PlantCV is an open-source image analysis software package targeted for plant phenotyping. The PlantCV project
is managed by Malia Gehan and
Noah Fahlgren and the effort of many generous contributors, collaborators, and users.
Vision
Phenotyping tools will be modular and reusable such that they can be combined and recombined
with ease to build flexible workflows to quickly extract biologically-relevant data from images and
sensors. These tools will be usable by both bioinformaticians/data scientists and biologists.
Mission
Provide a common interface for a collection of image analysis techniques that are
integrated from a variety of source packages and algorithms.
Utilize a modular architecture that enables flexibility in the design of analysis workflows
and rapid assimilation and integration of new methods.
Develop a network of users, collaborators, and contributors by developing openly in
real-time in the cloud using an open-source framework to rapidly disseminate new methods.
Provide a simplified interface for users to utilize the underlying tools and build custom
analysis workflows without significant experience with programming.
Utilize PlantCV as a tool for training researchers and students in image and data analysis
techniques.
Values
The PlantCV project values open communication and collaboration among stakeholders from diverse
backgrounds and areas of expertise. Through the PlantCV project we seek to highlight the valuable
ideas and contributions of members of the community.
About
PlantCV is an open-source image analysis software package targeted for plant phenotyping. The PlantCV project
was started at the Donald Danforth Plant Science Center in 2014, and is under active
development—new functionality and tutorials are added regularly. Keep up to date on our progress
via twitter:
Follow @plantcv
, or follow us on GitHub.
If you have questions, bug reports, suggestions for new features, etc. please post to our
GitHub issues page.
If you use PlantCV, please cite the appropriate PlantCV publication below.
Schuhl H, David Peery J, Gutierrez J, Gehan MA, Fahlgren N. 2022. Simplifying PlantCV workflows with
multiple objects. Authorea Preprints. DOI:
10.22541/au.166758437.76129704/v1.
Casto A, Schuhl H, Schneider D, Wheeler J, Gehan M, Fahlgren N. 2021. Analyzing chlorophyll
fluorescence images in PlantCV. Earth and Space Science Open Archive. DOI:
10.1002/essoar.10508322.2.
Gutierrez Ortega JA, Castillo SE, Gehan M, Fahlgren N. 2021. Segmentation of overlapping plants
in multi-plant image time series. Earth and Space Science Open Archive. DOI:
10.1002/essoar.10508337.2.
Hodge JG, Li Q, Doust A. 2021. De novo homology assessment from landmark data: A workflow to
identify and track segmented structures in plant time series images.
bioRxiv:2021.02.21.432162. DOI:
10.1101/2021.02.21.432162.
Berry JC, Fahlgren N, Pokorny AA, Bart RS, Veley KM. 2018. An automated, high-throughput
method for standardizing image color profiles to improve image-based plant phenotyping.
PeerJ 6:e5727. DOI: 10.7717/peerj.5727.
Gehan MA*, Fahlgren N*, Abbasi A, Berry JC, Callen ST, Chavez L, Doust AN, Feldman MJ, Gilbert
KB, Hodge JG, Hoyer JS, Lin A, Liu S, Lizárraga C, Lorence A, Miller M, Platon E, Tessman M,
Sax T. 2017. PlantCV v2: Image analysis software for high-throughput plant phenotyping.
PeerJ 5:e4088. DOI: 10.7717/peerj.4088.
Abbasi A, Fahlgren N. 2016. Naive Bayes pixel-level plant segmentation. In: 2016 IEEE Western
New York Image and Signal Processing Workshop (WNYISPW). 1–4. DOI:
10.1109/WNYIPW.2016.7904790.
Fahlgren N*, Feldman M*, Gehan MA*, Wilson MS, Shyu C, Bryant DW, Hill ST, McEntee CJ,
Warnasooriya SN, Kumar I, Ficor T, Turnipseed S, Gilbert KB, Brutnell TP, Carrington JC,
Mockler TC, Baxter I. 2015. A versatile phenotyping system and analytics platform reveals
diverse temporal responses to water availability in Setaria. Molecular Plant
8:1520–1535. DOI:
10.1016/j.molp.2015.06.005.
