Abstract
Citizen science is essential for nationwide ecological surveys of species distribution. While the accuracy of the information collected by beginner participants is not guaranteed, it is important to develop an automated system to assist species identification. Deep learning techniques for image recognition have been successfully applied in many fields and may contribute to species identification. However, deep learning techniques have not been utilized in ecological surveys of citizen science, because they require the collection of a large number of images, which is time-consuming and labor-intensive. To counter these issues, we propose a simple and effective strategy to construct species identification systems using fewer images. As an example, we collected 4,571 images of 204 species of Japanese dragonflies and damselflies from open-access websites (i.e., web scraping) and scanned 4,005 images from books and specimens for species identification. In addition, we obtained field occurrence records (i.e., range of distribution) of all species of dragonflies and damselflies from the National Biodiversity Center, Japan. Using the images and records, we developed a species identification system for Japanese dragonflies and damselflies. We validated that the accuracy of the species identification system was improved by combining web-scraped and scanned images; the top-1 accuracy of the system was 0.324 when trained using only web-scraped images, whereas it improved to 0.546 when trained using both web-scraped and scanned images. In addition, the combination of images and field occurrence records further improved the top-1 accuracy to 0.668. The values of top-3 accuracy under the three conditions were 0.565, 0.768, and 0.873, respectively. Thus, combining images with field occurrence records markedly improved the accuracy of the species identification system. The strategy of species identification proposed in this study can be applied to any group of organisms. Furthermore, it has the potential to strike a balance between continuously recruiting beginner participants and updating the data accuracy of citizen science.
Highlights
Monitoring biodiversity is essential to evaluate the status of global ecosystems, as biodiversity is an indicator of climate change, environmental pollution, overexploitation of resources, invasive species, and natural disasters
We found that some images of S. frequens were correctly identified by the combining model these images were incorrectly predicted as S. striolatum, S. vulgatum, and S. depressiusculum by the image-based model; some images of O. albistylum were correctly identified by the combining model these images were incorrectly predicted as O. glaucum, O. poecilops, and O. sabina by the image-based model
A large number of images are required to calibrate a large number of parameters in a deep learning system (Perez and Wang, 2017; Shahinfar et al, 2020)
Summary
Monitoring biodiversity is essential to evaluate the status of global ecosystems, as biodiversity is an indicator of climate change, environmental pollution, overexploitation of resources, invasive species, and natural disasters (van Klink et al, 2020; Hallmann et al, 2021). Basic data to quantify recent degradation in biodiversity are insufficient. Surveillance of bioindicators, such as dragonflies, frogs, and birds, is the first step toward the methodical quantification of biodiversity because these species are well-known, and their conservation is a priority (Paoletti, 1999; Kadoya and Washitani, 2007; Kadoya et al, 2009; Parmar et al, 2016; Zaghloul et al, 2020). In the 1990s, the Ministry of Environment of Japan conducted a comprehensive national survey of wellknown animal groups, including odonates, in collaboration with more than 300 donate specialists throughout Japan to compile their distribution records (National Biodiversity Center of Japan, Ministry of the Environment, Japan, Tokyo, 2002). No similar biodiversity surveys have been conducted since the 1990s, possibly because of insufficient budget for nationwide surveys
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