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. 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 v2.0: Image analysis software for high-throughput plant phenotyping. PeerJ Preprints. DOI: 10.7287/peerj.preprints.3225v1.
  2. 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.
  3. 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. 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.
  2. Tovar JC*, Hoyer JS*, Lin A, Tielking A, Callen ST, Castillo E, Miller M, Tessman M, Fahlgren N, Carrington JC, Nusinow DA, Gehan MA. 2017. Raspberry Pi powered imaging for plant phenotyping. bioRxiv:183822. DOI: 10.1101/183822.
  3. Veley KM, Berry JC, Fentress SJ, Schachtman DP, Baxter I, Bart RS. 2017. High-throughput profiling and analysis of plant responses over time to abiotic stress. bioRxiv:132787. DOI: 10.1101/132787.
  4. 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.
  5. 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.
  6. Gehan MA, Kellogg EA. 2017. High-throughput phenotyping. American Journal of Botany 104:505–508. DOI: 10.3732/ajb.1700044.
  7. 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.