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Biodiversity Information Science and Standards : Conference Abstract
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Congtian Lin‡, Jiangning Wang‡, Liqiang Ji‡
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Corresponding author: Congtian Lin (linct@ioz.ac.cn), Liqiang Ji (ji@ioz.ac.cn)
Received: 09 Jun 2019 | Published: 13 Jun 2019
© 2019 Congtian Lin, Jiangning Wang, Liqiang Ji
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Lin C, Wang J, Ji L (2019) Notes of Life: A platform for recording species observations driven by artificial intelligence. Biodiversity Information Science and Standards 3: e37010. https://doi.org/10.3897/biss.3.37010
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Abstract
Biodiversity research is stepping into a big data era with the rapid increase in the abundance of biodiversity data, especially the large number of species images. It has been a new trend and hot topic on how to utilize artificial intelligence to mine big biodiversity data to support wildlife observation and recognition. In this research, we integrate large numbers of species images, including higher plants, birds and insects, and use a state-of-the-art image deep learning technique to train species auto-recognition models. Currently, we get a model that can recognize more than 900 Chinese birds with top 1 accuracy 81% and top 5 accuracy 95% (top n accuracy means the probability that the correct answer presents in top n predicted results), and more models are coming soon. Based on these models, we developed a platform named Notes of Life (NOL, http://nol.especies.cn), which includes a website and a mobile application (app) for assisting biological scientists and citizen scientists to recognize and record wildlife. Users can upload their observation records and images of wildlife through our mobile app while they are investigating in the wild. The website is used for bulk data uploading and management. Species images can be classified by taxon-specific, plug-in recognition models that speed up the process of identification. There is an expert module in NOL where citizen scientists can work interactively with information provided by biological scientists, and post a species image identification request to experts when they cannot recognize the species by themselves or from models. The expert module is for improving the quality of citizen science data, and it is a supplement of the disadvantage of species auto-recognition models. Above all, NOL embraces the idea that scientific research supports citizen science and citizen science gives feedback to science, and of finding a sustainable way to collect increasingly more reliable data for biodiversity research.
Keywords
biodiversity data, deep learning, species recognition, citizen science
Presenting author
Congtian Lin
Presented at
Bioidiversity_Next 2019
Acknowledgements
We would like to thank The Strategic Priority Research Program of the Chinese Academy of Sciences and The National R&D Infrastructure and Facility Development Program of China, "Fundamental Science Data Sharing Platform" (DKA2017-12-02-10), who funded the project. We would like to thank Min Li, Bo Cai and Gang Song, who are the partners of NOL.
Funding program
The Strategic Priority Research Program of the Chinese Academy of Sciences and The National R&D Infrastructure and Facility Development Program of China
Grant title
The Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19050202) and Fundamental Science Data Sharing Platform (Grant No. DKA2017-12-02-10)
Hosting institution
The Institute of Zoology, Chinese Academy of Sciences