  This paper investigates the issue of real-world identification to fulfill
better species protection. We focus on plant species identification as it is a
classic and hot issue. In tradition plant species identification the samples
are scanned specimen and the background is simple. However, real-world species
recognition is more challenging. We first systematically investigate what is
realistic species recognition and the difference from tradition plant species
recognition. To deal with the challenging task, an interdisciplinary
collaboration is presented based on the latest advances in computer science and
technology. We propose a novel framework and an effective data augmentation
method for deep learning in this paper. We first crop the image in terms of
visual attention before general recognition. Besides, we apply it as a data
augmentation method. We call the novel data augmentation approach attention
cropping (AC). Deep convolutional neural networks are trained to predict
species from a large amount of data. Extensive experiments on traditional
dataset and specific dataset for real-world recognition are conducted to
evaluate the performance of our approach. Experiments first demonstrate that
our approach achieves state-of-the-art results on different types of datasets.
Besides, we also evaluate the performance of data augmentation method AC.
Results show that AC provides superior performance. Compared with the precision
of methods without AC, the results with AC achieve substantial improvement.
