Digital Taxonomist: Identifying Plant Species in Community Scientists' Photographs
This incremental improvement supports ecosystems research and conservation efforts by enhancing species identification in community science photographs.
The paper tackled the problem of automatically identifying plant species from amateur photographs to improve species range maps, achieving over 6 percentage points higher accuracy than an image-only classifier.
Automatic identification of plant specimens from amateur photographs could improve species range maps, thus supporting ecosystems research as well as conservation efforts. However, classifying plant specimens based on image data alone is challenging: some species exhibit large variations in visual appearance, while at the same time different species are often visually similar; additionally, species observations follow a highly imbalanced, long-tailed distribution due to differences in abundance as well as observer biases. On the other hand, most species observations are accompanied by side information about the spatial, temporal and ecological context. Moreover, biological species are not an unordered list of classes but embedded in a hierarchical taxonomic structure. We propose a multimodal deep learning model that takes into account these additional cues in a unified framework. Our Digital Taxonomist is able to identify plant species in photographs better than a classifier trained on the image content alone, the performance gained is over 6 percent points in terms of accuracy.