Going deeper in the automated identification of Herbarium specimens.
Going deeper in the automated identification of Herbarium specimens.
复制标题
DOI：
10.1186/s12862-017-1014-z
复制
发表时间：
2017-08-11
影响因子：
3.4
通讯作者：
Joly A
中科院分区：
文献类型：
--
作者：
Carranza-Rojas J;Goeau H;Bonnet P;Mata-Montero E;Joly A
数百个植物标本收藏积累了几个世纪以来的宝贵遗产和植物知识。最近的举措启动了雄心勃勃的保存计划，将这些信息数字化，并通过门户网站向植物学家和普通公众提供。然而，数以千计的标本在物种水平上仍未被识别，而许多标本应根据更新的分类学知识进行审查和更新。这些注释和修订要求植物学家在合理的时间内进行大量不切实际的工作。计算机视觉和机器学习方法应用于植物标本是很有前途的，但与通过叶片扫描或田间植物图片进行自动物种识别相比，仍然没有得到很好的研究。在这项工作中，我们建议研究和评估标本图像的准确性，以潜在地利用深度学习技术进行物种识别。此外，我们建议研究植物标本表与田间植物照片的组合是否在准确性方面相关，最后，我们探索是否可以使用来自一个地区的标本馆图像来与其他物种一起转移学习到另一个地区；例如，在收集的数据方面代表不足的地区。据我们所知，这是第一次使用深度学习来分析包含数千个草本植物物种的大数据集。结果表明深度学习在植物种类识别上的潜力，特别是通过对来自不同草本植物的不同数据集的训练和测试。这可能会导致创建半自动化系统，甚至是全自动化系统，以帮助分类学家和专家进行注释、分类和修订工作。
Hundreds of herbarium collections have accumulated a valuable heritage and knowledge of plants over several centuries. Recent initiatives started ambitious preservation plans to digitize this information and make it available to botanists and the general public through web portals. However, thousands of sheets are still unidentified at the species level while numerous sheets should be reviewed and updated following more recent taxonomic knowledge. These annotations and revisions require an unrealistic amount of work for botanists to carry out in a reasonable time. Computer vision and machine learning approaches applied to herbarium sheets are promising but are still not well studied compared to automated species identification from leaf scans or pictures of plants in the field. In this work, we propose to study and evaluate the accuracy with which herbarium images can be potentially exploited for species identification with deep learning technology. In addition, we propose to study if the combination of herbarium sheets with photos of plants in the field is relevant in terms of accuracy, and finally, we explore if herbarium images from one region that has one specific flora can be used to do transfer learning to another region with other species; for example, on a region under-represented in terms of collected data. This is, to our knowledge, the first study that uses deep learning to analyze a big dataset with thousands of species from herbaria. Results show the potential of Deep Learning on herbarium species identification, particularly by training and testing across different datasets from different herbaria. This could potentially lead to the creation of a semi, or even fully automated system to help taxonomists and experts with their annotation, classification, and revision works.
登录
查看更多内容
影响因子：
19.5
作者：
Russakovsky, Olga;Deng, Jia;Fei-Fei, Li
通讯作者：
Fei-Fei, Li
影响因子：
3.3
作者：
Casanova, Dalcimar;de Mesquita Sa Junior, Jarbas Joaci;Bruno, Odemir Martinez
通讯作者：
Bruno, Odemir Martinez
影响因子：
2.4
作者：
McDonald, Mark A.;Hildebrand, John A.;Wiggins, Sean M.
通讯作者：
Wiggins, Sean M.
自动物种鉴定：为什么不？
DOI：
10.1098/rstb.2003.1442
发表时间：
2004-04-29
影响因子：
6.3
作者：
Gaston, KJ;O'Neill, MA
通讯作者：
O'Neill, MA
影响因子：
3.4
作者：
Corney, David P. A.;Clark, Jonathan Y.;Wilkin, Paul
通讯作者：
Wilkin, Paul