PlantCV Software Development Publications

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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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 that use PlantCV software

This publication list includes groups across the world who have used PlantCV. It does not include review or opinion articles, or publications that reference but do not use PlantCV.

  1. Todd OE, Hanson LE, Dorn KM. 2024. A standard area diagram for Fusarium yellows rating in sugar beet (Beta vulgaris). Plant Pathology. DOI: 10.1111/ppa.14028.

  2. Li Q, Wang L, Rodriguez Gallo MC, Mehta D, Scandola S, Talasila M, Uhrig RG. 2024. B4 Raf-like MAPKKK RAF24 regulates Arabidopsis thaliana flowering time through HISTONE MONO-UBIQUITINATION 2. bioRxiv:2024.06.12.598286. DOI: 10.1101/2024.06.12.598286.

  3. Rieder P, Furat O, Usseglio-Viretta FLE, Allen J, Weddle PJ, Finegan DP, Smith K, Schmidt V. 2024. Stochastic 3D reconstruction of cracked polycrystalline NMC particles using 2D SEM data. arXiv [cond-mat.mtrl-sci]. DOI: 10.48550/arXiv.2410.12020.

  4. Sundharbaabu PR, Chang J, Kim Y, Shim Y, Lee B, Noh C, Heo S, Lee SS, Shim S-H, Lim K-I, Jo K, Lee JH. 2024. Artificial intelligence-enhanced analysis of genomic DNA visualized with nanoparticle-tagged peptides under electron microscopy. Small:e2405065. DOI: 10.1002/smll.202405065.

  5. Bengoa Luoni SA, Garassino F, Aarts MGM. 2024. A high-throughput approach for photosynthesis studies in a Brassicaceae panel. Methods in Molecular Biology (Clifton, N.J.) 2787:39–53. DOI: 10.1007/978-1-0716-3778-4_2.

  6. Karasov TL, Neumann M, Leventhal L, Symeonidi E, Shirsekar G, Hawks A, Monroe G, Pathodopsis Team, Exposito-Alonso M, Bergelson J, Weigel D, Schwab R. 2024. Continental-scale associations of Arabidopsis thaliana phyllosphere members with host genotype and drought. Nature Microbiology:1–11. DOI: 10.1038/s41564-024-01773-z.

  7. Patel AK, Bertolini E, Sagan V, Alifu H, Braud M, Shrestha N, Gul C, Eveland AL. 2024. Genomic prediction of maize tassel traits through LiDAR point cloud segmentation and machine learning phenotyping. AgriXiv. DOI: 10.31220/agrirxiv.2024.00269.

  8. Robinson KA, Augoustides V, Madenyika T, Sartor RC. 2024. ALPHA: A high throughput system for quantifying growth in aquatic plants. bioRxiv:2024.07.30.605820. DOI: 10.1101/2024.07.30.605820.

  9. Barbut FR, Cavel E, Donev EN, Gaboreanu I, Urbancsok J, Pandey G, Demailly H, Jiao D, Yassin Z, Derba-Maceluch M, Master ER, Scheepers G, Gutierrez L, Mellerowicz EJ. 2024. Integrity of xylan backbone affects plant responses to drought. Frontiers in Plant Science 15:1422701. DOI: 10.3389/fpls.2024.1422701.

  10. Mehta D, Scandola S, Kennedy C, Lummer C, Gallo MCR, Grubb LE, Tan M, Scarpella E, Uhrig RG. 2024. Twilight length alters growth and flowering time in Arabidopsis via LHY/CCA1. Science Advances 10:eadl3199. DOI: 10.1126/sciadv.adl3199.

  11. Spanò R, Petrozza A, Summerer S, Fortunato S, de Pinto MC, Cellini F, Mascia T. 2024. Overview of transcriptome changes and phenomic profile of sanitized artichoke vis-à-vis non-sanitized plants. Plant Biology . DOI: 10.1111/plb.13675.

