36 DISCUSSION We stress that this study was, by nature, limited in its scope (isolated to 55 species of trees commonly found in New Jersey urban and natural landscapes) and cannot be used as an accurate evaluation of these apps across all plant habits, taxa, and morphologies. Therefore, it should be understood that the following observations are meant to guide users who are likely to encounter the same taxa in their activities. This study also does not take into consideration the power of community and expert identifications available on some apps (Table 1); it only evaluates the suggestions given by the apps for immediate identification in the field. We acknowledge that the loss of a GPS coordi- nate may well influence output in some apps. The cosmopolitan species diversity of our regional urban plant community may negate the GPS value, or it could influence the aptness of the tool and its success in a forest inventory when considering a choice. Indeed, LeafSnap was initially conceived for and focuses on the tree species of the Northeastern forest community (Kumar et al. 2012), but our study sample extended to species in southern New Jersey beyond that database. Furthermore, we chose the apps for this study based on their download frequency and avail- ability of use. It is important to note that the apps are meant to engage larger aspects of the flora in total, and each app represents a different database, which can range from thousands to hundreds of thousands of species, as well as different algorithm learning tra- jectories for their own development for accuracy (Table 1). These various apps host vastly different scales of species range and type, with iNaturalist spanning beyond the plant kingdom (including ani- mals, fungi, and protists) as a community of experts and novices. The chi-squared test suggested repeatability in the output for constancy of identifications and sugges- tions, but as observed in general, there are limits to what can be expected as a tool to aid in tree species identification. That said, we fully expect that such outcomes would improve, as any specific app evolves with increased data process. The fact that an experi- enced observer can locate and define multiple traits much faster than can be accomplished with a phone camera enforces the use of such tools in support in training and confirmation. Across all apps, there was a general trend of higher percent accuracy in correctly identifying leaf ©2022 International Society of Arboriculture Schmidt et al: Analysis of the Accuracy of Photo-Based Plant Identification Applications photographs as opposed to bark photographs. This is not surprising, since the process in developing such tools has focused on image pattern recognition using shape, edge pattern, venation, and similar characteris- tics consistent with foliar morphology (Cope et al. 2012; Goëau et al. 2013; Wang et al. 2013; Wang et al. 2014; Zhao C et al. 2015; Zhao ZQ et al. 2015; Keivani et al. 2020). Our results highlight the general difficulty of using bark characteristics alone for tradi- tional tree identification due to the effects of conver- gent bark appearances across taxa, as well as the effects of the environment on bark texture and quali- ties. For the identification of trees in forested areas (where twigs and leaves are not easily observed) and the identification of deciduous trees during the win- ter, the use of bark can become a very reliable charac- teristic deserving greater attention. It would be reasonable to suggest that some ubiq- uitous species that have more data within an AI net- work, and those interesting species with iconic bark or leaf characters or aesthetically charismatic leaf form, would in general provide a higher confidence in either identifying or suggesting against a new image (e.g., the high genus-level identification rate for leaf photos of members of the Sapindaceae, including the easily recognizable Acer and Aesculus leaves). For PlantSnap in particular, while the percent of correct leaf identifications was only slightly below the per- centages for the other apps, the percent identifications for bark were exceedingly small, with only 1.36% iden- tification to genus and 0.00% identification to species. The app with the highest percentage of correctly identified photographs was PictureThis, with a com- bined leaf and bark correct identification percentage of 81.36% to genus and 67.84% to species. This app also boasts a 97.27% identification rate to genus and an 83.86% identification rate to species for leaf pho- tos, as well as a 65.45% identification rate to genus and a 51.82% identification rate to species for bark photos. With such a high percent accuracy for identi- fication of leaf photos to genus, we will likely suggest this app for our purposes with students if and when they feel they want to pay for such a tool as a confir- mation to their own field identifications. The PictureThis app always offered only one spe- cies identification for each photo uploaded in all but one taxon tested. That exception was with Taxodium distichum and Taxodium mucronatum, which were listed as difficult to distinguish from photographs and
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