# References

**Effective date:** June 4, 2026

**Last updated:** July 14, 2026

## Peer-reviewed research

Atkins, I. K., & Boldt, J. K. (2022). Photosynthetic responses of greenhouse ornamentals to interaction of irradiance, carbon dioxide concentration, and temperature. Journal of the American Society for Horticultural Science, 147(2), 82–94. https://doi.org/10.21273/JASHS05115-21

De Pascale, S., Dalla Costa, L., Vallone, S., Barbieri, G., & Maggio, A. (2011). Increasing water use efficiency in vegetable crop production: From plant to irrigation systems efficiency. HortTechnology, 21(3), 301–308. https://doi.org/10.21273/HORTTECH.21.3.301

Faust, J. E., Holcombe, V., Rajapakse, N. C., & Layne, D. R. (2005). The effect of daily light integral on bedding plant growth and flowering. HortScience, 40(3), 645–649.

Faust, J. E., & Logan, J. (2018). Daily light integral: A research review and high-resolution maps of the United States. HortScience, 53(9), 1250–1257. https://doi.org/10.21273/HORTSCI13144-18

Gautam, B., Dubey, R. K., Kaur, N., & Choudhary, O. P. (2021). Growth response of indoor ornamental plant species to various artificial light intensities (LED) in an indoor vertical garden. Plant Archives, 21(1), 695–700. https://doi.org/10.51470/PLANTARCHIVES.2021.v21.no1.096

Goëau, H., Bonnet, P., & Joly, A. (2022). Overview of PlantCLEF 2022: Image-based plant identification at global scale. In Working Notes of CLEF 2022 (Vol. 3180, paper 153). CEUR Workshop Proceedings. https://ceur-ws.org/Vol-3180/paper-153.pdf

Hart, A. G., Bosley, H., Hooper, C., Perry, J., Sellors-Moore, J., Moore, O., & Goodenough, A. E. (2023). Assessing the accuracy of free automated plant identification applications. People and Nature, 5(3), 1042–1052. https://doi.org/10.1002/pan3.10460

Jones, H. G. (2020). What plant is that? Tests of automated image recognition apps for plant identification on plants from the British flora. AoB PLANTS, 12(6), plaa052. https://doi.org/10.1093/aobpla/plaa052

Jones, H. G., & Jones, A. J. (2025). Application and pitfalls of the use of plant ID apps for urban flora and citizen science studies. Plant Ecology & Diversity. https://doi.org/10.1080/17550874.2025.2476938

Kang, I., & Lopez, R. G. (2024). Photosynthetic daily light integral effects on rooting and vegetative growth of cuttings of six foliage plants. HortScience, 59(12), 1757–1762. https://doi.org/10.21273/HORTSCI18109-24

Kim, J., & van Iersel, M. W. (2009). Daily water use of Abutilon and Lantana at various substrate water contents. Proceedings of the SNA Research Conference, 54, 226–229.

Kuchařová, Z., Vašutová, D., & Vašut, R. J. (2025). Evaluating the accuracy of the Seek app for conifer identification: A baseline for future educational use. Natural Sciences Education, 54. https://doi.org/10.1002/nse2.70030

Moupojou, E., Tagne, A., Retraint, F., Tadonkemwa, A., Wilfried, D., Tapamo, H., & Nkenlifack, M. (2023). FieldPlant: A dataset of field plant images for plant disease detection and classification with deep learning. IEEE Access, 11, 35398–35410. https://doi.org/10.1109/ACCESS.2023.3263042

Noyan, M. A. (2022). Uncovering bias in the PlantVillage dataset (arXiv:2206.04374). arXiv. https://arxiv.org/abs/2206.04374

Otter, J., Mayer, S., & Tomaszewski, C. A. (2020). Swipe right: A comparison of accuracy of plant identification apps for toxic plants. Journal of Medical Toxicology, 17(1), 42–47. https://doi.org/10.1007/s13181-020-00803-6

Pennisi, S. V., & van Iersel, M. W. (2012). Quantification of carbon assimilation of plants in simulated and in situ interiorscapes. HortScience, 47(4), 468–476.

Rzanny, M., Bebber, A., Wittich, H. C., Fritz, A., Boho, D., Mäder, P., & Wäldchen, J. (2024). More than rapid identification—Free plant identification apps can also be highly accurate. People and Nature. https://doi.org/10.1002/pan3.10676

Schmidt, R. J., Casario, B. M., Zipse, P. C., & Grabosky, J. C. (2022). An analysis of the accuracy of photo-based plant identification applications on fifty-five tree species. Arboriculture & Urban Forestry, 48(1), 27–43. https://doi.org/10.48044/jauf.2022.003

Siddiqua, A., Kabir, M. A., Ferdous, T., Ali, I. B., & Weston, L. A. (2022). Evaluating plant disease detection mobile applications: Quality and limitations. Agronomy, 12(8), 1869. https://doi.org/10.3390/agronomy12081869

Taylor, C. M., McCauley, D. M., & Nackley, L. L. (2026). Illuminating indoor tropicals: Characterizing photosynthetic light responses in high-value houseplants. HortScience, 61(3), 554–556. https://doi.org/10.21273/HORTSCI19169-25

Van Iersel, M. W., Dove, S., Kang, J.-G., & Burnett, S. E. (2010). Growth and water use of petunia as affected by substrate water content and daily light integral. HortScience, 45(2), 277–282.

