PlantCV is an imaging processing package specific for plants that is built upon open-source software platforms OpenCV 1, NumPy 2, and MatPlotLib 3. 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. Updates are available via twitter:

Please help us attend to your questions, bug reports, and requests by posting them on our GitHub issues page.

If you use PlantCV, please cite the initial published description. See also below.

Core PlantCV Development Team:
Noah Fahlgren, Director of Bioinformatics, Danforth Plant Science Center
Malia Gehan, Principal Investigator, Danforth Plant Science Center

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

  1. Bradski G (2000) The OpenCV library. Dr. Dobb's Journal 25(11):120-126.
  2. Oliphant TE (2007) Python for Scientific Computing. Computing in Science & Engineering, 9, 10-20.
  3. Hunter JD (2007) Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9, 90-95.

PlantCV Publications

  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. Enders TA, St. Dennis S, Oakland J, Callen ST, Gehan MA, Miller ND, Spalding EP, Springer NM, Hirsch CD. 2018. Classifying cold stress responses of inbred maize seedlings using RGB imaging. bioRxiv:432039. DOI: 10.1101/432039.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Gehan MA, Kellogg EA. 2017. High-throughput phenotyping. American Journal of Botany 104:505–508. DOI: 10.3732/ajb.1700044.
  11. 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.