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 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.

Towards high-throughput phenotyping of complex patterned behaviors: Lessons from mouse self-grooming

2011 ◽  
Author(s):  
E. Kyzar ◽  
S. Gaikwad ◽  
M. Pham ◽  
J. Green ◽  
A. Roth ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Phenotyping

Elucidating the Weakly Reversible Cs-Pb-Br Perovskite Nanocrystal Reaction Network with High-Throughput Maps and Transformations

2020 ◽  
Author(s):  
Jakob Dahl ◽  
Xingzhi Wang ◽  
Xiao Huang ◽  
Emory Chan ◽  
Paul Alivisatos
Keyword(s):  
High Throughput ◽  
Dynamic Equilibrium ◽  
Reaction Network ◽  
Substantial Impact ◽  
Equilibrium Behavior ◽  
Automated Method ◽  
Lead Bromide ◽  
Different Shapes ◽  
Complex Chemistry ◽  
Lead Halide

<p>Advances in automation and data analytics can aid exploration of the complex chemistry of nanoparticles. Lead halide perovskite colloidal nanocrystals provide an interesting proving ground: there are reports of many different phases and transformations, which has made it hard to form a coherent conceptual framework for their controlled formation through traditional methods. In this work, we systematically explore the portion of Cs-Pb-Br synthesis space in which many optically distinguishable species are formed using high-throughput robotic synthesis to understand their formation reactions. We deploy an automated method that allows us to determine the relative amount of absorbance that can be attributed to each species in order to create maps of the synthetic space. These in turn facilitate improved understanding of the interplay between kinetic and thermodynamic factors that underlie which combination of species are likely to be prevalent under a given set of conditions. Based on these maps, we test potential transformation routes between perovskite nanocrystals of different shapes and phases. We find that shape is determined kinetically, but many reactions between different phases show equilibrium behavior. We demonstrate a dynamic equilibrium between complexes, monolayers and nanocrystals of lead bromide, with substantial impact on the reaction outcomes. This allows us to construct a chemical reaction network that qualitatively explains our results as well as previous reports and can serve as a guide for those seeking to prepare a particular composition and shape. </p>

An Automated Method for the Determination of Serum Calcium with Glyoxal Bis (2-Hydroxyanil)

1967 ◽  
Vol 13 (6) ◽  
pp. 515-520 ◽  
Author(s):  
Genevieve Farese ◽  
Janice L Schmidt ◽  
Milton Mager
Keyword(s):  
Serum Calcium ◽  
Automated Analysis ◽  
Automated Method ◽  
Good Agreement

Abstract A completely automated analysis is described for the determination of serum calcium with glyoxal bis (2-hydroxyanil) solution (GBHA). The method is simple and precise, and the data obtained are in good agreement with results obtained by the manual GBHA procedure.

scholarly journals High-throughput phenotyping: breaking through the bottleneck in future crop breeding

2021 ◽  
Author(s):  
Peng Song ◽  
Jinglu Wang ◽  
Xinyu Guo ◽  
Wanneng Yang ◽  
Chunjiang Zhao
Keyword(s):  
High Throughput ◽  
Crop Breeding ◽  
High Throughput Phenotyping

scholarly journals High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops

2021 ◽  
Vol 22 (15) ◽  
pp. 8266
Author(s):  
Minsu Kim ◽  
Chaewon Lee ◽  
Subin Hong ◽  
Song Lim Kim ◽  
Jeong-Ho Baek ◽  
...  
Keyword(s):  
Drought Stress ◽  
High Throughput ◽  
Large Scale ◽  
Crop Yields ◽  
Plant Traits ◽  
Rapid Development ◽  
Plant Responses ◽  
Phenotypic Data ◽  
Agricultural Technologies ◽  
High Throughput Phenotyping

Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.

scholarly journals 3D-Volume Rendering of the Pelvis with Emphasis on Paraurethral Structures Based on MRI Scans and Comparisons between 3D Slicer and OsiriX®

2021 ◽  
Vol 45 (3) ◽  
Author(s):  
C. M. Durnea ◽  
S. Siddiqi ◽  
D. Nazarian ◽  
G. Munneke ◽  
P. M. Sedgwick ◽  
...  
Keyword(s):  
Image Processing ◽  
Secondary Analysis ◽  
Three Dimensional ◽  
3D Models ◽  
Clinical Role ◽  
Cross Sectional ◽  
3D Slicer ◽  
Mri Scans ◽  
3D Volume ◽  
Good Agreement

AbstractThe feasibility of rendering three dimensional (3D) pelvic models of vaginal, urethral and paraurethral lesions from 2D MRI has been demonstrated previously. To quantitatively compare 3D models using two different image processing applications: 3D Slicer and OsiriX. Secondary analysis and processing of five MRI scan based image sets from female patients aged 29–43 years old with vaginal or paraurethral lesions. Cross sectional image sets were used to create 3D models of the pelvic structures with 3D Slicer and OsiriX image processing applications. The linear dimensions of the models created using the two different methods were compared using Bland-Altman plots. The comparisons demonstrated good agreement between measurements from the two applications. The two data sets obtained from different image processing methods demonstrated good agreement. Both 3D Slicer and OsiriX can be used interchangeably and produce almost similar results. The clinical role of this investigation modality remains to be further evaluated.

scholarly journals High-throughput phenotyping of two plant-size traits of Eucalyptus species using neural networks

2021 ◽  
Author(s):  
Marcus Vinicius Vieira Borges ◽  
Janielle de Oliveira Garcia ◽  
Tays Silva Batista ◽  
Alexsandra Nogueira Martins Silva ◽  
Fabio Henrique Rojo Baio ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Throughput ◽  
Mean Squared Error ◽  
Vegetation Indices ◽  
Plant Size ◽  
Eucalyptus Species ◽  
Forest Inventories ◽  
Spectral Bands ◽  
High Throughput Phenotyping ◽  
Input Variables

AbstractIn forest modeling to estimate the volume of wood, artificial intelligence has been shown to be quite efficient, especially using artificial neural networks (ANNs). Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices. To do so, using the data obtained by the UAV as input variables, we tested different configurations (number of hidden layers and number of neurons in each layer) of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species. The experimental design was randomized blocks with four replicates, with 20 trees in each experimental plot. The treatments comprised five Eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis, and E. urograndis) and Corymbria citriodora. DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV, so that the multispectral sensor could obtain spectral bands to calculate vegetation indices (VIs). ANNs were then constructed using spectral bands and VIs as input layers, in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first applications of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher estimated r, lower estimated root mean squared error–RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the generated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time.

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