  Convolutional neural networks (CNNs) have become popular especially in
computer vision in the last few years because they achieved outstanding
performance on different tasks, such as image classifications. We propose a
nine-layer CNN for leaf identification using the famous Flavia and Foliage
datasets. Usually the supervised learning of deep CNNs requires huge datasets
for training. However, the used datasets contain only a few examples per plant
species. Therefore, we apply data augmentation and transfer learning to prevent
our network from overfitting. The trained CNNs achieve recognition rates above
99% on the Flavia and Foliage datasets, and slightly outperform current methods
for leaf classification.
