Abstract
Plant identification is a fine-grained classification task which aims to identify the family, genus, and species according to plant appearance features. Inspired by the hierarchical structure of taxonomic tree, the taxonomic loss was proposed, which could encode the hierarchical relationships among multilevel labels into the deep learning objective function by simple group and sum operation. By training various neural networks on PlantCLEF 2015 and PlantCLEF 2017 datasets, the experimental results demonstrated that the proposed loss function was easy to implement and outperformed the most commonly adopted cross-entropy loss. Eight neural networks were trained, respectively, by two different loss functions on PlantCLEF 2015 dataset, and the models trained by taxonomic loss led to significant performance improvements. On PlantCLEF 2017 dataset with 10,000 species, the SENet-154 model trained by taxonomic loss achieved the accuracies of 84.07%, 79.97%, and 73.61% at family, genus and species levels, which improved those of model trained by cross-entropy loss by 2.23%, 1.34%, and 1.08%, respectively. The taxonomic loss could further facilitate the fine-grained classification task with hierarchical labels.
Highlights
As the main form of life on the earth, plant plays an indispensable role in the ecosystem, which ensures the sustainable development of human society
The InceptionResNet-v2 trained by taxonomic loss obtains the most significant performance increase compared with the crossentropy one and improves three-level accuracies of 2.70%, 2.28%, and 2.45%. ese experimental results demonstrated that the proposed taxonomic loss was easy to implement and could effectively facilitate the training of both light-weight and complex neural networks
The state-of-the-art convolutional neural network (CNN) were trained by crossentropy and taxonomic loss on PlantCLEF 2017 dataset to further verify the proposed algorithm
Summary
As the main form of life on the earth, plant plays an indispensable role in the ecosystem, which ensures the sustainable development of human society. Plant identification is a crucial component of plant ecological research workflow, which is the foundation to protect and develop the plant diversity. As for the general public, identifying plant and learning its knowledge is an interesting and necessary experience. There are several methods of identifying plant, including taxonomic keys, written description, specimen comparison, and image comparison, expert determination is usually necessary [1]. For the large quantity of plant species and the low readability of taxonomic information, taxonomic knowledge and species identification skills are restricted to a limited and reducing number of persons [2, 3]. Even for experts with professional plant knowledge, it is not practical to identify all kinds of plant species by the manual identification methods, while for non-experts, it seems to be more infeasible
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