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/Workshops

PlantCV Publications

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

  1. 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.
  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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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. 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.
  2. 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.
  3. 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.
  4. Kinose R, Utsumi Y, Iwamura M, Kise K. 2022. Tiller estimation method using deep neural networks. DOI: 10.21203/rs.3.rs-1552723/v1.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. 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.
  41. 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.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. Gehan MA, Kellogg EA. 2017. High-throughput phenotyping. American Journal of Botany 104:505–508. DOI: 10.3732/ajb.1700044.
  47. 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.

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