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 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 Evaluation of RapidEye vegetation indices for prediction of biophysical/biochemical variables of winter wheat

2018 ◽  
Vol 30 ◽  
pp. 63-74
Author(s):  
Ilina Kamenova ◽  
Petar Dimitrov ◽  
Rusina Yordanova
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Linear Models ◽  
Chlorophyll Concentration ◽  
Vegetation Indices ◽  
Area Index ◽  
Leaf Chlorophyll ◽  
Red Edge ◽  
Absorbed Photosynthetically Active Radiation

The aim of the study is to evaluate the possibility for using RapidEye data for prediction of Leaf Area Index (LAI), fraction of Absorbed Photosynthetically Active Radiation (fAPAR), fraction of vegetation Cover (fCover), leaf Chlorophyll Concentration (CC) and Canopy Chlorophyll Content (CCC) of winter wheat. The relation of a number of vegetation indices (VIs) with these crop variables are accessed based on a regression analysis. Indices, which make use of the red edge band, such as Chlorophyll Index red edge (CIre) and red edge Normalized Difference Vegetation Index (reNDVI), were found most useful, resulting in linear models with R2 of 0.67, 0.71, 0.72, and 0.76 for fCover, LAI, CCC, and fAPAR respectively. CC was not related with any of the VIs.

scholarly journals Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhou Tang ◽  
Atit Parajuli ◽  
Chunpeng James Chen ◽  
Yang Hu ◽  
Samuel Revolinski ◽  
...  
Keyword(s):  
Prediction Model ◽  
High Throughput ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Selection Process ◽  
Forage Legume ◽  
Breeding Programs ◽  
High Throughput Phenotyping ◽  
Fully Automatic ◽  
Uav Images

AbstractAlfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

Functional analysis of high-content high-throughput imaging data

2016 ◽  
Vol 44 (11) ◽  
pp. 1903-1919 ◽  
Author(s):  
Xiaoqi Jiang ◽  
Steven Wink ◽  
Bob van de Water ◽  
Annette Kopp-Schneider
Keyword(s):  
Functional Analysis ◽  
High Throughput ◽  
Imaging Data ◽  
High Throughput Imaging

scholarly journals Estimating Leaf Chlorophyll Content of Buffaloberry Using Normalized Difference Vegetation Index Sensors

2021 ◽  
pp. 1-7
Author(s):  
Ji-Jhong Chen ◽  
Shuyang Zhen ◽  
Youping Sun
Keyword(s):  
Chlorophyll Content ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Red Light ◽  
Leaf Chlorophyll Content ◽  
Nir Light ◽  
Leaf Chlorophyll ◽  
Chlorophyll Extraction ◽  
Relative Chlorophyll Content

Commercial optical chlorophyll meters estimate relative chlorophyll content using the ratio of transmitted red light and near-infrared (NIR) light emitted from a red light-emitting diode (LED) and an NIR LED. Normalized difference vegetation index (NDVI) sensors have red and NIR light detectors and may be used to estimate chlorophyll content by detecting the transmitted red and NIR light through leaves. In this study, leaf chlorophyll content of ‘Torrey’ buffaloberry (Shepherdia ×utahensis) plants treated with 0 mm [zero nitrogen (N)], 2 mm (medium N), or 4 mm (ample N) ammonium nitrate for 3 weeks were evaluated using two commercial chlorophyll meters and NDVI sensors. The absolute chlorophyll content was determined using chlorophyll extraction. Our results showed that plants receiving ample N and medium N had decreased transmitted red light (i.e., greater absorption in red light). Measurements of optical chlorophyll meters, NDVI sensors, and chlorophyll extraction similarly showed that plants receiving medium N and ample N had greater leaf chlorophyll content than those receiving zero N. Relative leaf chlorophyll content estimated using NDVI sensors correlated positively with those from the chlorophyll meters (P < 0.0001; r2 range, 0.56–0.82). Therefore, our results indicate that NDVI measurements are sensitive to leaf chlorophyll content. These NDVI sensors, or specialized sensors developed using similar principles, can be used to estimate the relative chlorophyll content of nursery crops and help growers adjust fertilization to improve plant growth and nutrient status.

scholarly journals Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Alex X Lu ◽  
Yolanda T Chong ◽  
Ian Shen Hsu ◽  
Bob Strome ◽  
Louis-Francois Handfield ◽  
...  
Keyword(s):  
Subcellular Localization ◽  
High Throughput ◽  
Cell Biology ◽  
Protein Localization ◽  
Imaging Data ◽  
Environmental Chemical ◽  
Genetic Perturbations ◽  
Proteomic Responses ◽  
High Throughput Imaging ◽  
Localization Behavior

The evaluation of protein localization changes on a systematic level is a powerful tool for understanding how cells respond to environmental, chemical, or genetic perturbations. To date, work in understanding these proteomic responses through high-throughput imaging has catalogued localization changes independently for each perturbation. To distinguish changes that are targeted responses to the specific perturbation or more generalized programs, we developed a scalable approach to visualize the localization behavior of proteins across multiple experiments as a quantitative pattern. By applying this approach to 24 experimental screens consisting of nearly 400,000 images, we differentiated specific responses from more generalized ones, discovered nuance in the localization behavior of stress-responsive proteins, and formed hypotheses by clustering proteins that have similar patterns. Previous approaches aim to capture all localization changes for a single screen as accurately as possible, whereas our work aims to integrate large amounts of imaging data to find unexpected new cell biology.

