40 Schmidt et al: Analysis of the Accuracy of Photo-Based Plant Identification Applications immediate responses to photo uploads from the field. This app could be considered if sufficient funds are available or the need for accuracy is of the utmost importance. If funds are limited, iNaturalist seems to be the closest to PictureThis in terms of identification ability and also offers a community-based feature within the app that can help to gain a second (and often expert) opinion on a troublesome identification if time is not a factor. This feature might also be very helpful from an educational or training support per- spective by providing feedback on a user’s identifica- tion. Of course, over time and with different flora and context of use, other apps would possibly be preferred for other audiences. These identification apps also seem to have areas of weakness that are not limited to an individual app, such as the identification of unlobed leaves (79.69% vs. 98.13% for lobed leaves) and bark photos as a whole, in addition to relatively low identification rates for Betula leaves as well as the bark and leaves of Magnolia species. While currently problematic, this illuminates a very promising area for future, more targeted software development in order to better address these shared shortcomings. In general, despite the perception that these apps can be used to correctly identify plants to the species level, it is clear that these apps can, as a whole, only be expected to provide consistent and accurate identi- fications of Northeastern trees to the genus level at best. While this level of identification may be very helpful in reducing the potential species pool for identification within a genus, it is clear that in their current form, they do not consistently possess the accuracy needed to replace traditional identification tools or experienced professionals. LITERATURE CITED Bancks N, North EA, Johnson GR. 2018. An analysis of agree- ment between volunteer- and researcher-collected urban tree inventory data. Arboriculture & Urban Forestry. 44(2):73-86. https://doi.org/10.48044/jauf.2018.007 Barré P, Stöver BC, Müller KF, Steinhage V. 2017. LeafNet: A computer vision system for automatic plant species identifi- cation. Ecological Informatics. 40:50-56. https://doi.org/10 .1016/j.ecoinf.2017.05.005 Bilyk ZI, Shapovalov YB, Shapovalov VB, Megalinska AP, Andruszkiewicz F, Dołhańczuk-Śródka A. 2020. Assessment of mobile phone applications feasibility on plant recognition: Comparison with Google Lens AR-app. CEUR Workshop Proceedings 2731. p. 61-78. http://www.ceur-ws.org/Vol -2731/paper02.pdf Bonnet P, Joly A, Goëau H, Champ J, Vignau C, Molino JF, Bar- thélémy D, Boujemaa N. 2015. Plant identification: Man vs. machine. Multimedia Tools and Applications. 75(3):1647-1665. https://doi.org/10.1007/s11042-015-2607-4 Bonney R, Cooper CB, Dickinson J, Kelling S, Phillips T, Rosenberg KV, Shirk J. 2009. Citizen science: A developing tool for expanding science knowledge and scientific literacy. BioScience. 54(11):977-984. https://doi.org/10.1525/bio.2009 .59.11.9 Collins BR, Anderson KH. 1994. Plant communities of New Jersey: A study in landscape diversity. New Brunswick (NJ, USA): Rutgers University Press. 308 p. Cope JS, Corney D, Clark JY, Remagnino P, Wilkin P. 2012. Plant species identification using digital morphometrics: A review. Expert Systems with Applications. 39(8):7562-7573. https:// doi.org/10.1016/j.eswa.2012.01.073 Crall AW, Newman GJ, Stohlgren TJ, Holfelder KA, Graham J, Waller DM. 2011. Assessing citizen science data quality: An invasive species case study. Conservation Letters. 4(6):433-442. https://doi.org/10.1111/j.1755-263X.2011.00196.x Crocker SJ, Barnett CJ, Butler BJ, Hatfield MA, Kurtz CM, Lister TW, Meneguzzo DM, Miles PD, Morin RS, Nelson MD, Piva RJ, Riemann R, Smith JE, Woodall CW, Zipse W. 2017. New Jersey Forests 2013. Newtown Square (PA, USA): USDA Forest Service, Northern Research Station. RB NRS- 109. 90 p. https://doi.org/10.2737/NRS-RB-109 Crown CA, Greer BZ, Gift DM, Watt FS. 2018. Every tree counts: Reflections on NYC’s third volunteer street tree inventory. Arboriculture & Urban Forestry. 44(2):49-58. https://doi .org/10.48044/jauf.2018.005 Echeverria A, Ariz I, Moreno J, Peralta J, Gonzalez E. 2021. Learning plant biodiversity in nature: The use of the citizen– science platform iNaturalist as a collaborative tool in second- ary education. Sustainability. 13(2):735. https://doi.org/10 .3390/su13020735 Goëau H, Bonnet P, Joly A, Bakić V, Barbe J, Yahiaoui I, Selmi S, Carré J, Barthélémy D, Boujemaa N, Molino JF. 2013. Pl@ntNet mobile app. In: Proceedings of the 21st ACM international conference on multimedia. MM ’13: ACM Multimedia Conference; 2013 October 21–25; Barcelona, Spain. New York (NY, USA): Association for Computing Machinery. p. 423-424. iNaturalist (version 2.8.7) [Mobile application software]. San Francisco (CA, USA): iNaturalist, LLC. 2020 January 10. https://apps.apple.com/us/app/inaturalist/id421397028 Joly A, Goëau H, Bonnet P, Bakić V, Barbe J, Selmi S, Yahaioui I, Carré J, Mouysset E, Molino JF, Boujemaa N, Barthélémy D. 2014. Interactive plant identification based on social image data. Ecological Informatics. 23:22-34. https://doi .org/10.1016/j.ecoinf.2013.07.006 Jones H. 2020. Viewpoint: What plant is that? Tests of automated image recognition apps for plant identification on plants from the British flora. AoB PLANTS. 12(6):1-9. https://doi.org/10 .1093/aobpla/plaa052 Keivani M, Mazloum J, Sedaghatfar E, Tavakoli MB. 2020. Automated analysis of leaf shape, texture, and color features for plant classification. Traitement du Signal. 37(1):17-28. https://doi.org/10.18280/ts.370103 ©2022 International Society of Arboriculture
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