PlantCV v4: Image analysis software for high-throughput plant phenotyping

Haley Schuhl; Keely E. Brown; Hudanyun Sheng; Parag K. Bhatt; Jorge Gutierrez; Dominik Schneider; Anna L. Casto; Lucia Acosta-Gamboa; Joe G. Ballenger; Fabio Barbero; Jackson Braley; Autumn M. Brown; Leonardo Chavez; Shannon Cunningham; Malinda Dilhara; Adam M. Dimech; Joseph G. Duenwald; Annika Fischer; Jared M. Gordon; Chloe Hendrikse; Gabriela L. Hernandez; John G. Hodge; Martina Huber; Brandon M. Hurr; Sanaz Jarolmasjed; Karina Medina Jimenez; Samuel Kenney; Grant Konkel; Alexander Kutschera; Sunita Lama; Matthew Lohbihler; Argelia Lorence; Collin Luebbert; Nathaniel Ly; Heather K. Manching; Annarita Marrano; Susan Meerdink; Nicholas M. Miklave; Pavan Mudrageda; Katherine M. Murphy; J. David Peery; Ronald Pierik; Seth Polydore; Caleb Robey; Tess Rogers; Tyler J. Schultz; Eliza Seigel; Dhiraj Srivastava; Stephan Summerer; Josh Sumner; Chong Teng; Adriane E. Thompson; Jose C. Tovar; Tim van Daalen; Mark Watson; John J. Wheeler; Mark C. Wilson; Kaitlyn R. Ying; Alina Zare; Yutai Zhou; Malia A. Gehan; Noah Fahlgren

doi:10.1002/ppj2.70065

PlantCV v4: Image analysis software for high-throughput plant phenotyping

Haley Schuhl
, Keely E. Brown
, Hudanyun Sheng
, Parag K. Bhatt
, Jorge Gutierrez
, Dominik Schneider
, Anna L. Casto
, Lucia Acosta-Gamboa
, Joe G. Ballenger
, Fabio Barbero
, Jackson Braley
, Autumn M. Brown
, Leonardo Chavez
, Shannon Cunningham
, Malinda Dilhara
, Adam M. Dimech
, Joseph G. Duenwald
, Annika Fischer
, Jared M. Gordon
, Chloe Hendrikse

Gabriela L. Hernandez, John G. Hodge, Martina Huber, Brandon M. Hurr, Sanaz Jarolmasjed, Karina Medina Jimenez, Samuel Kenney, Grant Konkel, Alexander Kutschera, Sunita Lama, Matthew Lohbihler, Argelia Lorence, Collin Luebbert, Nathaniel Ly, Heather K. Manching, Annarita Marrano, Susan Meerdink, Nicholas M. Miklave, Pavan Mudrageda, Katherine M. Murphy, J. David Peery, Ronald Pierik, Seth Polydore, Caleb Robey, Tess Rogers, Tyler J. Schultz, Eliza Seigel, Dhiraj Srivastava, Stephan Summerer, Josh Sumner, Chong Teng, Adriane E. Thompson, Jose C. Tovar, Tim van Daalen, Mark Watson, John J. Wheeler, Mark C. Wilson, Kaitlyn R. Ying, Alina Zare, Yutai Zhou, Malia A. Gehan^*, Noah Fahlgren^*

^*Corresponding author for this work

Sub Experimental and Computational Plant Development

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.

Original language	English
Article number	e70065
Journal	Plant Phenome Journal
Volume	9
Issue number	1
DOIs	https://doi.org/10.1002/ppj2.70065
Publication status	Published - Dec 2026

Bibliographical note

Publisher Copyright:
© 2026 The Author(s). The Plant Phenome Journal published by Wiley Periodicals LLC on behalf of American Society of Agronomy and Crop Science Society of America.

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10.1002/ppj2.70065Licence: CC BY

The Plant Phenome Journal - 2026 - Schuhl - PlantCV v4 Image analysis software for high‐throughput plant phenotypingFinal published version, 5.2 MBLicence: CC BY

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Schuhl, H., Brown, K. E., Sheng, H., Bhatt, P. K., Gutierrez, J., Schneider, D., Casto, A. L., Acosta-Gamboa, L., Ballenger, J. G., Barbero, F., Braley, J., Brown, A. M., Chavez, L., Cunningham, S., Dilhara, M., Dimech, A. M., Duenwald, J. G., Fischer, A., Gordon, J. M., ... Fahlgren, N. (2026). PlantCV v4: Image analysis software for high-throughput plant phenotyping. Plant Phenome Journal, 9(1), Article e70065. https://doi.org/10.1002/ppj2.70065

@article{ac2db01491aa4214bb45fa42eb8bfae6,

title = "PlantCV v4: Image analysis software for high-throughput plant phenotyping",

