# Digital Taxonomist: Identifying Plant Species in Community Scientists' Photographs

arXiv: https://arxiv.org/abs/2106.03774 · HTML: https://mlsanity.com/paper/2106.03774/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [Presence-Only Geographical Priors for Fine-Grained Image Classification](https://mlsanity.com/paper/1906.05272/) (2019)
- [The iWildCam 2020 Competition Dataset](https://mlsanity.com/paper/2004.10340/) (2020)
- [Hierarchical Image Classification using Entailment Cone Embeddings](https://mlsanity.com/paper/2004.03459/) (2020)
- [Geo-Aware Networks for Fine-Grained Recognition](https://mlsanity.com/paper/1906.01737/) (2019)
- [Crop mapping from image time series: deep learning with multi-scale label hierarchies](https://mlsanity.com/paper/2102.08820/) (2021)
- [Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental Learning](https://mlsanity.com/paper/1802.05800/) (2018)
- [Improving Image Classification with Location Context](https://mlsanity.com/paper/1505.03873/) (2015)
- [Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding](https://mlsanity.com/paper/1808.04505/) (2018)
- [Country-wide high-resolution vegetation height mapping with Sentinel-2](https://mlsanity.com/paper/1904.13270/) (2019)

## Built on by (most cited first, 4 of 4)

- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [Active Learning-Based Species Range Estimation](https://mlsanity.com/paper/2311.02061/) (2023)
- [Taxonomy-Aware Evaluation of Vision-Language Models](https://mlsanity.com/paper/2504.05457/) (2025)
- [Automatic Fused Multimodal Deep Learning for Plant Identification](https://mlsanity.com/paper/2406.01455/) (2024)

## Abstract (arXiv)

Automatic identification of plant specimens from amateur photographs could improve species range maps, thus supporting ecosystems research as well as conservation efforts. However, classifying plant specimens based on image data alone is challenging: some species exhibit large variations in visual appearance, while at the same time different species are often visually similar; additionally, species observations follow a highly imbalanced, long-tailed distribution due to differences in abundance as well as observer biases. On the other hand, most species observations are accompanied by side information about the spatial, temporal and ecological context. Moreover, biological species are not an unordered list of classes but embedded in a hierarchical taxonomic structure. We propose a multimodal deep learning model that takes into account these additional cues in a unified framework. Our Digital Taxonomist is able to identify plant species in photographs better than a classifier trained on the image content alone, the performance gained is over 6 percent points in terms of accuracy.