The Three-River-Source region is a highly significant natural reserve in
China that harbors a plethora of untamed botanical resources. To meet the
practical requirements of botanical research and intelligent plant management,
we construct a large-scale dataset for Plant detection in the
Three-River-Source region (PTRS). This dataset comprises 6965 high-resolution
images of 2160*3840 pixels, captured by diverse sensors and platforms, and
featuring objects of varying shapes and sizes. Subsequently, a team of
botanical image interpretation experts annotated these images with 21 commonly
occurring object categories. The fully annotated PTRS images contain 122, 300
instances of plant leaves, each labeled by a horizontal rectangle. The PTRS
presents us with challenges such as dense occlusion, varying leaf resolutions,
and high feature similarity among plants, prompting us to develop a novel
object detection network named PlantDet. This network employs a window-based
efficient self-attention module (ST block) to generate robust feature
representation at multiple scales, improving the detection efficiency for small
and densely-occluded objects. Our experimental results validate the efficacy of
our proposed plant detection benchmark, with a precision of 88.1%, a mean
average precision (mAP) of 77.6%, and a higher recall compared to the baseline.
Additionally, our method effectively overcomes the issue of missing small
objects. We intend to share our data and code with interested parties to
advance further research in this field.

本文构建了植物检测的大规模数据集，使用窗口自注意力模块的目标检测网络 PlantDet，有效提高小目标的检测效率，达到 88.1% 的精度和 77.6% 的平均精度（mAP），旨在为进一步的植物研究提供数据和代码。

三江源地区植物检测基准

PlantDet: A benchmark for Plant Detection in the Three-Rivers-Source  Region

Herbarium sheets present a unique view of the world's botanical history,
evolution, and diversity. This makes them an all-important data source for
botanical research. With the increased digitisation of herbaria worldwide and
the advances in the fine-grained classification domain that can facilitate
automatic identification of herbarium specimens, there are a lot of
opportunities for supporting research in this field. However, existing datasets
are either too small, or not diverse enough, in terms of represented taxa,
geographic distribution or host institutions. Furthermore, aggregating multiple
datasets is difficult as taxa exist under a multitude of different names and
the taxonomy requires alignment to a common reference. We present the Herbarium
Half-Earth dataset, the largest and most diverse dataset of herbarium specimens
to date for automatic taxon recognition.

本研究介绍了全球最大、最多样化的标本收藏数据集 Herbarium Half-Earth，旨在通过数字化技术协助自动识别和对分类进行研究，此数据集对于支持植物学研究具有非常重要的意义。

2021 植物标本库半地球挑战数据集

The Herbarium 2021 Half-Earth Challenge Dataset

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.

本研究旨在开发一种自动化方法，利用计算机视觉和深度学习技术帮助专家识别标本以加速植物学研究。为此，提供了一个由专家标记的干燥标本图像数据集用于挑战性场景的自动化识别的发展。