An Efficient Pipeline for Crop Image Extraction and Vegetation Index Derivation Using Unmanned Aerial Systems

2020 ◽  
Vol 63 (4) ◽  
pp. 1133-1146
Author(s):  
Beichen Lyu ◽  
Stuart D. Smith ◽  
Yexiang Xue ◽  
Katy M. Rainey ◽  
Keith Cherkauer
Keyword(s):  
Data Processing ◽  
High Throughput ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Unmanned Aerial Systems ◽  
Growth Stages ◽  
Processing Pipeline ◽  
High Throughput Phenotyping ◽  
Aerial Systems

HighlightsThis study addresses two computational challenges in high-throughput phenotyping: scalability and efficiency.Specifically, we focus on extracting crop images and deriving vegetation indices using unmanned aerial systems.To this end, we outline a data processing pipeline, featuring a crop localization algorithm and trie data structure.We demonstrate the efficacy of our approach by computing large-scale and high-precision vegetation indices in a soybean breeding experiment, where we evaluate soybean growth under water inundation and temporal change.Abstract. In agronomy, high-throughput phenotyping (HTP) can provide key information for agronomists in genomic selection as well as farmers in yield prediction. Recently, HTP using unmanned aerial systems (UAS) has shown advantages in both cost and efficiency. However, scalability and efficiency have not been well studied when processing images in complex contexts, such as using multispectral cameras, and when images are collected during early and late growth stages. These challenges hamper further analysis to quantify phenotypic traits for large-scale and high-precision applications in plant breeding. To solve these challenges, our research team previously built a three-step data processing pipeline, which is highly modular. For this project, we present improvements to the previous pipeline to improve canopy segmentation and crop plot localization, leading to improved accuracy in crop image extraction. Furthermore, we propose a novel workflow based on a trie data structure to compute vegetation indices efficiently and with greater flexibility. For each of our proposed changes, we evaluate the advantages by comparison with previous models in the literature or by comparing processing results using both the original and improved pipelines. The improved pipeline is implemented as two MATLAB programs: Crop Image Extraction version 2 (CIE 2.0) and Vegetation Index Derivation version 1 (VID 1.0). Using CIE 2.0 and VID 1.0, we compute canopy coverage and normalized difference vegetation indices (NDVIs) for a soybean phenotyping experiment. We use canopy coverage to investigate excess water stress and NDVIs to evaluate temporal patterns across the soybean growth stages. Both experimental results compare favorably with previous studies, especially for approximation of soybean reproductive stage. Overall, the proposed methodology and implemented experiments provide a scalable and efficient paradigm for applying HTP with UAS to general plant breeding. Keywords: Data processing pipeline, High-throughput phenotyping, Image processing, Soybean breeding, Unmanned aerial systems, Vegetation indices.

scholarly journals Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy

2018 ◽  
Vol 8 (9) ◽  
pp. 1435 ◽  
Author(s):  
Xiaochen Zou ◽  
Iina Haikarainen ◽  
Iikka Haikarainen ◽  
Pirjo Mäkelä ◽  
Matti Mõttus ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Imaging Spectroscopy ◽  
Leaf Angle ◽  
Field Crops ◽  
Leaf Chlorophyll ◽  
Simple Ratio ◽  
The Relationship

Leaf area index (LAI) is an important biophysical variable for understanding the radiation use efficiency of field crops and their potential yield. On a large scale, LAI can be estimated with the help of imaging spectroscopy. However, recent studies have revealed that the leaf angle greatly affects the spectral reflectance of the canopy and hence imaging spectroscopy data. To investigate the effects of the leaf angle on LAI-sensitive narrowband vegetation indices, we used both empirical measurements from field crops and model-simulated data generated by the PROSAIL canopy reflectance model. We found the relationship between vegetation indices and LAI to be notably affected, especially when the leaf mean tilt angle (MTA) exceeded 70 degrees. Of the indices used in the study, the modified soil-adjusted vegetation index (MSAVI) was most strongly affected by leaf angles, while the blue normalized difference vegetation index (BNDVI), the green normalized difference vegetation index (GNDVI), the modified simple ratio using the wavelength of 705 nm (MSR705), the normalized difference vegetation index (NDVI), and the soil-adjusted vegetation index (SAVI) were only affected for sparse canopies (LAI < 3) and MTA exceeding 60°. Generally, the effect of MTA on the vegetation indices increased as a function of decreasing LAI. The leaf chlorophyll content did not affect the relationship between BNDVI, MSAVI, NDVI, and LAI, while the green atmospherically resistant index (GARI), GNDVI, and MSR705 were the most strongly affected indices. While the relationship between SR and LAI was somewhat affected by both MTA and the leaf chlorophyll content, the simple ratio (SR) displayed only slight saturation with LAI, regardless of MTA and the chlorophyll content. The best index found in the study for LAI estimation was BNDVI, although it performed robustly only for LAI > 3 and showed considerable nonlinearity. Thus, none of the studied indices were well suited for across-species LAI estimation: information on the leaf angle would be required for remote LAI measurement, especially at low LAI values. Nevertheless, narrowband indices can be used to monitor the LAI of crops with a constant leaf angle distribution.

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