Improved Herbarium-Field Triplet Network for Cross-Domain Plant Identification: NEUON Submission to LifeCLEF 2021 Plant.

This paper presents the submissions made by our team to PlantCLEF 2021. The challenge’s goal was to identify plant species based on the test set made from only plant images in the field, given a training dataset consisting of primarily herbarium images. We implemented a two-streamed Herbarium-Field Triplet Loss Network to evaluate the similarity between herbarium and field pairs, thereby matching species from both herbarium and field domains. The network is made from two convolutional neural networks taking herbarium and field images as input, respectively. The network employed is a similar but improved version of our submission to the previous year’s challenge [1]. In addition, we trained a one-streamed network taking both herbarium and field images as input to enable the learning of the features of each species irrespective of their domains. We found that an ensemble of these networks performed better than the Herbarium-Field Triplet Loss Network alone. We achieved a Mean Reciprocal Rank (MRR) of 0.181 for the primary metric, which focused on the whole test set. Comparably, we achieved an MRR of 0.158 for the secondary metric, which focused on the subset of species with fewer field training images.