Publications Using PlantCV
Pierz LD, Heslinga DR, Buell CR, Haus MJ. 2023. An image-based technique for automated root disease
severity assessment using PlantCV. Applications in Plant Sciences 11:e11507. DOI:
10.1002/aps3.11507.
Griffiths M, Liu AE, Gunn SL, Mutan NM, Morales EY, Topp CN. 2023. A temporal atlas and
response to nitrate availability of 3D root system architecture in diverse pennycress
(Thlaspi arvense L.) accessions. bioRxiv:2023.01.14.524046. DOI:
10.1101/2023.01.14.524046.
Gao M. 2022. Machine Learning Approaches to High Throughput Phenotyping. In: Accelerating Science and
Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling
and Simulation. Springer Nature Switzerland, 303–316. DOI:
10.1007/978-3-031-23606-8_19.
Chairat S, Chaichulee S, Dissaneewate T, Wangkulangkul P, Kongpanichakul L. 2023. AI-Assisted
Assessment of Wound Tissue with Automatic Color and Measurement Calibration on Images Taken with a
Smartphone. Healthcare (Basel, Switzerland) 11. DOI:
10.3390/healthcare11020273.
Knapp A, Stefani J, Katz E, Bloom AJ. 2022. Novel method for the quantification of rosette area from
images of Arabidopsis seedlings grown on agar plates. Applications in Plant Sciences 10:e11504.
DOI: 10.1002/aps3.11504.
Blaschek L, Murozuka E, Serk H, Ménard D, Pesquet E. 2022. Different combinations of laccase paralogs
nonredundantly control the amount and composition of lignin in specific cell types and cell wall
layers in Arabidopsis. The Plant Cell. DOI:
10.1093/plcell/koac344.
Ghiasi Noei F, Imami M, Didaran F, Ghanbari MA, Zamani E, Ebrahimi A, Aliniaeifard S, Farzaneh M,
Javan-Nikkhah M, Feechan A, Mirzadi Gohari A. 2022. Stb6 mediates stomatal immunity, photosynthetic
functionality, and the antioxidant system during the Zymoseptoria tritici-wheat interaction.
Frontiers in Plant Science 13:1004691. DOI:
10.3389/fpls.2022.1004691.
Yu L, M Julkowska M. 2022. RasberryPi-computer based phenotyping for side view image process v2.
protocols.io DOI:
10.17504/protocols.io.eq2lynp7pvx9/v2.
Wang W, Talide L, Viljamaa S, Niittylä T. 2022. Aspen growth is not limited by starch reserves.
Current Biology: CB.
DOI: 10.1016/j.cub.2022.06.056.
Beyene G, Chauhan RD, Villmer J, Husic N, Wang N, Gebre E, Girma D, Chanyalew S, Assefa K, Tabor G,
Gehan M, McGrone M, Yang M, Lenderts B, Schwartz C, Gao H, Gordon-Kamm W, Taylor NJ, MacKenzie DJ.
2022. CRISPR/Cas9-mediated tetra-allelic mutation of the “Green Revolution” SEMIDWARF-1 (SD-1) gene
confers lodging resistance in Tef (Eragrostis tef). Plant Biotechnology Journal.
DOI: 10.1111/pbi.13842.
Kinose R, Utsumi Y, Iwamura M, Kise K. 2022. Tiller estimation method using deep neural networks.
DOI: 10.21203/rs.3.rs-1552723/v1.
Castillo SE, Tovar JC, Shamin A, Gutirerrez J, Pearson P, Gehan MA. 2022. A protocol for Chenopodium
quinoa pollen germination. Plant Methods 18:65. DOI:
10.1186/s13007-022-00900-3.
Marrano A, Moyers BT. 2022. Scanning the rice Global MAGIC population for dynamic genetic control of
seed traits under vegetative drought. The Plant Phenome Journal 5. DOI:
10.1002/ppj2.20033.