  12. Einspanier S, Tominello-Ramirez C, Hasler M, Barbacci A, Raffaele S, Stam R. High-resolution disease phenotyping reveals distinct resistance mechanisms of wild tomato crop wild relatives against Sclerotinia sclerotiorum. Plant Phenomics 0. DOI: 10.34133/plantphenomics.0214.

  13. Dimech AM, Kaur S, Breen EJ. 2024. Mapping and quantifying unique branching structures in lentil (Lens culinaris Medik.). Plant Methods 20:95. DOI: 10.1186/s13007-024-01223-1.

  14. Hoffmann M. 2024. Accessible Real-Time Weed Detection and Segmentation with Deep Learning. In: Proceedings of ARC-Konferenz. University of Applied Sciences, Munich, Germany. https://www.researchgate.net/publication/381266962_Accessible_Real-Time_Weed_Detection_and_Segmentation_with_Deep_Learning.

  15. Banerjee S, Reynolds J, Taggart M, Daniele MA, Bozkurt A, Lobaton E. 2024. Quantifying Visual Differences in Drought Stressed Maize through Reflectance and Data-Driven Analysis. Preprints. DOI: 10.20944/preprints202404.1949.v1.

  16. Zohranyan V, Navasardyan V, Navasardyan H, Borggrefe J, Navasardyan S. 2024. Dr-SAM: An End-to-End Framework for Vascular Segmentation, Diameter Estimation, and Anomaly Detection on Angiography Images. arXiv [cs.CV]. DOI: 10.48550/arXiv.2404.17029.

  17. Scandola S, Mehta D, Castillo B, Boyce N, Uhrig RG. 2023. Systems-level proteomics and metabolomics reveals the diel molecular landscape of diverse kale cultivars. Frontiers in Plant Science 14:1170448. DOI: 10.3389/fpls.2023.1170448.

  18. Zhan J, Bélanger S, Lewis S, Teng C, McGregor M, Beric A, Schon MA, Nodine MD, Meyers BC. 2024. Premeiotic 24-nt phasiRNAs are present in the Zea genus and unique in biogenesis mechanism and molecular function. bioRxiv:2024.03.29.587306. DOI: 10.1101/2024.03.29.587306.

  19. Nandudu L, Strock C, Ogbonna A, Kawuki R, Jannink J-L. 2024. Genetic analysis of cassava brown streak disease root necrosis using image analysis and genome-wide association studies. Frontiers in Plant Science 15:1360729. DOI: 10.3389/fpls.2024.1360729.

  20. Sun Y, Miller C, Rajurkar AB, Lynch RC, Alyward A, Zhang L, Shaner M, Copeland CD, Ye H, Nguyen HT, Busch W, Michael TP. 2024. Genome-Wide Association Study Reveals Influence of Cell-specific Gene Networks on Soybean Root System Architecture. bioRxiv:2024.02.27.581071. DOI: 10.1101/2024.02.27.581071.

  21. Rankenberg T, van Veen H, Sedaghatmehr M, Liao C-Y, Devaiah MB, Stouten EA, Balazadeh S, Sasidharan R. 2024. Differential leaf flooding resilience in Arabidopsis thaliana is controlled by ethylene signaling-activated and age-dependent phosphorylation of ORESARA1 activity. Plant Communications:100848. DOI: 10.1016/j.xplc.2024.100848.

  22. Styer A, Pettinga D, Caddell D, Coleman-Derr D. 2024. Improving rice drought tolerance through host-mediated microbiome selection. bioRxiv:2024.02.03.578672. DOI: 10.1101/2024.02.03.578672.

  23. Mathieu D, Bryson AE, Hamberger B, Singan V, Keymanesh K, Wang M, Barry K, Mondo S, Pangilinan J, Koriabine M, Grigoriev IV, Bonito G, Hamberger B. 2024. Multilevel analysis between Physcomitrium patens and Mortierellaceae endophytes explores potential long-standing interaction among land plants and fungi. The Plant Journal: For Cell and Molecular Biology. DOI: 10.1111/tpj.16605.

