# Application of Computer Vision and Machine Learning for Digitized Herbarium Specimens: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2104.08732 · HTML: https://mlsanity.com/paper/2104.08732/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 155 references that are in the map.
- First posted on arXiv 18 Apr 2021.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Semi-Supervised Semantic Segmentation with Cross-Consistency Training](https://mlsanity.com/paper/2003.09005/) (2020)
- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [Fixing the train-test resolution discrepancy](https://mlsanity.com/paper/1906.06423/) (2019)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [Single-Stage Semantic Segmentation from Image Labels](https://mlsanity.com/paper/2005.08104/) (2020)
- [Weakly-Supervised Semantic Segmentation via Sub-category Exploration](https://mlsanity.com/paper/2008.01183/) (2020)
- [Knowledge Distillation Meets Self-Supervision](https://mlsanity.com/paper/2006.07114/) (2020)
- [Few-Shot Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.02536/) (2017)
- [A Survey on Instance Segmentation: State of the art](https://mlsanity.com/paper/2007.00047/) (2020)
- [Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes](https://mlsanity.com/paper/1611.08323/) (2016)
- [The Herbarium Challenge 2019 Dataset](https://mlsanity.com/paper/1906.05372/) (2019)
- [Mask Encoding for Single Shot Instance Segmentation](https://mlsanity.com/paper/2003.11712/) (2020)
- [Semi-Supervised Semantic Image Segmentation with Self-correcting Networks](https://mlsanity.com/paper/1811.07073/) (2018)
- [Deep Leaf Segmentation Using Synthetic Data](https://mlsanity.com/paper/1807.10931/) (2018)
- [Learning Cross-domain Generalizable Features by Representation Disentanglement](https://mlsanity.com/paper/2003.00321/) (2020)
- [Detection and Annotation of Plant Organs from Digitized Herbarium Scans using Deep Learning](https://mlsanity.com/paper/2007.13106/) (2020)

## Built on by (most cited first, 1 of 1)

- [Automated pipeline for herbarium label digitization](https://mlsanity.com/paper/2608.28676/) (2026)

## Abstract (arXiv)

Herbarium contains treasures of millions of specimens which have been preserved for several years for scientific studies. To speed up more scientific discoveries, a digitization of these specimens is currently on going to facilitate easy access and sharing of its data to a wider scientific community. Online digital repositories such as IDigBio and GBIF have already accumulated millions of specimen images yet to be explored. This presents a perfect time to automate and speed up more novel discoveries using machine learning and computer vision. In this study, a thorough analysis and comparison of more than 50 peer-reviewed studies which focus on application of computer vision and machine learning techniques to digitized herbarium specimen have been examined. The study categorizes different techniques and applications which have been commonly used and it also highlights existing challenges together with their possible solutions. It is our hope that the outcome of this study will serve as a strong foundation for beginners of the relevant field and will also shed more light for both computer science and ecology experts.