Before revision:  Our results revealed that human experts using actual specimens yield the lowest classification error . However, our proposed, much faster, automated approach using deep Convolutional Neural Nets comes very close to human accuracy. Contrary to previous findings in the literature, we find that machines following a typical flat classification approach commonly used in machine learning performs better than forcing machines to adopt a hierarchical, local per parent node approach used by human taxonomic experts. Finally, we publicly share our unique dataset to serve as a public benchmark dataset in this field.
After revision:  Our results revealed that human experts using actual specimens yield the lowest classification error (