38 Schmidt et al: Analysis of the Accuracy of Photo-Based Plant Identification Applications foreign to the naturalist or ecologist. As a teaching tool, the power of linking an interested person to a larger, more professionally adept community is an invaluable asset (Pollock et al. 2015). There is a potential for questing or gamification (Kingsley and Grabner-Hagen 2015) of early natural resource man- agement or natural sciences students in the tactical use of these apps (Struwe et al. 2014). In order to better understand the limitations of these apps and in turn how to best utilize them to attain the most confident data possible, we set out to explore the effects that different morphological fea- tures had on the ability of the apps to correctly iden- tify a tree. Starting with the broadest morphological comparison, there seems to be a relatively small dif- ference between the ability of the apps to successfully identify broadleaf species and needle/scale-bearing species by leaf to genus (89.24% and 91.67%, respec- tively) and to species (65.60% and 63.89%, respec- tively). This is slightly surprising due to the apparent visual similarities between the leaves of needle-bearing trees. From a practical perspective, this could be a very important piece of information for community science projects and tree inventories, as it is a com- mon issue that many novices believe all needled ever- green trees belong to the genus Pinus (Bancks et al. 2018). The use of these apps can help to ensure that needle-bearing trees can be more often identified cor- rectly to at least the genus. When just considering broadleaf species, there are several morphological characteristics that offer an important insight into the success of these apps. Across all runs, the apps seem to have a higher percent of correct identifications to the genus for trees with com- pound leaves than for simple leaves (96.00% as opposed to 87.36%). This is likely in large part due to the greater number of genera within the region con- taining a majority of simple leaves, as opposed to compound leaves. In terms of the lobation of simple leaves, a similar trend seems to exist in regard to lobed leaves vs. unlobed leaves, with the seemingly more numerous unlobed genera having a lower percent correct identi- fication than the lobed leaves. When, however, the type of lobation (palmately or pinnately) is distin- guished, an interesting trend becomes apparent: when considering the identification of palmately lobed leaves, there was a staggering 100% correct identifi- cation rate to genus. This is particularly important as ©2022 International Society of Arboriculture we, the authors, find there to be a propensity for indi- viduals new to tree identification to misidentify Pla- tanus species as Acer species and vice versa, unless there is a specific training emphasis in this area. This distinction was addressed by Roman et al. (2017) when completing a brief training session with begin- ner tree inventory volunteers, which resulted in a high level of accuracy. With such a high percentage of proper identifications for this leaf type, the use of these apps seems to offer the ability of even inexperi- enced naturalists to confidently distinguish genera of trees with palmately lobed leaves when a similar type of training is not feasible. Taking a cue from the success of the apps with the bark of Betula species, it would be interesting to include only photos with bark containing visible len- ticels (e.g., young Prunus serotina, Pinus strobus, and Quercus rubra) in order to determine if there is a correlation between bark with visible lenticels and a higher percent identification, or if the Betula species are merely skewing the data. It is important to note that for deciduous species, a leafless condition or unreliable access to expanded leaves can occur in New Jersey from November to April, or 6 months of every year, which can put more pressure on attempt- ing to attain accurate identifications from bark (or bud) characteristics. Given the extremely low accu- racy of these apps in identifying trees by bark images, however, such apps did not seem to offer an adequate solution to this problem at the time of our study. From a managerial perspective, this is an area in which tar- geted software development would greatly improve the apps’ utility in the field. The taxonomy of tree species has the potential to illuminate helpful trends in species characteristics that can help divide the list of possible species into more manageable groups. If a potential user is able to identify the taxonomic order, family, or genus to which a particular specimen belongs, it can be very helpful to understand the reliability of the identifica- tion that the apps tend to provide. For example, if a tree with a nut is found, it can be predicted that a pho- tograph of the leaves will correctly identify the tree to genus 87.81% of the time. If the tree can even be nar- rowed down to the walnut family or the beech family, the confidence in correct identification to genus can increase even further to 100.00% and 96.25% for leaf photos, respectively. To take it even further, if a tree can be identified as an oak, there is an 83.06% chance
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