  24. Arango-Caro S, Ying K, Lee I, Parsley K, Callis-Duehl K. 2024. A model of science, technology, engineering, and mathematics remote research-based learning. The American Biology Teacher 86:24–29. DOI: 10.1525/abt.2024.86.1.24.

  25. Liu M-G, Campbell T, Li W, Wang X-Q. 2023. Analyzing architectural diversity in maize plants using the skeleton-image-based method. Journal of Integrative Agriculture 22:3804–3809. DOI: 10.1016/j.jia.2023.05.017.

  26. Kirwan RF, Abbas F, Atmosukarto I, Loo AWY, Lim JH, Yeo S. 2023. Scalable agritech growbox architecture. Frontiers in the Internet of Things 2. DOI: 10.3389/friot.2023.1256163.

  27. Pongkorn A, Suriya N, Orapadee J. 2023. Design and development of a low cost automated greenhouse for plant phenotyping. In: Proceedings of the 2023 4th International Conference on Control, Robotics and Intelligent System. CCRIS ’23. New York, NY, USA: Association for Computing Machinery, 201–206. DOI: 10.1145/3622896.3622929.

  28. June V, Song X, Jeffrey Chen Z. 2023. Imprinting but not cytonuclear interactions affects parent-of-origin effect on seed size in Arabidopsis hybrids. bioRxiv:2023.09.15.557997. DOI: 10.1101/2023.09.15.557997.

  29. Tegtmeier R, Hickok D, Robbins K, Khan A. 2023. An image-analysis based leaf disc assay for the rapid evaluation of genetic resistance to fire blight in apples. European Journal of Plant Pathology / European Foundation for Plant Pathology. DOI: 10.1007/s10658-023-02750-8.

  30. Huber M, Julkowska MM, Snoek LB, van Veen H, Toulotte J, Kumar V, Kajala K, Sasidharan R, Pierik R. 2023. Towards increased shading capacity: A combined phenotypic and genetic analysis of rice shoot architecture. Plants, People, Planet. DOI: 10.1002/ppp3.10419.

  31. Derba-Maceluch M, Sivan P, Donev EN, Gandla ML, Yassin Z, Vaasan R, Heinonen E, Andersson S, Amini F, Scheepers G, Johansson U, Vilaplana FJ, Albrectsen BR, Hertzberg M, Jönsson LJ, Mellerowicz EJ. 2023. Impact of xylan on field productivity and wood saccharification properties in aspen. Frontiers in Plant Science 14:1218302. DOI: 10.3389/fpls.2023.1218302.

  32. Acosta-Gamboa L, Czymmek K, Klebanovych A, Kenney S, Gordon J, Gehan M. 2023. Utilization of Imaging Approaches to Understand Chenopodium quinoa, a Model Plant to Study Salt Stress. Microscopy and Microanalysis 29:866–867. DOI: 10.1093/micmic/ozad067.429.

  33. Kharraz N, Szabó I. 2023. Monitoring of plant growth through methods of phenotyping and image analysis. COLUMELLA – Journal of Agricultural and Environmental Sciences 10:49–59. DOI: 10.18380/SZIE.COLUM.2023.10.1.49.

  34. Gohari AM, Noei FG, Ebrahimi A, Ghanbari MA, Didaran F, Farzaneh M, Mehrabi R. 2023. Physiological and molecular responses of a resistant and susceptible wheat cultivar to the fungal wheat pathogen Zymoseptoria tritici. Research Square. DOI: 10.21203/rs.3.rs-3062887/v1.

  35. Griffiths M, Liu AE, Gunn SL, Mutan NM, Morales EY, Topp CN. 2023. A temporal analysis and response to nitrate availability of 3D root system architecture in diverse pennycress (Thlaspi arvense L.) accessions. Frontiers in Plant Science 14. DOI: 10.3389/fpls.2023.1145389.

  36. Katz E, Knapp A, Lensink M, Keller CK, Stefani J, Li J-J, Shane E, Tuermer-Lee K, Bloom AJ, Kliebenstein DJ. 2022. Genetic variation underlying differential ammonium and nitrate responses in Arabidopsis thaliana. The Plant Cell 34:4696–4713. DOI: 10.1093/plcell/koac279.

