Zusammenfassung
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.
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