Wang, Y.-T. (1987). Effect of warm medium, light intensity, BA, and parent leaf on propagation of golden pothos. HortScience, 22(4), 597–599.

Warsaw, A. L., Fernandez, R. T., Cregg, B. M., & Andresen, J. A. (2009). Water conservation, growth, and water use efficiency of container-grown woody ornamentals irrigated based on daily water use. HortScience, 44(5), 1308–1318.

Wei, T., Chen, Z., Yu, X., et al. (2026). A large-scale in-the-wild dataset for plant disease segmentation. Scientific Data, 13, 205. https://doi.org/10.1038/s41597-025-06513-4

Xu, M., Park, J.-E., Lee, J., Yang, J., & Yoon, S. (2024). Plant disease recognition datasets in the age of deep learning: Challenges and opportunities. Frontiers in Plant Science, 15, 1452551. https://doi.org/10.3389/fpls.2024.1452551

## Data sources, APIs, and reference datasets

ASPCA Animal Poison Control Center. (2026). Toxic and non-toxic plants [Data set]. Compiled and distributed in machine-readable form by Plant Smart. https://www.aspca.org/pet-care/animal-poison-control/toxic-and-non-toxic-plants

Association of Specialty Cut Flower Growers. (2026). Cut Flowers of the Year [Award program records, 2000–2026]. https://www.ascfg.org/about-us/cut-flowers-of-the-year/

Costello, L. R., & Jones, K. S. (2014). WUCOLS IV: Water use classification of landscape species [Data set]. University of California Cooperative Extension and California Department of Water Resources. https://ucanr.edu/sites/WUCOLS/

Global Biodiversity Information Facility. (2026). GBIF Backbone Taxonomy [Data set]. https://www.gbif.org/dataset/d7dddbf4-2cf0-4f39-9b2a-bb099caae36c

Goêau, H., Bonnet, P., & Joly, A. (2026). Pl@ntNet API [Computer software]. INRIA / CIRAD / IRD / INRAE. https://my.plantnet.org/

Gross, K. C., Wang, C. Y., & Saltveit, M. E. (Eds.). (2016). The commercial storage of fruits, vegetables, and florist and nursery stocks: Cut flowers and greens (Agriculture Handbook 66). USDA Agricultural Research Service. https://www.ars.usda.gov/northeast-area/beltsville-md-barc/beltsville-agricultural-research-center/food-quality-laboratory/docs/ah66/

Kindwise. (2026). Plant.id API v3 [Computer software]. https://web.plant.id/

National Renewable Energy Laboratory. (2009). National Solar Radiation Database (NSRDB), 1998–2009 [Data set]. U.S. Department of Energy. https://nsrdb.nrel.gov/

Open-Meteo. (2026). Open-Meteo weather API [Computer software]. https://open-meteo.com/

Poelen, J. H., Simons, J. D., & Mungall, C. J. (2026). Global Biotic Interactions: Interpreted data products (v0.9) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.20546682

PRISM Climate Group. (2023). USDA Plant Hardiness Zone Map [Data set]. Oregon State University and U.S. Department of Agriculture. https://planthardiness.ars.usda.gov/

Robinson, G. S., Ackery, P. R., Kitching, I. J., Beccaloni, G. W., & Hernández, L. M. (2023). HOSTS – a database of the world's lepidopteran hostplants [Data set]. Natural History Museum. https://data.nhm.ac.uk/dataset/hosts/resource/877f387a-36a3-486c-a0c1-b8d5fb69f85a

USA National Phenology Network. (2026). Nature's Notebook observational dataset [Data set]. Accessed via the USA-NPN Data Services API. https://www.usanpn.org/data/observational

Wikimedia Foundation. (2026). Wikipedia and Wikimedia Commons [Online encyclopedias and media repository]. https://www.wikipedia.org/, https://commons.wikimedia.org/

World Flora Online Consortium. (2026). World Flora Online [Data set]. http://www.worldfloraonline.org/

## Vendor disclosures and competitor product surfaces

iNaturalist. (2025, September). Updated computer vision model and geomodel with over 1,500 new taxa (v2.25) [Forum post]. https://www.inaturalist.org/posts/118979-updated-computer-vision-model-and-geomodel-with-over-1-500-new-taxa

Kindwise. (2023, November). Major Plant.id model upgrade [Blog post]. https://www.kindwise.com/post/major-plant-id-model-upgrade

Kindwise. (2024). 2024 Plant.id model update (v4.0.1) [Blog post]. https://www.kindwise.com/post/2024-model-update

Kindwise. (2026). Plant.id [Product page]. https://www.kindwise.com/plant-id

PictureThis. (2026). PictureThis – Plant identifier [App Store listing, id1252497129]. Apple App Store. https://apps.apple.com/us/app/picturethis-plant-identifier/id1252497129

PictureThis. (2026). PictureThis [Homepage]. https://www.picturethisai.com/

Planta. (2026). About Planta. https://getplanta.com/en/about

Planta. (2026). Planta – AI plant & garden care [App Store listing, id1410126781]. Apple App Store. https://apps.apple.com/us/app/planta-ai-plant-garden-care/id1410126781

Planta. (2026). Planta [Homepage]. https://getplanta.com/

University of Illinois Extension. (2022, January 21). How accurate are photo-based plant identification apps? [Blog post]. https://extension.illinois.edu/blogs/garden-scoop/2022-01-21-how-accurate-are-photo-based-plant-identification-apps