  37. Dang LM, Min K, Nguyen TN, Park HY, Lee, O New, Song H-K, Moon H. 2023. Vision-Based White Radish Phenotypic Trait Measurement with Smartphone Imagery. Agronomy 13:1630. DOI: 10.3390/agronomy13061630.

  38. Genangeli A, Avola G, Bindi M, Cantini C, Cellini F, Grillo S, Petrozza A, Riggi E, Ruggiero A, Summerer S, Tedeschi A, Gioli B. 2023. Low-cost hyperspectral imaging to detect drought stress in high-throughput phenotyping. Plants 12. DOI: 10.3390/plants12081730.

  39. Bethge H, Mohammadi Nakhjiri Z, Rath T, Winkelmann T. 2023. Towards automated detection of hyperhydricity in plant in vitro culture. Plant Cell, Tissue and Organ Culture. DOI: 10.1007/s11240-023-02528-0.

  40. Sakeef N, Scandola S, Kennedy C, Lummer C, Chang J, Uhrig RG, Lin G. 2023. Machine learning classification of plant genotypes grown under different light conditions through the integration of multi-scale time-series data. Computational and Structural Biotechnology Journal 21:3183–3195. DOI: 10.1016/j.csbj.2023.05.005.

  41. Liu M-G, Thomas C, Li W, Wang X-Q. 2023. A skeleton-image-based method for analyzing architectural diversity in maize plants. Journal of Integrative Agriculture. DOI: 10.1016/j.jia.2023.05.017.

  42. Tegtmeier R, Hickok D, Robins K, Khan A. 2023. Image-based leaf disc assay for the rapid evaluation of genetic resistance to fire blight in apples. DOI: 10.21203/rs.3.rs-2829015/v1.

  43. Iturburu L, Kwannandar J, Dyke SJ, Liu X, Zhang X, Ramirez J. 2023. Towards rapid and automated vulnerability classification of concrete buildings. Earthquake Engineering and Engineering Vibration 22:309–332. DOI: 10.1007/s11803-023-2171-2.

  44. Panda K, Mohanasundaram B, Gutierrez J, McLain L, Castillo SE, Sheng H, Casto A, Gratacós G, Chakrabarti A, Fahlgren N, Pandey S, Gehan MA, Slotkin RK. 2023. The plant response to high CO2 levels is heritable and orchestrated by DNA methylation. The New Phytologist 238:2427–2439. DOI: 10.1111/nph.18876.

  45. Manss C, von Szadkowski K, Bald J, Richard D, Scholz C, König D, Ruckelshausen A. 2023. Towards selective hoeing depending on evaporation from the soil. In: Bonn: Gesellschaft für Informatik e.V., 149–158. LINK.

  46. 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.

  47. 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.

  48. 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.

  49. Mohagheghi A, Moallem M. 2023. An energy-efficient PAR-based horticultural lighting system for greenhouse cultivation of lettuce. IEEE Access: Practical Innovations, Open Solutions 11:8834–8844. DOI: 10.1109/access.2023.3237757.

  50. Kwon YM, Vranken N, Hoge C, Lichak MR, Norovich AL, Francis KX, Camacho-Garcia J, Bista I, Wood J, McCarthy S, Chow W, Tan HH, Howe K, Bandara S, von Lintig J, Rüber L, Durbin R, Svardal H, Bendesky A. 2022. Genomic consequences of domestication of the Siamese fighting fish. Science Advances 8:eabm4950. DOI: 10.1126/sciadv.abm4950.

  51. 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.

  52. 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.

  53. 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.

  54. 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.

  55. 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.

  56. 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.

  57. Kinose R, Utsumi Y, Iwamura M, Kise K. 2022. Tiller estimation method using deep neural networks. DOI: 10.21203/rs.3.rs-1552723/v1.

  58. 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.

  59. 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.

  60. 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.

  61. 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.

