32 Schmidt et al: Analysis of the Accuracy of Photo-Based Plant Identification Applications taken from different individuals of the same species so that no 2 photos of a single character were taken from a single tree (a bark photo and a leaf photo from the same tree was, however, permissible). As the team collected images, photos from several individu- als were collected and then aggregated into folders for the targeted species. Then, 4 leaf and 4 bark images were selected for each of the species being studied. When possible, leaves and bark without noticeable infection or infestation were selected (cherry leaf spot, Blumeriella jaapii [Rehm] Arx, was not feasible to exclude in Prunus serotina). Efforts were made when possible to attain photo- graphs representing the phenotypic variation present in the species in terms of morphology and tree age. For example, bark photos of young, mature, and old trees were included when possible, and for trees with multiple leaf shapes (e.g., Sassafras albidum), repre- sentatives of each leaf type were included. When pos- sible, photos of each species from different locations were included in order to attempt to account for some of the ecotypic variation in the species (e.g., Pinus rigida from the pitch pine/scrub oak forests of North New Jersey and the pitch pine forests of South New Jersey). The majority of these photos were taken in Mahlon Dickerson Reservation in Morris County and on the Rutgers University–Cook/Douglass campus in New Brunswick, as well as in Medford, Moorestown, and Pennsauken, New Jersey. All of the photos used in this study were taken by authors of this paper, the vast majority of which were collected in the month of July 2020. Phenotypic vari- ation between the photos of each species is therefore minimal due to the limited time of year they were col- lected. Photos were collected using the built-in cam- eras on either the Apple iPhone XS® , iPhone 11® 12-megapixel image, or a Samsung Galaxy S9® as a as a 12-megapixel image, as well as a small number from a Nikon 3100 digital camera as a 13.5-megapixel image. Bark photos were taken so that the only char- acter visible in the frame was the bark whenever pos- sible (i.e., avoiding leaves, fruits, and epicormic sprouts). Some space was left to the sides of the tree so that the whole trunk section could be viewed. The “zoom” feature was avoided when at all possible in order to ensure that the photo would not be distorted. Leaf photos were taken so that there would be one leaf (or possibly a few if the leaves were smaller) cen- tered and focused in the frame with the natural sur- roundings around it. Efforts were made to exclude ©2022 International Society of Arboriculture fruit and bark from the photos to ensure that they were identifying from the leaf alone. Epicormic sprouts were avoided when possible, as their form is often divergent from the typical canopy leaf. The images used in the study are freely available online via Rutgers University libraries (Schmidt et al. 2021). Data Collection Four bark photos and four leaf photos of each species were selected according to the above criteria and uploaded individually to each of the apps. For the sake of consistency, the photos were merely uploaded to the app and allowed to crop and focus on their own without any interference or the moving of frames. All photos were uploaded to a digital storage folder and then re-downloaded before uploading them to any of the apps so that there was no GPS data associated with the images. All apps were provided the same set of images, and all photos were uploaded to the apps within the state of New Jersey. Once a photo was uploaded, each app typically offered one or more guesses (an identification was not always made by PictureThis and Plant Identification) as to the identity of the plant. These identifications and suggestions were given in the form of a species name with a generic name and specific epithet (e.g., Acer rubrum). For this study, only automated or system-generated suggestions for plant identification were used. We did not consider the community aspects of some apps, wherein suggestions from experts or other users could have also been considered, negating an important supplemental aspect which is available in some apps (e.g., PlantNet, PlantSnap, and iNaturalist). In order to determine the accuracy of these identi- fications and suggestions to both the genus and species levels, we coded the responses by breaking the app suggestions into the genus and then the specific epithet components to segregate correct genus-level identifi- cations. We then recorded separately if the app correctly identified the plant’s genus and specific epithet. For clarity, and since completely different species can share the same specific epithet (e.g., ‘americana’ in Ulmus americana and Tilia americana), the specific epithet identification/suggestion was not used in isolation. The results were interpreted and recorded as follows: • Genus Identification: If the tree was identified correctly to the genus in the first suggestion, it received a score of 1 for the Genus Identification. If it was not, it received a score of 0.
January 2022
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