Lights, camera, action: high-throughput plant phenotyping is ready for a close-up

Fahlgren, Noah; Gehan, Malia A.; Baxter, Ivan

doi:10.1016/j.pbi.2015.02.006

Title: Lights, camera, action: high-throughput plant phenotyping is ready for a close-up

Full Record
Other Related Research

Authors:: Fahlgren, Noah; Gehan, Malia A.; Baxter, Ivan

Publication Date:: Wed Apr 01 00:00:00 EDT 2015

Sponsoring Org.:: USDOE

OSTI Identifier:: 1251531

Grant/Contract Number:: DOE-SC-008796

Resource Type:: Published Article

Journal Name:: Current Opinion in Plant Biology

Additional Journal Information:: Journal Name: Current Opinion in Plant Biology Journal Volume: 24 Journal Issue: C; Journal ID: ISSN 1369-5266

Publisher:: Elsevier

Country of Publication:: United Kingdom

Language:: English

Citation Formats


                    Fahlgren, Noah, Gehan, Malia A., and Baxter, Ivan. Lights, camera, action: high-throughput plant phenotyping is ready for a close-up.  United Kingdom: N. p., 2015. 
Web.  doi:10.1016/j.pbi.2015.02.006.

Copy to clipboard


                    Fahlgren, Noah, Gehan, Malia A., & Baxter, Ivan. Lights, camera, action: high-throughput plant phenotyping is ready for a close-up.  United Kingdom.  https://doi.org/10.1016/j.pbi.2015.02.006

Copy to clipboard


                    Fahlgren, Noah, Gehan, Malia A., and Baxter, Ivan. Wed .  
"Lights, camera, action: high-throughput plant phenotyping is ready for a close-up".  United Kingdom.  https://doi.org/10.1016/j.pbi.2015.02.006.

Copy to clipboard


                    
@article{osti_1251531,

  title        = {Lights, camera, action: high-throughput plant phenotyping is ready for a close-up},

  author       = {Fahlgren, Noah and Gehan, Malia A. and Baxter, Ivan},

  abstractNote = {},

  doi          = {10.1016/j.pbi.2015.02.006},

  journal      = {Current Opinion in Plant Biology},

  number       = C,

  volume       = 24,

  place        = {United Kingdom},

  year         = {Wed Apr 01 00:00:00 EDT 2015},

  month        = {Wed Apr 01 00:00:00 EDT 2015}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Publisher's Version of Record
https://doi.org/10.1016/j.pbi.2015.02.006

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 373 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

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

Journal Article Berry, Jeffrey C. ; Fahlgren, Noah ; Pokorny, Alexandria A. ; ... - PeerJ

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, wemore »« less
Cited by 19
https://doi.org/10.7717/peerj.5727

Full Text Available
A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer

Journal Article Wijewardane, Nuwan K. ; Zhang, Huichun ; Yang, Jinliang ; ... - Journal of Experimental Botany

Abstract Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive, and models show poor transferability among different datasets. This study had three specific objectives: first, to assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum; second, to evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur); and third, to investigate the usefulness of this spectral library for predicting external datasetsmore »« less
https://doi.org/10.1093/jxb/erad129
PlantCV v2: Image analysis software for high-throughput plant phenotyping

Journal Article Gehan, Malia A. ; Fahlgren, Noah ; Abbasi, Arash ; ... - PeerJ

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here in this paper we present the details andmore »« less
Cited by 121
https://doi.org/10.7717/peerj.4088

Full Text Available
UAV-Based High Resolution Thermal Imaging for Vegetation Monitoring, and Plant Phenotyping Using ICI 8640 P, FLIR Vue Pro R 640, and thermoMap Cameras

Journal Article Sagan, Vasit ; Maimaitijiang, Maitiniyazi ; Sidike, Paheding ; ... - Remote Sensing

The growing popularity of Unmanned Aerial Vehicles (UAVs) in recent years, along with decreased cost and greater accessibility of both UAVs and thermal imaging sensors, has led to the widespread use of this technology, especially for precision agriculture and plant phenotyping. There are several thermal camera systems in the market that are available at a low cost. However, their efficacy and accuracy in various applications has not been tested. In this study, three commercially available UAV thermal cameras, including ICI 8640 P-series (Infrared Cameras Inc., USA), FLIR Vue Pro R 640 (FLIR Systems, USA), and thermoMap (senseFly, Switzerland) have beenmore »« less
Cited by 110
https://doi.org/10.3390/rs11030330
A Versatile Phenotyping System and Analytics Platform Reveals Diverse Temporal Responses to Water Availability in Setaria

Journal Article Fahlgren, Noah ; Feldman, Maximilian ; Gehan, Malia A. ; ... - Molecular Plant
Cited by 112
https://doi.org/10.1016/j.molp.2015.06.005

Similar Records