, leading to limited access and the increased relevance of “affordable phenotyping” solutions. We present two robot vectors for automated plant phenotyping under controlled conditions. Using 3D-printed components and readily-available hardware and electronic components, these designs are inexpensive, flexible and easily modified to multiple tasks. We present a design for a thermal imaging robot for high-precision time-lapse imaging of canopies and a Plate Imager for high-throughput phenotyping of roots and shoots of plants grown on media plates. Phenotyping in controlled conditions requires multi-position spatial and temporal monitoring of environmental conditions. We also present a low-cost sensor platform for environmental monitoring based on inexpensive sensors, microcontrollers and internet-of-things (IoT) protocols.

scholarly journals ScreenSeed as a novel high throughput seed germination phenotyping method

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicolas Merieux ◽  
Pierre Cordier ◽  
Marie-Hélène Wagner ◽  
Sylvie Ducournau ◽  
Sophie Aligon ◽  
...  
Keyword(s):  
Arabidopsis Thaliana ◽  
Image Analysis ◽  
Seed Germination ◽  
High Throughput ◽  
Automated Image Analysis ◽  
Experimental Protocol ◽  
Visual Scoring ◽  
Radicle Protrusion ◽  
High Throughput Phenotyping ◽  
Fast Procedure

AbstractA high throughput phenotyping tool for seed germination, the ScreenSeed technology, was developed with the aim of screening genotype responsiveness and chemical drugs. This technology was presently used with Arabidopsis thaliana seeds to allow characterizing seed samples germination behavior by incubating seeds in 96-well microplates under defined conditions and detecting radicle protrusion through the seed coat by automated image analysis. This study shows that this technology provides a fast procedure allowing to handle thousands of seeds without compromising repeatability or accuracy of the germination measurements. Potential biases of the experimental protocol were assessed through statistical analyses of germination kinetics. Comparison of the ScreenSeed procedure with commonly used germination tests based upon visual scoring displayed very similar germination kinetics.

scholarly journals Evaluating Geometric Measurement Accuracy Based on 3D Reconstruction of Automated Imagery in a Greenhouse

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2270 ◽  
Author(s):  
Jing Zhou ◽  
Xiuqing Fu ◽  
Leon Schumacher ◽  
Jianfeng Zhou
Keyword(s):  
Measurement Error ◽  
High Throughput ◽  
3D Models ◽  
Dynamic Responses ◽  
Three Dimensions ◽  
Automated Method ◽  
Environmental Variations ◽  
High Throughput Phenotyping ◽  
Geometric Measurement ◽  
Good Agreement

Geometric dimensions of plants are significant parameters for showing plant dynamic responses to environmental variations. An image-based high-throughput phenotyping platform was developed to automatically measure geometric dimensions of plants in a greenhouse. The goal of this paper was to evaluate the accuracy in geometric measurement using the Structure from Motion (SfM) method from images acquired using the automated image-based platform. Images of nine artificial objects of different shapes were taken under 17 combinations of three different overlaps in x and y directions, respectively, and two different spatial resolutions (SRs) with three replicates. Dimensions in x, y and z of these objects were measured from 3D models reconstructed using the SfM method to evaluate the geometric accuracy. A metric power of unit (POU) was proposed to combine the effects of image overlap and SR. Results showed that measurement error of dimension in z is the least affected by overlap and SR among the three dimensions and measurement error of dimensions in x and y increased following a power function with the decrease of POU (R2 = 0.78 and 0.88 for x and y respectively). POUs from 150 to 300 are a preferred range to obtain reasonable accuracy and efficiency for the developed image-based high-throughput phenotyping system. As a study case, the developed system was used to measure the height of 44 plants using an optimal POU in greenhouse environment. The results showed a good agreement (R2 = 92% and Root Mean Square Error = 9.4 mm) between the manual and automated method.

scholarly journals Microarray Core Detection by Geometric Restoration

2012 ◽  
Vol 35 (5-6) ◽  
pp. 381-393 ◽  
Author(s):  
Jimmy C. Azar ◽  
Christer Busch ◽  
Ingrid B. Carlbom
Keyword(s):  
High Throughput ◽  
Tissue Microarrays ◽  
Automated Image Analysis ◽  
High Throughput Analysis ◽  
Tissue Samples ◽  
The Core ◽  
Automated Method ◽  
Precise Shape ◽  
Diagnosis Of Cancer ◽  
Detection And Localization

Whole-slide imaging of tissue microarrays (TMAs) holds the promise of automated image analysis of a large number of histopathological samples from a single slide. This demands high-throughput image processing to enable analysis of these tissue samples for diagnosis of cancer and other conditions. In this paper, we present a completely automated method for the accurate detection and localization of tissue cores that is based on geometric restoration of the core shapes without placing any assumptions on grid geometry. The method relies on hierarchical clustering in conjunction with the Davies-Bouldin index for cluster validation in order to estimate the number of cores in the image wherefrom we estimate the core radius and refine this estimate using morphological granulometry. The final stage of the algorithm reconstructs circular discs from core sections such that these discs cover the entire region of each core regardless of the precise shape of the core. The results show that the proposed method is able to reconstruct core locations without any evidence of localization. Furthermore, the algorithm is more efficient than existing methods based on the Hough transform for circle detection. The algorithm’s simplicity, accuracy, and computational efficiency allow for automated high-throughput analysis of microarray images.

scholarly journals Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding

2021 ◽  
Vol 13 (14) ◽  
pp. 2670
Author(s):  
Paul Herzig ◽  
Peter Borrmann ◽  
Uwe Knauer ◽  
Hans-Christian Klück ◽  
David Kilias ◽  
...  
Keyword(s):  
Grain Yield ◽  
Plant Breeding ◽  
High Throughput ◽  
Prediction Accuracy ◽  
Vegetation Period ◽  
Plant Phenotyping ◽  
Yield Prediction ◽  
Sensor Systems ◽  
Barley Breeding ◽  
High Throughput Phenotyping

With advances in plant genomics, plant phenotyping has become a new bottleneck in plant breeding and the need for reliable high-throughput plant phenotyping techniques has emerged. In the face of future climatic challenges, it does not seem appropriate to continue to solely select for grain yield and a few agronomically important traits. Therefore, new sensor-based high-throughput phenotyping has been increasingly used in plant breeding research, with the potential to provide non-destructive, objective and continuous plant characterization that reveals the formation of the final grain yield and provides insights into the physiology of the plant during the growth phase. In this context, we present the comparison of two sensor systems, Red-Green-Blue (RGB) and multispectral cameras, attached to unmanned aerial vehicles (UAV), and investigate their suitability for yield prediction using different modelling approaches in a segregating barley introgression population at three environments with weekly data collection during the entire vegetation period. In addition to vegetation indices, morphological traits such as canopy height, vegetation cover and growth dynamics traits were used for yield prediction. Repeatability analyses and genotype association studies of sensor-based traits were compared with reference values from ground-based phenotyping to test the use of conventional and new traits for barley breeding. The relative height estimation of the canopy by UAV achieved high precision (up to r = 0.93) and repeatability (up to R2 = 0.98). In addition, we found a great overlap of detected significant genotypes between the reference heights and sensor-based heights. The yield prediction accuracy of both sensor systems was at the same level and reached a maximum prediction accuracy of r2 = 0.82 with a continuous increase in precision throughout the entire vegetation period. Due to the lower costs and the consumer-friendly handling of image acquisition and processing, the RGB imagery seems to be more suitable for yield prediction in this study.

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