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- P-ISSN2765-2203
- E-ISSN2765-2211
- KCI Candidate
Abstract
In order to support biomimicry technology development, it is necessary to develop an omnidirectional service platform which can recommend principles of biomimicry and business ideas, providing experts’ networks and carrying out their relevant education and promotion on the ground of baseline data and application research materials related to biomimicry. This study was conducted to establish any probable plans for construction and utilization of the future open-platform which will collect and serve the technology of biomimicry. Accordingly, biological and ecological information databases were examined along with the appreciation of construction and management of major biomimicry DB, and, based on the materials from the interview of related experts, a customer journey map was schematized. Lastly, in order to suggest a mid-to-long-term target-model, the roles of a future biomimicry knowledge service-platform were determined along with the potential plans for its construction and management based on case analysis and customers’ needs.
Abstract
Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep- learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.