Arboriculture & Urban Forestry 48(1): January 2022 • Species Identification: If the tree was identified correctly to the species in the first suggestion, it received a score of 1 for the Species Identifica- tion. If it was not, it received a score of 0. - If the tree was identified to one of the hybrids of the correct species in the first suggestion (or identified as a parent of a tested hybrid), it received a score of 0.5 for Species Identification. • Suggested Genus/Genera: If the tree was identi- fied correctly to the genus in the first OR any other suggestion, it received a score of 1 for the Suggested Genus. If it was not, it received a score of 0. • Suggested Species: If the tree was identified correctly to the species in the first OR any other suggestion, it received a score of 1 for the Sug- gested Species. If it was not, it received a score of 0. - If the tree was identified to a hybrid of the correct species in any suggestion (or identi- fied as a parent of a tested hybrid), it received a score of 0.5 for Suggested Species. - If the tree was identified to more than one hybrid of the correct species in any suggestion (or identified as both parents of a tested hybrid), it received a score of 1 for Suggested Species. If the tree was misidentified in the first suggestion, the first proposed species was recorded. Data were tabulated as the percentage of correct identification or suggestion across each species bark and leaf set, or across classification or app groupings. We arbitrarily defined evaluation categories of high, moderate, and low confidence (95% to 100% correct, 80% to 94% correct, and < 80% correct, respec- tively). Data were developed and processed from the July 2020 photo collection through the following 50 days, so any inferences from the apps that were cho- sen are based on their program and algorithm devel- opment as of summer 2020. In order to ensure that the data collected would be consistent through multiple runs, all photos of 4 selected species (Quercus alba, Betula lenta, Acer saccharinum, and Pinus rigida) were run through all 6 apps for a second time several days after the first run, but before any updates were allowed to occur on any of the apps, as this could have influenced the accuracy of the apps (Jones 2020). Then, a chi-squared (χ2 ) test was run in order to deter- mine if there was a statistically significant difference between the outcomes of the multiple runs. 33 Finally, for interpretation of the results, species were categorized into groupings by bark characteris- tics as detailed in Wojtech (2011) to look for patterns in the app-response results: Peeling Horizontally, Lenticels Visible, Smooth Unbroken, Vertical Cracks or Seams in Otherwise Smooth Bark, Broken into Vertical Strips, Broken into Scales or Plates, or With Ridges and Furrows. For species with different bark types at different life stages or in different forms (e.g., the many bark types of Acer rubrum), the species was placed into each group (e.g., Acer rubrum being listed under Smooth Unbroken, Vertical Cracks or Seams, and Vertical Strips). When a taxon was not explicitly mentioned within Wojtech (2011), species were cate- gorized according to the text descriptions for each category. RESULTS Chi-squared values of χ² = 0.1296 and χ² = 0.0106 were determined for identifications and suggestions, respectively. Their corresponding P values (P = 0.7188 and P = 0.9179, respectively) both fail to reject the null hypothesis that there is no difference in the accu- racy of the apps’ identifications of the same photo- graphs on 2 different days with a significance level of 0.05. PlantSnap was able to correctly identify a compa- rable percentage of the tested leaf photos, however, the percentage of correct bark identifications was exceedingly low across all taxa. Due to the low levels of accuracy in the identification of North American trees by bark characteristics, the data collected from the PlantSnap app was excluded from consideration when looking for general trends across all apps as sorted by taxonomic order, family, genus, or species (Tables 2, 3, and 4). Across all apps, leaf photos always outperformed bark photos by a large margin. In terms of bark images alone, none of the tested apps provided an overall accuracy of over 70% in identifications and none over 80% in overall suggestions. We observed a moderate confidence in Genus Identifications for leaf photos across our selected taxa in all but 2 cases: PlantSnap provided a low confidence, and PictureThis provided a high confidence. Species Identifications for leaf images across all taxa were only moderately confident for PictureThis and showed low confidence for all other apps tested. For Genus Suggestions and Species Suggestions, scores generally increased ©2022 International Society of Arboriculture
January 2022
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