Tanaka K, Kato Y, Mikawa M, Fujisawa M. 2022. Dynamic grass color scale display technique based on
grass length for green landscape-friendly animation display. arXiv:2203.08496 [cs.GR].
http://arxiv-export3.library.cornell.edu/abs/2203.08496.
Arunachalam A, Andreasson H. 2022. MSI-RPi: Affordable, portable, and modular multispectral imaging
prototype suited to operate in UV, visible and mid-infrared regions. Journal of Mobile
Multimedia:723–742. DOI:
10.13052/jmm1550-4646.18312.
Scandola S, Mehta D, Li Q, Rodriguez Gallo MC, Castillo B, Uhrig RG. 2022. Multi-omic analysis shows
REVEILLE clock genes are involved in carbohydrate metabolism and proteasome function.
Plant Physiology. DOI: 10.1093/plphys/kiac269.
Chang L, Li D, Hameed MK, Yin Y, Huang D, Niu Q. 2021. Using a hybrid neural network model DCNN–LSTM
for image-based nitrogen nutrition diagnosis in muskmelon. Horticulturae 7:489.
DOI: 10.3390/horticulturae7110489.
Afzali S, Mosharafian S, van Iersel MW, Mohammadpour Velni J. 2021. Development and implementation of
an IoT-enabled optimal and predictive lighting control strategy in greenhouses. Plants 10:2652.
DOI: 10.3390/plants10122652.
Polydore S, Fahlgren N. 2021. Phenotypic analysis of a European Camelina sativa
diversity panel. Earth and Space Science Open Archive. DOI:
10.1002/essoar.10508336.2.
Teng C, Fahlgren N, Meyers BC. 2021. Tasselyzer, a machine learning method to quantify
anther extrusion in maize, based on PlantCV. bioRxiv:2021.09.27.461799.
DOI: 10.1101/2021.09.27.461799.
Roquis D, Robertson M, Yu L, Thieme M, Julkowska M, Bucher E. 2021. Genomic impact of
stress-induced transposable element mobility in Arabidopsis. Nucleic Acids Research.
DOI: 10.1093/nar/gkab828.
Cox KL, Manchego J, Meyers BC, Czymmek KJ, Harkess A. 2021. Automated imaging of duckweed
growth and development. bioRxiv:2021.07.21.453240. DOI:
10.1101/2021.07.21.453240.
Huber M, Julkowska MM, Snoek BL, van Veen H, Toulotte J, Kumar V, Kajala K, Sasidharan R, Pierik R.
2021. Towards increased shading potential: a combined phenotypic and genetic analysis of rice shoot
architecture. bioRxiv:2021.05.25.445664. DOI:
10.1101/2021.05.25.445664.
Renaud JB, DesRochers N, Hoogstra S, Garnham CP, Sumarah MW. 2021. Structure activity
relationship for fumonisin phytotoxicity. Chemical Research in Toxicology 34:1604–1611.
DOI: 10.1021/acs.chemrestox.1c00057.
Li Q, Liu N, Liu Q, Zheng X, Lu L, Gao W, Liu Y, Liu Y, Zhang S, Wang Q, Pan J, Chen C, Mi Y,
Yang M, Cheng X, Ren G, Yuan Y-W, Zhang X. 2021. DEAD-box helicases modulate dicing body
formation in Arabidopsis. Science Advances 7. DOI:
10.1126/sciadv.abc6266.
van de Koot WQM, van Vliet LJJ, Chen W, Doonan JH, Nibau C. 2021. Development of an image
analysis pipeline to estimate sphagnum colony density in the field. Plants 10. DOI:
10.3390/plants10050840.
Badhan S, Desai K, Dsilva M, Sonkusare R, Weakey S. 2021. Real-time weed detection using
machine learning and stereo-vision. In: 2021 6th International Conference for Convergence
in Technology (I2CT). 1–5. DOI: 10.1109/I2CT51068.2021.9417989.
