The identification of tree species from bark images is a challenging computer vision problem. However, even in the era of deep learning today, bark recognition continues to be explored by traditional methods using time-consuming handcrafted features, mainly due to the problem of limited data. In this work, we implement a patch-based convolutional neural network alternative for analyzing a challenging bark dataset Bark-101, comprising of 2587 images from 101 classes. We propose to apply image re-scaling during the patch extraction process to compensate for the lack of sufficient data. Individual patch-level predictions from fine-tuned CNNs are then combined by classical majority voting to obtain image-level decisions. Since ties can often occur in the voting process, we investigate various tie-breaking strategies from ensemble-based classifiers. Our study outperforms the classification accuracy achieved by traditional methods applied to Bark-101, thus demonstrating the feasibility of applying patch-based CNNs to such challenging datasets.