# Detection and Annotation of Plant Organs from Digitized Herbarium Scans using Deep Learning

arXiv: https://arxiv.org/abs/2007.13106 · HTML: https://mlsanity.com/paper/2007.13106/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [From human experts to machines: An LLM supported approach to ontology and knowledge graph construction](https://mlsanity.com/paper/2403.08345/) (2024)
- [SIM-Net: A Multimodal Fusion Network Using Inferred 3D Object Shape Point Clouds from RGB Images for 2D Classification](https://mlsanity.com/paper/2506.18683/) (2025)
- [Application of Computer Vision and Machine Learning for Digitized Herbarium Specimens: A Systematic Literature Review](https://mlsanity.com/paper/2104.08732/) (2021)
- [Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention](https://mlsanity.com/paper/2106.02141/) (2021)
- [AT-ViT: Area-Targeted Multi-View Vision Transformer with Cross-Attention and Multi-Scale Patching for Plant Trait Recognition in Herbarium Images](https://mlsanity.com/paper/2608.21067/) (2026)

## Abstract (arXiv)

As herbarium specimens are increasingly becoming digitized and accessible in online repositories, advanced computer vision techniques are being used to extract information from them. The presence of certain plant organs on herbarium sheets is useful information in various scientific contexts and automatic recognition of these organs will help mobilize such information. In our study we use deep learning to detect plant organs on digitized herbarium specimens with Faster R-CNN. For our experiment we manually annotated hundreds of herbarium scans with thousands of bounding boxes for six types of plant organs and used them for training and evaluating the plant organ detection model. The model worked particularly well on leaves and stems, while flowers were also present in large numbers in the sheets, but not equally well recognized.