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Ultrasonics
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June 2, 2022
A review of ultrasonic sensing and machine learning methods to monitor industrial processes
Alexander L Bowler, Michael P Pound, Nicholas J Watson
Plant Phenomics (Washington, D.C.)
|
April 27, 2026
Deep learning in plant phenotyping: the first ten years
Jordan Ubbens, Ian Stavness, Michael P Pound, et al.
Trends in Plant Science
|
August 21, 2012
What lies beneath: underlying assumptions in bioimage analysis
Tony P Pridmore, Andrew P French, Michael P Pound
Frontiers in Plant Science
|
September 28, 2020
Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning
Faraz Ahmad Khan, Ute Voß, Michael P Pound, et al.
Current Opinion in Biotechnology
|
July 23, 2018
Uncovering the hidden half of plants using new advances in root phenotyping
Jonathan A Atkinson, Michael P Pound, Malcolm J Bennett, et al.
Plant Physiology
|
October 22, 2014
Automated recovery of three-dimensional models of plant shoots from multiple color images
Michael P Pound, Andrew P French, Erik H Murchie, et al.
Annals of Botany
|
January 10, 2017
Image-based 3D canopy reconstruction to determine potential productivity in complex multi-species crop systems
Alexandra J Burgess, Renata Retkute, Michael P Pound, et al.
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|
May 15, 2020
Three Dimensional Root CT Segmentation using Multi-Resolution Encoder-Decoder Networks
Mohammadreza Soltaninejad, Craig J Sturrock, Marcus Griffiths, et al.
Plant Methods
|
March 14, 2017
AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping
Michael P Pound, Susan Fozard, Mercedes Torres Torres, et al.
The Plant Cell
|
April 5, 2012
CellSeT: novel software to extract and analyze structured networks of plant cells from confocal images
Michael P Pound, Andrew P French, Darren M Wells, et al.
Page
of 3
Search research articles
Search
Showing results (1-10 of 27) with videos related to
Sort By:
Page
of 3
Ultrasonics
|
June 2, 2022
A review of ultrasonic sensing and machine learning methods to monitor industrial processes
Alexander L Bowler, Michael P Pound, Nicholas J Watson
Plant Phenomics (Washington, D.C.)
|
April 27, 2026
Deep learning in plant phenotyping: the first ten years
Jordan Ubbens, Ian Stavness, Michael P Pound, et al.
Trends in Plant Science
|
August 21, 2012
What lies beneath: underlying assumptions in bioimage analysis
Tony P Pridmore, Andrew P French, Michael P Pound
Frontiers in Plant Science
|
September 28, 2020
Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning
Faraz Ahmad Khan, Ute Voß, Michael P Pound, et al.
Current Opinion in Biotechnology
|
July 23, 2018
Uncovering the hidden half of plants using new advances in root phenotyping
Jonathan A Atkinson, Michael P Pound, Malcolm J Bennett, et al.
Plant Physiology
|
October 22, 2014
Automated recovery of three-dimensional models of plant shoots from multiple color images
Michael P Pound, Andrew P French, Erik H Murchie, et al.
Annals of Botany
|
January 10, 2017
Image-based 3D canopy reconstruction to determine potential productivity in complex multi-species crop systems
Alexandra J Burgess, Renata Retkute, Michael P Pound, et al.
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|
May 15, 2020
Three Dimensional Root CT Segmentation using Multi-Resolution Encoder-Decoder Networks
Mohammadreza Soltaninejad, Craig J Sturrock, Marcus Griffiths, et al.
Plant Methods
|
March 14, 2017
AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping
Michael P Pound, Susan Fozard, Mercedes Torres Torres, et al.
The Plant Cell
|
April 5, 2012
CellSeT: novel software to extract and analyze structured networks of plant cells from confocal images
Michael P Pound, Andrew P French, Darren M Wells, et al.
Page
of 3