scholarly journals A Method of High Throughput Monitoring Crop Physiology Using Chlorophyll Fluorescence and Multispectral Imaging

2018 ◽  
Vol 9 ◽  
Author(s):  
Heng Wang ◽  
Xiangjie Qian ◽  
Lan Zhang ◽  
Sailong Xu ◽  
Haifeng Li ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
High Throughput ◽  
Multispectral Imaging ◽  
Crop Physiology

scholarly journals Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuai Che ◽  
Guoying Du ◽  
Ning Wang ◽  
Kun He ◽  
Zhaolan Mo ◽  
...  
Keyword(s):  
High Throughput ◽  
Vegetation Index ◽  
Multispectral Imaging ◽  
Vegetation Indices ◽  
Spectral Imaging ◽  
Shielding Effect ◽  
Quadratic Model ◽  
Biomass Estimation ◽  
Marine Aquaculture ◽  
Significant Difference

Abstract Background Pyropia is an economically advantageous genus of red macroalgae, which has been cultivated in the coastal areas of East Asia for over 300 years. Realizing estimation of macroalgae biomass in a high-throughput way would great benefit their cultivation management and research on breeding and phenomics. However, the conventional method is labour-intensive, time-consuming, manually destructive, and prone to human error. Nowadays, high-throughput phenotyping using unmanned aerial vehicle (UAV)-based spectral imaging is widely used for terrestrial crops, grassland, and forest, but no such application in marine aquaculture has been reported. Results In this study, multispectral images of cultivated Pyropia yezoensis were taken using a UAV system in the north of Haizhou Bay in the midwestern coast of Yellow Sea. The exposure period of P. yezoensis was utilized to prevent the significant shielding effect of seawater on the reflectance spectrum. The vegetation indices of normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference of red edge (NDRE) were derived and indicated no significant difference between the time that P. yezoensis was completely exposed to the air and 1 h later. The regression models of the vegetation indices and P. yezoensis biomass per unit area were established and validated. The quadratic model of DVI (Biomass = − 5.550DVI2 + 105.410DVI + 7.530) showed more accuracy than the other index or indices combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and relative estimated accuracy (Ac) values of 0.925, 8.06, and 74.93%, respectively. The regression model was further validated by consistently predicting the biomass with a high R2 value of 0.918, RMSE of 8.80, and Ac of 82.25%. Conclusions This study suggests that the biomass of Pyropia can be effectively estimated using UAV-based spectral imaging with high accuracy and consistency. It also implied that multispectral aerial imaging is potential to assist digital management and phenomics research on cultivated macroalgae in a high-throughput way.

scholarly journals An LED-based multi-actinic illumination system for the high throughput study of photosynthetic light responses

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5589
Author(s):  
João Serôdio ◽  
William Schmidt ◽  
Jörg C. Frommlet ◽  
Gregor Christa ◽  
Matthew R. Nitschke
Keyword(s):  
Chlorophyll Fluorescence ◽  
High Throughput ◽  
Light Stress ◽  
Limiting Factor ◽  
Phenotypic Traits ◽  
Light Responses ◽  
Illumination System ◽  
Digital Manipulation ◽  
Multiple Samples

The responses of photosynthetic organisms to light stress are of interest for both fundamental and applied research. Functional traits related to the photoinhibition, the light-induced loss of photosynthetic efficiency, are particularly interesting as this process is a key limiting factor of photosynthetic productivity in algae and plants. The quantitative characterization of light responses is often time-consuming and calls for cost-effective high throughput approaches that enable the fast screening of multiple samples. Here we present a novel illumination system based on the concept of ‘multi-actinic imaging’ of in vivo chlorophyll fluorescence. The system is based on the combination of an array of individually addressable low power RGBW LEDs and custom-designed well plates, allowing for the independent illumination of 64 samples through the digital manipulation of both exposure duration and light intensity. The illumination system is inexpensive and easily fabricated, based on open source electronics, off-the-shelf components, and 3D-printed parts, and is optimized for imaging of chlorophyll fluorescence. The high-throughput potential of the system is illustrated by assessing the functional diversity in light responses of marine macroalgal species, through the fast and simultaneous determination of kinetic parameters characterizing the response to light stress of multiple samples. Although the presented illumination system was primarily designed for the measurement of phenotypic traits related to photosynthetic activity and photoinhibition, it can be potentially used for a number of alternative applications, including the measurement of chloroplast phototaxis and action spectra, or as the basis for microphotobioreactors.

Abstract 2679: Clinical validation of high throughput AQUA and multispectral imaging for breast cancer.