Palermo F, Oh C, Althoefer K, Poslad S, Farkhatdinov I. 2021. Investigation of images of cracks
via graph theory for developing an optimal exploration algorithm for a robotic manipulator. In:
2021 International Conference on Robotics and Automation. IEEE.
Kienbaum L, Abondano MC, Blas R, Schmid K. 2021. DeepCob: Precise and high-throughput analysis
of maize cob geometry using deep learning with an application in genebank phenomics.
bioRxiv:2021.03.16.435660. DOI:
10.1101/2021.03.16.435660.
Al-Lami MK, Nguyen D, Oustriere N, Burken JG. 2021. High throughput screening of native species
for tailings eco-restoration using novel computer visualization for plant phenotyping. The
Science of the Total Environment 780:146490. DOI:
10.1016/j.scitotenv.2021.146490.
Kim J, Go S, Noh K, Park S, Lee S. 2021. Fully leveraging deep learning methods for
constructing retinal fundus photomontages. Applied Sciences 11:1754. DOI:
10.3390/app11041754.
Zhang X, Wang D, Elberse J, Qi L, Shi W, Peng Y-L, Schuurink RC, Van den Ackerveken G, Liu J.
2021. Structure-guided analysis of the Arabidopsis JASMONATE-INDUCED OXYGENASE (JOX) 2 reveals
key residues of plant JOX recognizing jasmonic acid substrate. Molecular Plant. DOI:
10.1016/j.molp.2021.01.017.
Fernández Nevyl S, Battaglia ME. 2021. Developmental plasticity in Arabidopsis thaliana
under combined cold and water deficit stresses during flowering stage. Planta 253:50.
DOI: 10.1007/s00425-021-03575-7.
Paradis OP, Jessurun NT, Tehranipoor M, Asadizanjani N. 2020. Color normalization for robust
automatic bill of materials generation and visual inspection of PCBs. In: ISTFA 2020: Papers
Accepted for the Planned 46th International Symposium for Testing and Failure Analysis.
ASM International,. DOI:
10.31399/asm.cp.istfa2020p0172.
Nurminen A, Malhi A. 2020. Green thumb engineering: Artificial intelligence for managing IoT
enabled houseplants. In: 2020 IEEE Global Conference on Artificial Intelligence and Internet
of Things (GCAIoT). 01–07. DOI:
10.1109/GCAIoT51063.2020.9345850.
White AE, Dikow RB, Baugh M, Jenkins A, Frandsen PB. 2020. Generating segmentation masks of
herbarium specimens and a data set for training segmentation models using deep learning.
Applications in Plant Sciences 8:e11352. DOI:
10.1002/aps3.11352.
Kumar D, Kushwaha S, Delvento C, Liatukas Ž, Vivekanand V, Svensson JT, Henriksson T,
Brazauskas G, Chawade A. 2020. Affordable phenotyping of winter wheat under field and
controlled conditions for drought tolerance. Agronomy 10:882. DOI:
10.3390/agronomy10060882.
Teng C, Zhang H, Hammond R, Huang K, Meyers BC, Walbot V. 2020. Dicer-like 5 deficiency confers
temperature-sensitive male sterility in maize. Nature Communications 11:2912. DOI:
10.1038/s41467-020-16634-6.
Acosta-Gamboa LM, Suxing L, Jarrod W C, Zachary C C, Raquel T, Walter P S, Jessica P Y-C,
Lorence A. 2020. Characterization of the response to abiotic stresses of high ascorbate
Arabidopsis lines using phenomic approaches. Plant Physiology and Biochemistry
151:500–515. DOI:
10.1016/j.plaphy.2020.03.038.
Tovar JC, Quillatupa C, Callen ST, Castillo SE, Pearson P, Shamin A, Schuhl H, Fahlgren N,
Gehan MA. 2020. Heating quinoa shoots results in yield loss by inhibiting fruit production and
delaying maturity. The Plant Journal: For Cell and Molecular Biology 102:1058–1073. DOI:
10.1111/tpj.14699.
