PlantCV
Plant phenotyping using computer vision

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: , or follow us on GitHub.

If you have questions, bug reports, suggestions for new features, etc. please post to our GitHub issues page.

We encourage community contribution. See our guide for contributors, our code of conduct, and current list of contributors .

Presentations

PlantCV Publications

If you use PlantCV, please cite the appropriate PlantCV publication below.

  1. 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.
  2. 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.
  3. 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.
  4. 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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. Tovar JC, Quillatupa C, Callen ST, Elizabeth Castillo S, Pearson P, Shamin A, Schuhl H, Fahlgren N, Gehan MA. 2019. Heating quinoa shoots results in yield loss by inhibiting fruit production and delaying maturity. bioRxiv:727545. DOI: 10.1101/727545.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. Gehan MA, Kellogg EA. 2017. High-throughput phenotyping. American Journal of Botany 104:505–508. DOI: 10.3732/ajb.1700044.
  18. 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.