Real-world plant species identification based on deep convolutional neural networks and visual attention

TL;DR

This paper presents a novel deep learning framework with an attention-based data augmentation method for real-world plant species identification, outperforming existing approaches on traditional and real-world datasets.

Contribution

It introduces an interdisciplinary framework and a new attention cropping data augmentation technique for improved plant species recognition in complex environments.

Findings

Achieves state-of-the-art results on multiple datasets.

Attention cropping (AC) enhances model performance significantly.

AC provides substantial improvement over non-augmented methods.

Abstract

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.