Schneider D, Lopez LS, Li M, Crawford JD, Kirchhoff H, Kunz H-H. 2019. Fluctuating light
experiments and semi-automated plant phenotyping enabled by self-built growth racks and
simple upgrades to the IMAGING-PAM. Plant Methods 15:156. DOI:
10.1186/s13007-019-0546-1.
Shakoor N, Agnew E, Ziegler G, Lee S, Lizarraga C, Fahlgren N, Baxter I, Mockler TC. 2019.
Genomewide association study reveals transient loci underlying the genetic architecture of
biomass accumulation under cold stress in Sorghum. bioRxiv:760025. DOI:
10.1101/760025.
Zheng X, Fahlgren N, Abbasi A, Berry JC, Carrington JC. 2019. Antiviral ARGONAUTEs against
Turnip Crinkle Virus revealed by image-based trait analysis. Plant Physiology
180:1418–1435. DOI: 10.1104/pp.19.00121.
Enders TA, St. Dennis S, Oakland J, Callen ST, Gehan MA, Miller ND, Spalding EP, Springer NM,
Hirsch CD. 2019. Classifying cold-stress responses of inbred maize seedlings using RGB imaging.
Plant Direct 3:e00104. DOI:
10.1002/pld3.104.
Feldman MJ, Ellsworth PZ, Fahlgren N, Gehan MA, Cousins AB, Baxter I. 2018. Components of
water use efficiency have unique genetic signatures in the model C4 grass Setaria.
Plant Physiology 178:699–715. DOI:
10.1104/pp.18.00146.
Armoniené R, Odilbekov F, Vivekanand V, Chawade A. 2018. Affordable imaging lab for noninvasive
analysis of biomass and early vigour in cereal crops. BioMed Research International 2018.
DOI: 10.1155/2018/5713158.
Tovar JC*, Hoyer JS*, Lin A, Tielking A, Callen ST, Castillo SE, Miller M, Tessman M, Fahlgren
N, Carrington JC, Nusinow DA, Gehan MA. 2018. Raspberry Pi-powered imaging for plant
phenotyping. Applications in Plant Sciences 6:e1031. DOI:
10.1002/aps3.1031.
Liang Z, Pandey P, Stoerger V, Xu Y, Qiu Y, Ge Y, Schnable JC. 2018. Conventional and
hyperspectral time-series imaging of maize lines widely used in field trials. GigaScience
7:1–11. DOI:
10.1093/gigascience/gix117.
Veley KM, Berry JC, Fentress SJ, Schachtman DP, Baxter I, Bart R. 2017. High-throughput
profiling and analysis of plant responses over time to abiotic stress. Plant Direct
1:e00023. DOI: 10.1002/pld3.23.
Liu S, Acosta-Gamboa LM, Huang X, Lorence A. 2017. Novel low cost 3D surface model
reconstruction system for plant phenotyping. Journal of Imaging 3:39. DOI:
10.3390/jimaging3030039.
Ubbens JR, Stavness I. 2017. Deep Plant Phenomics: A deep learning platform for complex plant
phenotyping tasks. Frontiers in Plant Science 8:1190. DOI:
10.3389/fpls.2017.01190.
Feldman MJ, Paul RE, Banan D, Barrett JF, Sebastian J, Yee M-C, Jiang H, Lipka AE, Brutnell TP,
Dinneny JR, Leakey ADB, Baxter I. 2017. Time dependent genetic analysis links field and
controlled environment phenotypes in the model C4 grass Setaria. PLoS Genetics
13:e1006841. DOI:
10.1371/journal.pgen.1006841.
Gehan MA, Kellogg EA. 2017. High-throughput phenotyping. American Journal of Botany
104:505–508. DOI: 10.3732/ajb.1700044.
Agnew E*, Bray A*, Floro E*, Ellis N, Gierer J, Lizárraga C, O’Brien D, Wiechert M, Mockler TC,
Shakoor N, Topp CN. 2016. Whole-plant manual and image-based phenotyping in controlled
environments. In: Current Protocols in Plant Biology. John Wiley & Sons, Inc. DOI:
10.1002/cppb.20044.