The Herbarium Challenge 2019 Dataset

TL;DR

This paper introduces the Herbarium Challenge 2019 Dataset, a specialized collection of herbarium sheet images aimed at advancing automated plant specimen identification using computer vision and deep learning.

Contribution

It provides a new, expert-labeled dataset of dried herbarium sheets to support development of automated identification methods for botanical research.

Findings

Dataset enables training of deep learning models for herbarium specimen identification

Facilitates research on automated analysis of dried botanical specimens

Addresses challenges posed by dried specimens unlike wild photos

Abstract

Herbarium sheets are invaluable for botanical research, and considerable time and effort is spent by experts to label and identify specimens on them. In view of recent advances in computer vision and deep learning, developing an automated approach to help experts identify specimens could significantly accelerate research in this area. Whereas most existing botanical datasets comprise photos of specimens in the wild, herbarium sheets exhibit dried specimens, which poses new challenges. We present a challenge dataset of herbarium sheet images labeled by experts, with the intent of facilitating the development of automated identification techniques for this challenging scenario.