FlorID – A nationwide identification service for plants from photos and habitat information

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Philipp Brun , Lucienne de Witte , Manuel Richard Popp , Damaris Zurell , Dirk Nikolaus Karger , Patrice Descombes , Riccardo de Lutio , Jan Dirk Wegner , Christophe Bornand , Stefan Eggenberg , Tasko Olevski , Niklaus E. Zimmermann
{"title":"FlorID – A nationwide identification service for plants from photos and habitat information","authors":"Philipp Brun ,&nbsp;Lucienne de Witte ,&nbsp;Manuel Richard Popp ,&nbsp;Damaris Zurell ,&nbsp;Dirk Nikolaus Karger ,&nbsp;Patrice Descombes ,&nbsp;Riccardo de Lutio ,&nbsp;Jan Dirk Wegner ,&nbsp;Christophe Bornand ,&nbsp;Stefan Eggenberg ,&nbsp;Tasko Olevski ,&nbsp;Niklaus E. Zimmermann","doi":"10.1016/j.envsoft.2025.106402","DOIUrl":null,"url":null,"abstract":"<div><div>Citizen science has become key to biodiversity monitoring but critically depends on accurate quality control that is scalable and tailored to the focal region. We developed FlorID, a free-to-use identification service for all native and many non-native plants of Switzerland. FlorID can identify &gt;3000 species, using vision transformers trained on 1.5M photos, and ecological predictions from multilayer perceptrons, trained on 6.7M occurrence observations and 20 high-resolution environmental variables. Embedded in a free-to-use application programming interface, FlorID can be accessed directly, via webservice, and via FlorApp smartphone application. If multiple images and spatiotemporal location are available, FlorID correctly identifies 93% of field observations and has a top-5 accuracy of 99%. Ecological predictions boost identification success especially for native species with distinct distributions. By evaluating information on appearance and fine-grained ecology, FlorID is a blueprint for similar solutions targeting different taxa or regions, and a basis for developments like automated community inventories.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106402"},"PeriodicalIF":4.8000,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Modelling & Software","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1364815225000866","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Citizen science has become key to biodiversity monitoring but critically depends on accurate quality control that is scalable and tailored to the focal region. We developed FlorID, a free-to-use identification service for all native and many non-native plants of Switzerland. FlorID can identify >3000 species, using vision transformers trained on 1.5M photos, and ecological predictions from multilayer perceptrons, trained on 6.7M occurrence observations and 20 high-resolution environmental variables. Embedded in a free-to-use application programming interface, FlorID can be accessed directly, via webservice, and via FlorApp smartphone application. If multiple images and spatiotemporal location are available, FlorID correctly identifies 93% of field observations and has a top-5 accuracy of 99%. Ecological predictions boost identification success especially for native species with distinct distributions. By evaluating information on appearance and fine-grained ecology, FlorID is a blueprint for similar solutions targeting different taxa or regions, and a basis for developments like automated community inventories.

Abstract Image

FlorID -一个全国性的植物识别服务,从照片和栖息地信息
公民科学已经成为生物多样性监测的关键,但关键取决于精确的质量控制,这种控制是可扩展的,并且适合重点地区。我们开发了FlorID,这是一个免费使用的识别服务,可以识别瑞士所有本地和许多非本地植物。FlorID可以识别3000个物种,使用在150万张照片上训练的视觉转换器,以及在6.7万次发生观测和20个高分辨率环境变量上训练的多层感知器的生态预测。嵌入在一个免费使用的应用程序编程接口,FlorID可以直接访问,通过网络服务,并通过FlorApp智能手机应用程序。如果有多个图像和时空位置可用,FlorID可以正确识别93%的现场观测结果,前5名的准确率为99%。生态预测提高了识别的成功率,特别是对具有不同分布的本地物种。通过评估外观和细粒度生态的信息,FlorID是针对不同分类群或地区的类似解决方案的蓝图,也是自动化社区清单等开发的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信