2013 ◽  
Author(s):  
Cristen K. Hays ◽  
Danielle Murphy ◽  
Bana Mouwakeh ◽  
Peter Miller ◽  
Clifford Hoyt ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Throughput ◽  
Multispectral Imaging ◽  
Clinical Validation

scholarly journals Establishment of integrated protocols for automated high throughput kinetic chlorophyll fluorescence analyses

Plant Methods ◽  
2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Henning Tschiersch ◽  
Astrid Junker ◽  
Rhonda C. Meyer ◽  
Thomas Altmann
Keyword(s):  
Chlorophyll Fluorescence ◽  
High Throughput

Chlorophyll fluorescence imaging as tool for understanding the impact of fungal diseases on plant performance: a phenomics perspective

2009 ◽  
Vol 36 (11) ◽  
pp. 880 ◽  
Author(s):  
Julie D. Scholes ◽  
Stephen A. Rolfe
Keyword(s):  
Chlorophyll Fluorescence ◽  
Fluorescence Imaging ◽  
High Throughput ◽  
Fungal Pathogens ◽  
Photosynthetic Performance ◽  
Plant Performance ◽  
Chlorophyll Fluorescence Imaging ◽  
Infected Leaf ◽  
Experimental Conditions ◽  
The Impact

Chlorophyll fluorescence imaging is a non-invasive, non-destructive means with which to examine the impact of fungal pathogens on the photosynthetic metabolism of host plants. As such, it has great potential for screening purposes in high-throughput phenomics environments. However, there is great diversity in the responses of plants to different plant-fungal pathogens and the choice of suitable experimental conditions and protocols and interpretation of the results requires both preliminary laboratory experiments and an understanding of the biology of the specific plant-pathogen interaction. In this review, we examine the interaction between biotrophic, hemi-biotrophic and necrotrophic fungal pathogens and their hosts to illustrate the extent to which chlorophyll fluorescence imaging can be used to detect the presence of disease before the appearance of visible symptoms, distinguish between compatible and incompatible fungal interactions, identify heterogeneity in photosynthetic performance within the infected leaf and provide insights into the underlying mechanisms. The limitations and challenges of using chlorophyll fluorescence imaging in high throughput screens is discussed.

Chlorophyll fluorescence screening of Arabidopsis thaliana for CO2 sensitive photorespiration and photoinhibition mutants

2009 ◽  
Vol 36 (11) ◽  
pp. 867 ◽  
Author(s):  
Murray R. Badger ◽  
Hossein Fallahi ◽  
Sarah Kaines ◽  
Shunichi Takahashi
Keyword(s):  
Arabidopsis Thaliana ◽  
Chlorophyll Fluorescence ◽  
Fluorescence Imaging ◽  
High Throughput ◽  
Rapid Decline ◽  
Arabidopsis Mutants ◽  
High Co2 ◽  
Chlorophyll Fluorescence Parameter ◽  
Low Co2 ◽  
Fluorescence Parameter

Exposure of Arabidopsis thaliana (L.) photorespiration mutants to air leads to a rapid decline in the Fv/Fm chlorophyll fluorescence parameter, reflecting a decline in PSII function and an onset of photoinhibition. This paper demonstrates that chlorophyll fluorescence imaging of Fv/Fm can be used as an easy and efficient means of detecting Arabidopsis mutants that are impaired in various aspects of photorespiration. This screen was developed to be sensitive and high throughput by the use of exposure to zero CO2 conditions and the use of array grids of 1-week-old Arabidopsis seedlings as the starting material for imaging. Using this procedure, we screened ~25 000 chemically mutagenised M2 Arabidopsis seeds and recovered photorespiration phenotypes (reduction in Fv/Fm at low CO2) at a frequency of ~4 per 1000 seeds. In addition, we also recovered mutants that showed reduced Fv/Fm at high CO2. Of this group, we detected a novel ‘reverse photorespiration’ phenotype that showed a high CO2 dependent reduction in Fv/Fm. This chlorophyll fluorescence screening technique promises to reveal novel mutants associated with photorespiration and photoinhibition.

scholarly journals UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence

2019 ◽  
Vol 11 (4) ◽  
pp. 410 ◽  
Author(s):  
Yiannis Ampatzidis ◽  
Victor Partel
Keyword(s):  
Artificial Intelligence ◽  
Data Acquisition ◽  
High Throughput ◽  
Multispectral Imaging ◽  
Phenotypic Trait ◽  
Phenotypic Characteristics ◽  
Individual Tree ◽  
High Throughput Phenotyping ◽  
Citrus Varieties ◽  
Canopy Size

Traditional plant breeding evaluation methods are time-consuming, labor-intensive, and costly. Accurate and rapid phenotypic trait data acquisition and analysis can improve genomic selection and accelerate cultivar development. In this work, a technique for data acquisition and image processing was developed utilizing small unmanned aerial vehicles (UAVs), multispectral imaging, and deep learning convolutional neural networks to evaluate phenotypic characteristics on citrus crops. This low-cost and automated high-throughput phenotyping technique utilizes artificial intelligence (AI) and machine learning (ML) to: (i) detect, count, and geolocate trees and tree gaps; (ii) categorize trees based on their canopy size; (iii) develop individual tree health indices; and (iv) evaluate citrus varieties and rootstocks. The proposed remote sensing technique was able to detect and count citrus trees in a grove of 4,931 trees, with precision and recall of 99.9% and 99.7%, respectively, estimate their canopy size with overall accuracy of 85.5%, and detect, count, and geolocate tree gaps with a precision and recall of 100% and 94.6%, respectively. This UAV-based technique provides a consistent, more direct, cost-effective, and rapid method to evaluate phenotypic characteristics of citrus varieties and rootstocks.

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