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| EAGER: Artificial Intelligence (AI) to accelerate plant species discovery | |
| 项目编号 | 2054684 |
| Damon Little | |
| 项目主持机构 | New York Botanical Garden |
| 开始日期 | 2021-05-15 |
| 结束日期 | 04/30/2023 |
| 英文摘要 | Herbaria are archival scientific collections of pressed and dried plant specimens. These collections contain an overwhelming number of un-named specimens, some of which may be new to science. There are approximately 400,000 known vascular plant species with an estimated 80,000 still to be discovered—many of which are likely already in herbarium collections. With the urgent threats of climate change we need new tools to quicken the pace of species discovery within these collections to better conserve species before they go extinct. A United Nations report indicates that more than one million species are at risk of extinction, and amid this dire prediction a recent estimate suggests plants are disappearing more quickly than animals. There are approximately 3,000 herbaria worldwide and they are massive repositories of plant diversity data: these collections not only represent a vast amount of plant diversity, but since herbarium collections include specimens dating back hundreds of years, they provide snapshots of plant diversity through time. Herbarium specimens not only maintain their morphological features but also include collection dates and locations. This information, multiplied by millions of plant collections, provides the framework for understanding plant diversity on a massive scale and learning how it has changed over time. Artificial Intelligence (AI) is a powerful tool that can vastly accelerate species discovery in herbaria. This project will provide training and professional development opportunities for a post-doctoral researcher. This project will develop a tool for rapid curation and identification of plant specimens from herbaria collections using AI: this easy-to-use online tool, iCurate, will be readily used by herbaria world-wide to automatically identify and curate herbarium specimens by inputting their own specimen images. This tool will be built from the wide range of digitized vascular plant specimens present in the GBIF and iDigBio databases, significantly reducing the time to identify specimens and allowing herbaria to curate plant groups for which they lack expertise. The iCurate tool will be optimized and expanded through a stepwise process that uses crowd-sourced AI models submitted to a series of online competitions. The competitions will use an ever- increasing number of species and images for model training eventually including all public vascular plant specimen images in GBIF and iDigBio. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. |
| 资助机构 | US-NSF |
| 项目经费 | $299,754.00 |
| 项目类型 | Standard Grant |
| 国家 | US |
| 语种 | 英语 |
| 文献类型 | 项目 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/212081 |
| 推荐引用方式 GB/T 7714 | Damon Little.EAGER: Artificial Intelligence (AI) to accelerate plant species discovery.2021. |
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