  62. 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.

  63. Pollari M, Sipari N, Poque S, Himanen K, Mäkinen K. 2022. Effects of poty-Potexvirus synergism on growth, photosynthesis and metabolite status of Nicotiana benthamiana. Viruses 15:121. DOI: 10.3390/v15010121.

  64. 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.

  65. Ebersbach J, Khan NA, McQuillan I, Higgins EE, Horner K, Bandi V, Gutwin C, Vail SL, Robinson SJ, Parkin IAP. 2021. Exploiting high-throughput indoor phenotyping to characterize the founders of a structured B. napus breeding population. Frontiers in Plant Science 12:780250. DOI: 10.3389/fpls.2021.780250.

  66. 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.

  67. 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.

  68. 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.

  69. 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.

  70. 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.

  71. 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.

  72. 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.

  73. 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.

  74. 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.

  75. 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.

  76. 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.

  77. 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.

  78. 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.

  79. 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.

  80. 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.

  81. 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.

  82. Komjáthy L, Jung A, Lengyel J, Pék Z. 2021. Gépi látáson alapuló szikleveles paradicsompalánta számláló alkalmazása. KERTGAZDASÁG HORTICULTURE 53:36–45.

  83. 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.

  84. 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.

  85. 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.

  86. 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.

  87. 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.

  88. 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.

  89. 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.

  90. 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.

  91. 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.

  92. 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.

  93. Misra T, Arora A, Marwaha S, Ray M, Raju D, Kumar S, Goel S, Sahoo RN, Chinnusamy V. 2019. Artificial neural network for estimating leaf fresh weight of rice plant through visual-nir imaging. Indian Journal of Agricultural Sciences 89. DOI: 10.56093/ijas.v89i10.94631.

  94. 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.

  95. 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.

  96. 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.

  97. 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.

  98. 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.

  99. 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.

  100. 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.

  101. 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.

  102. 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.

  103. Gehan MA, Kellogg EA. 2017. High-throughput phenotyping. American Journal of Botany 104:505–508. DOI: 10.3732/ajb.1700044.

  104. 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.

Dissertations and Reports that use PlantCV software

  1. Knapp A. 2024. A High-Resolution Oxygen Analyzer and Other Tools for Plant Nutrition Research and Beyond. UC Davis.

  2. Castaño Mansillas J. 2024. Eina per la simulació intel· ligent d’il· luminació artificial per a models 3D de patrimoni cultural. Universitat Politècnica de Catalunya (UPC) - BarcelonaTech.

  3. Mathieu DTAD. 2024. Physcomitrium patens: Applications in Synthetic Biology and the Curation of Diterpenoid Libraries.

  4. Davidsson A. 2024. ASIC implementation exploration for EfficientNet optimizations. Department of Electrical and Information Technology Lund University.

  5. Nandudu L. 2024. Genomics and High-Throughput Phenotyping of Cassava Brown Streak Viruses. Cornell University.

  6. Garassino F, Caracciolo L, é van de Belt J, Albiero M, Pool R, Schreurs F, de Ridder D, Harbinson J, Aarts MGM. 2023. Analysis of natural variation in photosynthesis in a panel of Brassicaceae species. Wageningen University, Wageningen, the Netherlands.

  7. Bang DL. 2023. Evolução da anatomia vocal associada à diversidade acústica em Bokermannohyla Faivovich, Haddad, Garcia, Frost, Campbell & Wheeler, 2005 (Anura, Hylidae …. Universidade de São Paulo.

  8. Corlew A. 2023. Assessing the utility of high-throughput phenotyping for ecological applications. University of Tennessee at Chattanooga.

  9. Lemaire L, Lallemand M, Draye X. 2023. Innovation pédagogique dans le cadre des travaux pratiques de physiologie végétale: développement d’une station de phénotypage pour plante individuelle. Université catholique de Louvain.

  10. Garrity NK. 2023. Applied Image Based Pod Characterization of Virginia Type Peanuts (A. hypogaea). North Carolina State University.