abstract = "PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.",

author = "Haley Schuhl and Brown, \{Keely E.\} and Hudanyun Sheng and Bhatt, \{Parag K.\} and Jorge Gutierrez and Dominik Schneider and Casto, \{Anna L.\} and Lucia Acosta-Gamboa and Ballenger, \{Joe G.\} and Fabio Barbero and Jackson Braley and Brown, \{Autumn M.\} and Leonardo Chavez and Shannon Cunningham and Malinda Dilhara and Dimech, \{Adam M.\} and Duenwald, \{Joseph G.\} and Annika Fischer and Gordon, \{Jared M.\} and Chloe Hendrikse and Hernandez, \{Gabriela L.\} and Hodge, \{John G.\} and Martina Huber and Hurr, \{Brandon M.\} and Sanaz Jarolmasjed and Jimenez, \{Karina Medina\} and Samuel Kenney and Grant Konkel and Alexander Kutschera and Sunita Lama and Matthew Lohbihler and Argelia Lorence and Collin Luebbert and Nathaniel Ly and Manching, \{Heather K.\} and Annarita Marrano and Susan Meerdink and Miklave, \{Nicholas M.\} and Pavan Mudrageda and Murphy, \{Katherine M.\} and Peery, \{J. David\} and Ronald Pierik and Seth Polydore and Caleb Robey and Tess Rogers and Schultz, \{Tyler J.\} and Eliza Seigel and Dhiraj Srivastava and Stephan Summerer and Josh Sumner and Chong Teng and Thompson, \{Adriane E.\} and Tovar, \{Jose C.\} and \{van Daalen\}, Tim and Mark Watson and Wheeler, \{John J.\} and Wilson, \{Mark C.\} and Ying, \{Kaitlyn R.\} and Alina Zare and Yutai Zhou and Gehan, \{Malia A.\} and Noah Fahlgren",

note = "Publisher Copyright: {\textcopyright} 2026 The Author(s). The Plant Phenome Journal published by Wiley Periodicals LLC on behalf of American Society of Agronomy and Crop Science Society of America.",

year = "2026",

month = dec,

doi = "10.1002/ppj2.70065",

language = "English",

volume = "9",

journal = "Plant Phenome Journal",

issn = "2578-2703",

publisher = "John Wiley and Sons Ltd",

number = "1",

}

Schuhl, H, Brown, KE, Sheng, H, Bhatt, PK, Gutierrez, J, Schneider, D, Casto, AL, Acosta-Gamboa, L, Ballenger, JG, Barbero, F, Braley, J, Brown, AM, Chavez, L, Cunningham, S, Dilhara, M, Dimech, AM, Duenwald, JG, Fischer, A, Gordon, JM, Hendrikse, C, Hernandez, GL, Hodge, JG, Huber, M, Hurr, BM, Jarolmasjed, S, Jimenez, KM, Kenney, S, Konkel, G, Kutschera, A, Lama, S, Lohbihler, M, Lorence, A, Luebbert, C, Ly, N, Manching, HK, Marrano, A, Meerdink, S, Miklave, NM, Mudrageda, P, Murphy, KM, Peery, JD, Pierik, R, Polydore, S, Robey, C, Rogers, T, Schultz, TJ, Seigel, E, Srivastava, D, Summerer, S, Sumner, J, Teng, C, Thompson, AE, Tovar, JC, van Daalen, T, Watson, M, Wheeler, JJ, Wilson, MC, Ying, KR, Zare, A, Zhou, Y, Gehan, MA & Fahlgren, N 2026, 'PlantCV v4: Image analysis software for high-throughput plant phenotyping', Plant Phenome Journal, vol. 9, no. 1, e70065. https://doi.org/10.1002/ppj2.70065

TY - JOUR

T1 - PlantCV v4

T2 - Image analysis software for high-throughput plant phenotyping

AU - Schuhl, Haley

AU - Brown, Keely E.

AU - Sheng, Hudanyun

AU - Bhatt, Parag K.

AU - Gutierrez, Jorge

AU - Schneider, Dominik

AU - Casto, Anna L.

AU - Acosta-Gamboa, Lucia

AU - Ballenger, Joe G.

AU - Barbero, Fabio

AU - Braley, Jackson

AU - Brown, Autumn M.

AU - Chavez, Leonardo

AU - Cunningham, Shannon

AU - Dilhara, Malinda

AU - Dimech, Adam M.

AU - Duenwald, Joseph G.

AU - Fischer, Annika

AU - Gordon, Jared M.

AU - Hendrikse, Chloe

AU - Hernandez, Gabriela L.

AU - Hodge, John G.

AU - Huber, Martina

AU - Hurr, Brandon M.

AU - Jarolmasjed, Sanaz

AU - Jimenez, Karina Medina

AU - Kenney, Samuel

AU - Konkel, Grant

AU - Kutschera, Alexander

AU - Lama, Sunita

AU - Lohbihler, Matthew

AU - Lorence, Argelia

AU - Luebbert, Collin

AU - Ly, Nathaniel

AU - Manching, Heather K.

AU - Marrano, Annarita

AU - Meerdink, Susan

AU - Miklave, Nicholas M.

AU - Mudrageda, Pavan

AU - Murphy, Katherine M.

AU - Peery, J. David

AU - Pierik, Ronald

AU - Polydore, Seth

AU - Robey, Caleb

AU - Rogers, Tess

AU - Schultz, Tyler J.

AU - Seigel, Eliza

AU - Srivastava, Dhiraj

AU - Summerer, Stephan

AU - Sumner, Josh

AU - Teng, Chong

AU - Thompson, Adriane E.

AU - Tovar, Jose C.

AU - van Daalen, Tim

AU - Watson, Mark

AU - Wheeler, John J.

AU - Wilson, Mark C.

AU - Ying, Kaitlyn R.

AU - Zare, Alina

AU - Zhou, Yutai

AU - Gehan, Malia A.

AU - Fahlgren, Noah

PY - 2026/12

Y1 - 2026/12

N2 - PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.

AB - PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.

UR - https://www.scopus.com/pages/publications/105030260122

U2 - 10.1002/ppj2.70065

DO - 10.1002/ppj2.70065

M3 - Article

AN - SCOPUS:105030260122

SN - 2578-2703

VL - 9

JO - Plant Phenome Journal

JF - Plant Phenome Journal

IS - 1

M1 - e70065

ER -

PlantCV v4: Image analysis software for high-throughput plant phenotyping

Abstract

Bibliographical note

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