Abstract: We use deep convolutional neural networks to identify the plant species captured in a photograph and evaluate different factors affecting the performance of these networks. Three powerful and popular deep learning architectures, namely GoogLeNet, AlexNet, and VGGNet, are used for this purpose. Transfer learning is used to fine-tune the pre-trained models, using the plant task datasets of LifeCLEF 2015. To decrease the chance of overfitting, data augmentation techniques are applied based on image transforms such as rotation, translation, reflect...
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Topics: 
Artificial intelligence
Machine learning