  11. Yong YH. 2023. IoT data analytics for operational status tracking in the agriculture field. eprints.utar.edu.my.

  12. Genangeli A. 2023. Innovative applications of low-cost hyperspectral technologies in plants phenotyping and post-harvest processes. University of Florence.

  13. Restrepo-Arias J. 2023. Método de clasificación de imágenes, empleando técnicas de inteligencia artificial, integrado a una plataforma IoT de agricultura inteligente. Universidad Nacional de Colombia.

  14. Gautam S. 2023. Natural variation in camelina nitrogen responses. Montana State University.

  15. Morris A. 2022. Effect of heat stress on in vitro pollen germination and pollen tube elongation of Chenopodium quinoa and wild relatives. King Abdullah University of Science and Technology.

  16. Tellez Salamanca WD, Rodríguez Cruz YM. 2022. Diseño de robot cartesiano para procesos de segmentación y fenotipado de plantas con visión artificial. Universidad Santo Tomás.

  17. Pommerenke RSN. 2022. Assessing Phenotypic Response of Plants Irrigated with Municipal Solid Waste Landfill Leachate Using Computer Visualization. Missouri University of Science and Technology.

  18. Mohagheghi A. 2022. Intelligent Control and Monitoring for Energy-efficient Horticultural Lighting. Simon Fraser University.

  19. Herrera GDM. 2022. Genetic Characterization of Resitance to Frogeye Leaf Spot of Soybean. Louisiana State University.

  20. Al-Lami M. 2022. Ecological restoration of lead/zinc/copper mine tailings: Phytomanagement and amendment strategies to enhance substrate functionality and biomass production. Missouri University of Science and Technology.

  21. Garcia JMG. 2022. Computer Vision Phenomics and Quantitative Genetics of Sweet Corn Ear Architecture and Fungal Disease Resistance. University of Florida.

  22. Hodge JG. 2022. Genetic Regulation of Vegetative Architecture Across Ontogeny in the Model Grass Setaria. Oklahoma State University.

  23. Rodríguez P, Javier E. 2021. Computer imaging strategies for feature-rich quantitative analysis of plant pathogens. Universitat Politècnica de València.

  24. Wiering NP. 2021. Methods and knowledge towards the improvement of legume cover crops for the Northern US.

  25. Ta JK. 2021. High-throughput phenotyping and modeling to dissect the genetic architecture of plant plasticity and growth. University of California Davis.

  26. Adke SS. 2021. Supervised and Weakly Supervised Deep Learning for Instance Segmentation and Counting of Plant Parts. University of Georgia.

  27. Wang L. 2021. Characterization of a Novel Protein Kinase Involved in Flowering. era.library.ualberta.ca.

  28. de Almeida Rodrigues L. 2021. PixelCropRobot, protótipo robótico de baixo custo para fenotipagem em horticultura. Faculdade de Ciências da Universidade do Porto (FCUP).

  29. Sorice A, Petrie J, Patrick S, Daniel T, Tkach C, Patterson Q, Weyrick N, Guarnieri K, Venturini A, Herkins M, Varughese S, Ling P, Fife J. 2020. Volume Optimization for Food Production During Deep Space Exploration. ntrs.nasa.gov.

  30. Hurtado CV. 2020. Evaluation of the symbiotic relation between endophyte and poplar trees exposed to landfill leachate. Missouri University of Science and Technology.

  31. Gamboa LA. 2019. Assessing the contribution of multiple ascorbate pathways to abiotic stress tolerance using phenomic approaches. Arkansas State University.

  32. Liang Z. 2019. New Approaches to Use Genomics, Field Traits, and High-throughput Phenotyping for Gene Discovery in Maize (Zea mays). University of Nebraska.

  33. Mostaeen G. 2019. Towards Collaborative Scientific Workflow Management System. University of Saskatchewan.

  34. Hoyer JS. 2017. Analysis of Argonaute-small RNA-Transcription Factor Circuits Controlling Leaf Development. Washington University in St. Louis. DOI: 10.7936/K7KP81KG.