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Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields

Plant methods, 2020-03, Vol.16 (1), p.29-29, Article 29

2020

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Automated Recovery of Three-Dimensional Models of Plant Shoots from Multiple Color Images

Plant physiology (Bethesda), 2014-12, Vol.166 (4), p.1688-1698

2014

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Root System Markup Language: Toward a Unified Root Architecture Description Language

Plant physiology (Bethesda), 2015-03, Vol.167 (3), p.617-627

2015

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Deep machine learning provides state-of-the-art performance in image-based plant phenotyping

Gigascience, 2017-10, Vol.6 (10), p.1-10

2017

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Colocalization of fluorescent markers in confocal microscope images of plant cells

Nature protocols, 2008-04, Vol.3 (4), p.619-628

2008

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Three Dimensional Root CT Segmentation Using Multi-Resolution Encoder-Decoder Networks

IEEE transactions on image processing, 2020-01, Vol.29, p.6667-6679

2020

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Root gravitropism is regulated by a transient lateral auxin gradient controlled by a tipping-point mechanism

Proceedings of the National Academy of Sciences - PNAS, 2012-03, Vol.109 (12), p.4668-4673

2012

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RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

Gigascience, 2019-11, Vol.8 (11)

2019

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The 4-Dimensional Plant: Effects of Wind-Induced Canopy Movement on Light Fluctuations and Photosynthesis

Frontiers in plant science, 2016-09, Vol.7, p.1392-1392

2016

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RooTrak: Automated Recovery of Three-Dimensional Plant Root Architecture in Soil from X-Ray Microcomputed Tomography Images Using Visual Tracking

Plant physiology (Bethesda), 2012-02, Vol.158 (2), p.561-569

2012

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RootNav: Navigating Images of Complex Root Architectures

Plant physiology (Bethesda), 2013-08, Vol.162 (4), p.1802-1814

2013

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Integration of hormonal signaling networks and mobile microRNAs is required for vascular patterning in Arabidopsis roots

Proceedings of the National Academy of Sciences - PNAS, 2014-01, Vol.111 (2), p.857-862

2014

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Rice auxin influx carrier OsAUX1 facilitates root hair elongation in response to low external phosphate

Nature communications, 2018-04, Vol.9 (1), p.1408-7, Article 1408

2018

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Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images

Frontiers in plant science, 2019-11, Vol.10, p.1516-1516

2019

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Systems Analysis of Auxin Transport in the Arabidopsis Root Apex

The Plant cell, 2014-03, Vol.26 (3), p.862-875

2014

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Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models

Scientific reports, 2021-12, Vol.11 (1), p.23279-23279, Article 23279

2021

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Extracting multiple interacting root systems using X‐ray microcomputed tomography

The Plant journal : for cell and molecular biology, 2015-12, Vol.84 (5), p.1034-1043

2015

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Low-Cost Automated Vectors and Modular Environmental Sensors for Plant Phenotyping

Sensors (Basel, Switzerland), 2020-06, Vol.20 (11), p.3319

2020

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High-Resolution Three-Dimensional Structural Data Quantify the Impact of Photoinhibition on Long-Term Carbon Gain in Wheat Canopies in the Field

Plant physiology (Bethesda), 2015-10, Vol.169 (2), p.1192-1204

2015

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Towards infield, live plant phenotyping using a reduced-parameter CNN

Machine vision and applications, 2020, Vol.31 (1-2), p.2-2, Article 2

2020

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Recovering Wind-Induced Plant Motion in Dense Field Environments via Deep Learning and Multiple Object Tracking

Plant physiology (Bethesda), 2019-09, Vol.181 (1), p.28-42

2019

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CellSeT: Novel Software to Extract and Analyze Structured Networks of Plant Cells from Confocal Images

The Plant cell, 2012-04, Vol.24 (4), p.1353-1361

2012

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High-Throughput Quantification of Root Growth Using a Novel Image-Analysis Tool

Plant physiology (Bethesda), 2009-08, Vol.150 (4), p.1784-1795

2009

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Quantification of root water uptake in soil using X‐ray computed tomography and image‐based modelling

Plant, cell and environment, 2018-01, Vol.41 (1), p.121-133

2018

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Mechanical modelling quantifies the functional importance of outer tissue layers during root elongation and bending

The New phytologist, 2014-06, Vol.202 (4), p.1212-1222

2014

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Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields

Automated Recovery of Three-Dimensional Models of Plant Shoots from Multiple Color Images

Root System Markup Language: Toward a Unified Root Architecture Description Language

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping

Colocalization of fluorescent markers in confocal microscope images of plant cells

Three Dimensional Root CT Segmentation Using Multi-Resolution Encoder-Decoder Networks

Root gravitropism is regulated by a transient lateral auxin gradient controlled by a tipping-point mechanism

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

The 4-Dimensional Plant: Effects of Wind-Induced Canopy Movement on Light Fluctuations and Photosynthesis

RooTrak: Automated Recovery of Three-Dimensional Plant Root Architecture in Soil from X-Ray Microcomputed Tomography Images Using Visual Tracking

RootNav: Navigating Images of Complex Root Architectures

Integration of hormonal signaling networks and mobile microRNAs is required for vascular patterning in Arabidopsis roots

Rice auxin influx carrier OsAUX1 facilitates root hair elongation in response to low external phosphate

Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images

Systems Analysis of Auxin Transport in the Arabidopsis Root Apex

Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models

Extracting multiple interacting root systems using X‐ray microcomputed tomography

Low-Cost Automated Vectors and Modular Environmental Sensors for Plant Phenotyping

High-Resolution Three-Dimensional Structural Data Quantify the Impact of Photoinhibition on Long-Term Carbon Gain in Wheat Canopies in the Field

Towards infield, live plant phenotyping using a reduced-parameter CNN

Recovering Wind-Induced Plant Motion in Dense Field Environments via Deep Learning and Multiple Object Tracking

CellSeT: Novel Software to Extract and Analyze Structured Networks of Plant Cells from Confocal Images

High-Throughput Quantification of Root Growth Using a Novel Image-Analysis Tool

Quantification of root water uptake in soil using X‐ray computed tomography and image‐based modelling

Mechanical modelling quantifies the functional importance of outer tissue layers during root elongation and bending

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