Dataset Information

Development of a camera trap for perching dragonflies: a new tool for freshwater environmental assessment.

ABSTRACT: Although dragonflies are excellent environmental indicators for monitoring terrestrial water ecosystems, automatic monitoring techniques using digital tools are limited. We designed a novel camera trapping system with an original dragonfly detector based on the hypothesis that perching dragonflies can be automatically detected using inexpensive and energy-saving photosensors built in a perch-like structure. A trial version of the camera trap was developed and evaluated in a case study targeting red dragonflies (Sympetrum spp.) in Japan. During an approximately 2-month period, the detector successfully detected Sympetrum dragonflies while using extremely low power consumption (less than 5 mW). Furthermore, a short-term field experiment using time-lapse cameras for validation at three locations indicated that the detection accuracy was sufficient for practical applications. The frequency of false positive detection ranged from 17 to 51 over an approximately 2-day period. The detection sensitivities were 0.67 and 1.0 at two locations, where a time-lapse camera confirmed that Sympetrum dragonflies perched on the trap more than once. However, the correspondence between the detection frequency by the camera trap and the abundance of Sympetrum dragonflies determined by field observations conducted in parallel was low when the dragonfly density was relatively high. Despite the potential for improvements in our camera trap and its application to the quantitative monitoring of dragonflies, the low cost and low power consumption of the detector make it a promising tool.

SUBMITTER: Yoshioka A

PROVIDER: S-EPMC7505062 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Development of a camera trap for perching dragonflies: a new tool for freshwater environmental assessment.

Yoshioka Akira A Shimizu Akira A Oguma Hiroyuki H Kumada Nao N Fukasawa Keita K Jingu Shoma S Kadoya Taku T

PeerJ 20200918

Although dragonflies are excellent environmental indicators for monitoring terrestrial water ecosystems, automatic monitoring techniques using digital tools are limited. We designed a novel camera trapping system with an original dragonfly detector based on the hypothesis that perching dragonflies can be automatically detected using inexpensive and energy-saving photosensors built in a perch-like structure. A trial version of the camera trap was developed and evaluated in a case study targeting ...[more]

PMID: 32999757

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Project description:Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best-practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open-grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data.

Project description:Wild animals are essential for ecosystem structuring and stability, and thus they are important for ecological research. Since most wild animals have high athletic or concealable abilities or both, it is used to be relatively difficult to acquire evidence of animal appearances before applications of camera traps in ecological researches. However, a single camera trap may produce thousands of animal images in a short period of time and inevitably ends up with millions of images requiring classification. Although there have been many methods developed for classifying camera trap images, almost all of them follow the pattern of a very deep convolutional neural network processing all camera trap images. Consequently, the corresponding surveillance area may need to be delicately controlled to match the network capability, and it may be difficult to expand the area in the future. In this study, we consider a scenario in which camera traps are grouped into independent clusters, and images produced by a cluster are processed by an edge device installed with a customized network. Accordingly, edge devices in this scenario may be highly heterogeneous due to cluster scales. Resultantly, networks popular in the classification of camera trap images may not be deployable for edge devices without modifications requiring the expertise which may be hard to obtain. This motivates us to automatize network design via neural architecture search for edge devices. However, the search may be costly due to the evaluations of candidate networks, and its results may be infeasible without considering the resource limits of edge devices. Accordingly, we propose a search method using regression trees to evaluate candidate networks to lower search costs, and candidate networks are built based on a meta-architecture automatically adjusted regarding to the resource limits. In experiments, the search consumes 6.5 hours to find a network applicable to the edge device Jetson X2. The found network is then trained on camera trap images through a workstation and tested on Jetson X2. The network achieves competitive accuracies compared with the automatically and the manually designed networks.

Dataset Information

Development of a camera trap for perching dragonflies: a new tool for freshwater environmental assessment.

Publications

Development of a camera trap for perching dragonflies: a new tool for freshwater environmental assessment.

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