Automated Feature-Specific Tree Species Identification from Natural Images using Deep Semi-Supervised Learning
This addresses the problem of automated tree species identification for ecological monitoring and forestry, offering a tool for real-world settings, though it is incremental as it builds on existing semi-supervised methods.
The paper tackled tree species identification from natural images by developing a deep semi-supervised learning approach that leverages unlabeled data, achieving accuracies of 93.96% for leaves and 93.11% for bark, with top-5 classification accuracies of 94.04% for leaves and 83.04% for bark.
Prior work on plant species classification predominantly focuses on building models from isolated plant attributes. Hence, there is a need for tools that can assist in species identification in the natural world. We present a novel and robust two-fold approach capable of identifying trees in a real-world natural setting. Further, we leverage unlabelled data through deep semi-supervised learning and demonstrate superior performance to supervised learning. Our single-GPU implementation for feature recognition uses minimal annotated data and achieves accuracies of 93.96% and 93.11% for leaves and bark, respectively. Further, we extract feature-specific datasets of 50 species by employing this technique. Finally, our semi-supervised species classification method attains 94.04% top-5 accuracy for leaves and 83.04% top-5 accuracy for bark.