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  • P-ISSN2765-2203
  • E-ISSN2765-2211

Bioimage Analyses Using Artificial Intelligence and Future Ecological Research and Education Prospects: A Case Study of the Cichlid Fishes from Lake Malawi Using Deep Learning

Proceedings of the National Institute of Ecology of the Republic of Korea, (P)2765-2203; (E)2765-2211
2022, v.3 no.2, pp.67-72
https://doi.org/10.22920/PNIE.2022.3.2.67
Deokjin Joo (Hashed)
Jungmin You (Research Institute of Ecoscience, Ewha Womans University)
Yong-Jin Won (Division of EcoScience, Ewha Womans University)
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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.

keywords
Animal detection Artificial intelligence Citizenscience Deep learning Education Machine learning
Submission Date
2021-10-20
Revised Date
2021-12-13
Accepted Date
2021-12-13

Proceedings of the National Institute of Ecology of the Republic of Korea