Automatic Leaf Recognition Based on Deep Convolutional Networks

Leaf recognition remains a hot research topic receiving intensive attention in computer vision. In this paper, we propose deep convolutional networks with deep learning framework on the large scale of leaf databases. Different from the existing leaf recognition algorithms that mainly depend on traditional feature extractions and pattern matching operations, our method can achieve automatic leaf recognition based on deep convolutional networks without any explicit feature extraction or matching. Because it does not require any feature detection and selection, the advantages of our framework are obvious, especially for the large scale leaf databases. Specifically, we design deep convolutional networks structure and adopt fine-tuning strategy for our network initialization. In addition, we also develop a visualization-guided parameter tuning scheme to guarantee the accuracy of our deep learning framework. Our method is evaluated on several different databases with different scales. Comparison experiments are performed and demonstrate that the accuracy of our method outperforms traditional methods.