Automatic Leaf Recognition Based on Attention DenseNet

Automatic leaf recognition algorithm is widely used in plant taxonomy, horticulture teaching, traditional Chinese medicine research and plant protection, which is one of the research hotspots in information science. Due to the diversity of plant leaves, the variety of leaf forms, and the susceptibility to seasonal and other external factors, there is often a small inter-class variance and a large intra-class variance, which brings great challenges to the task of automatic leaf recognition. To solve this problem, we propose a leaf recognition algorithm base on the attention mechanism and dense connection. Firstly, base on dense connection, DenseNet is applied to realize the cross-layer learning of our model, which effectively improves the generalization ability of the network to the intra-class variance. At the same time, the learning ability of our model to the discriminative features such as the veins and textures of plant leaves is also improved. Secondly, we also employ the attention mechanism to further enhance the ability of our network in learning discriminative features of plant leaves. The experimental results show that our Attention DenseNet achieves a high accuracy of leaf recognition in our plant leaf database, including the challenging cases. Visual and statistical comparisons with state-of-the-art methods also demonstrate its effectiveness.