Abstract
Citizen scientists have the potential to play an important role in combating mosquito-borne diseases such as malaria, dengue fever, Zika, and West Nile virus. While public health officials do not have the labor or resources to collect adequate spatial and temporal coverage of where, when, and what types of mosquitoes are found, these officials may be hesitant to include citizen science data because they are not confident of the data. The GLOBE Observer Mosquito Habitat Mapper (GO MHM) app was launched in 2017, and data are being collected from around the world. As part of the app, photos of mosquito larvae are submitted. Computer vision software provides a means of assisting in the validation of the images for correctness. We have developed an image recognition prototype that includes image collection, training of image classifiers, specimen recognition, and expert validation and analytics. Photos of known mosquito larvae are used to train a Deep Learning classification model. Citizen scientists submit photos, and the system conducts an analysis that indicates the probability of correct mosquito identification. New and better classification models can be trained from new data received from citizen scientists. Experts can have access to the recognition results to assess data quality and devise ways to improve the quality of GO MHM data, while public health officials can use the data to assist in the mitigation of disease outbreaks. Recommendations are made for prototype improvements.
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Notes
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Co-author Boger participates in Global Mosquito Alert, a UN sponsored consortium.
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Personal communication with the Global Mosquito Alert Consortium.
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References
Barré, P., Stöver, B. C., Müller, K. F., & Steinhage, V. (2017). LeafNet: A computer vision system for automatic plant species identification. Ecological Informatics, 40, 50–56.
Berger-Wolf, T. Y., Rubenstein, D. I., Stewart, C. V., Holmberg, J. A., Parham, J., Menon, S., Crall, J., Van Oast, J., Kiciman, E., & Joppa, L. (2017). Wildbook: Crowdsourcing, computer vision, and data science for conservation. arXiv preprint arXiv:1710.08880.
Bonney, R., Cooper, C. B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K. V., & Shirk, J. (2009). Citizen science: A developing tool for expanding science knowledge and scientific literacy. Bioscience, 59(11), 977–984.
Bonney, R., Shirk, J. L., Phillips, T. B., Wiggins, A., Ballard, H. L., Miller-Rushing, A. J., & Parrish, J. K. (2014). Next steps for citizen science. Science, 343(6178), 1436–1437.
Bonney, R., Phillips, T. B., Ballard, H. L., & Enck, J. W. (2016). Can citizen science enhance public understanding of science? Public Understanding of Science, 25(1), 2–16.
Chandler, M., See, L., Copas, K., Bonde, A. M., López, B. C., Danielsen, F., Legind, J. K., Masinde, S., Miller-Rushing, A. J., Newman, G., & Rosemartin, A. (2017). Contribution of citizen science towards international biodiversity monitoring. Biological Conservation, 213, 280–294.
Dhami, D. S., Leake, D., & Natarajan, S. (2017, March). Knowledge-based morphological classification of galaxies from vision features. In Workshops at the thirty-first AAAI conference on artificial intelligence.
Dieleman, S., Willett, K. W., & Dambre, J. (2015). Rotation-invariant convolutional neural networks for galaxy morphology prediction. Monthly Notices of the Royal Astronomical Society, 450(2), 1441–1459.
Dollár, P., & Zitnick, C. L. (2013). Structured forests for fast edge detection. In Proceedings of the IEEE international conference on computer vision (pp. 1841–1848).
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2014, January). Decaf: A deep convolutional activation feature for generic visual recognition. In International conference on machine learning (pp. 647–655).
Follett, R., & Strezov, V. (2015). An analysis of citizen science based research: usage and publication patterns. PLoS One, 10(11), e0143687.
Haklay, M. (2013). Citizen Science and volunteered geographic information: Overview and typology of participation (pp. 105–122). Dordrecht: Springer Netherlands.
Haywood, B. K., Parrish, J. K., & Dolliver, J. (2016). Place-based and data-rich citizen science as a precursor for conservation action. Conservation Biology, 30(3), 476–486.
Horns, J. J., Adler, F. R., & Şekercioğlu, Ç. H. (2018). Using opportunistic citizen science data to estimate avian population trends. Biological Conservation, 221, 151–159.
Hunter, J., Alabri, A., & van Ingen, C. (2013). Assessing the quality and trustworthiness of citizen science data. Concurrency and Computation: Practice and Experience, 25(4), 454–466.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014, November). Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 675–678). ACM.
Jordan, R. C., Gray, S. A., Howe, D. V., Brooks, W. R., & Ehrenfeld, J. G. (2011). Knowledge gain and behavioral change in citizen-science programs. Conservation Biology, 25(6), 1148–1154.
Keshavan, A., Yeatman, J., & Rokem, A. (2018). Combining citizen science and deep learning to amplify expertise in neuroimaging. BioRxiv, 363382.
Kress, W. J., Garcia-Robledo, C., Soares, J. V., Jacobs, D., Wilson, K., Lopez, I. C., & Belhumeur, P. N. (2018). Citizen science and climate change: Mapping the range expansions of native and exotic plants with the mobile app leafsnap. Bioscience, 68(5), 348–358.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105).
Land-Zandstra, A. M., Devilee, J. L., Snik, F., Buurmeijer, F., & van den Broek, J. M. (2016). Citizen science on a smartphone: Participants’ motivations and learning. Public Understanding of Science, 25(1), 45–60.
Lawrence, A. (2006). ‘No personal motive?’ Volunteers, biodiversity, and false dichotomies of participation. Ethics, Place & Environment: A Journal of Philosophy & Geography, 9, 279–298.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.
Leta, S., Beyene, T. J., De Clercq, E. M., Amenu, K., Kraemer, M. U., & Revie, C. W. (2018). Global risk mapping for major diseases transmitted by Aedes aegypti and Aedes albopictus. International Journal of Infectious Diseases, 67, 25–35.
Mattos, A. B., Herrmann, R., Shigeno, K. K., & Feris, R. S. (2014). A mission-oriented citizen science platform for efficient flower classification based on combination of feature descriptors. In EMR@ ICMR (pp. 45–52).
Mazumdar, S., Wrigley, S., & Ciravegna, F. (2017, January 19). Citizen science and crowdsourcing for earth observations: An analysis of stakeholder opinions on the present and future. Remote Sensing, 9(1), 87.
McKinley, D. C., Miller-Rushing, A. J., Ballard, H., Bonney, R., Brown, H., Evans, D. M., French, R. A., Parrish, J. K., Phillips, T. B., Ryan, S. F., & Shanley, L. A. (2015). Investing in citizen science can improve natural resource management and environmental protection. Issues in Ecology, 2015(19), 1–27.
McKinley, D. C., Miller-Rushing, A. J., Ballard, H. L., Bonney, R., Brown, H., Cook-Patton, S. C., Evans, D. M., French, R. A., Parrish, J. K., Phillips, T. B., & Ryan, S. F. (2017). Citizen science can improve conservation science, natural resource management, and environmental protection. Biological Conservation, 208, 15–28.
Munoz, J. P., Boger, R., Dexter, S. Low, R., & Li, J. (2018). Image recognition of disease-carrying insects: A system for combating infectious diseases using image classification techniques and citizen science. In HICSS.
Nguyen, A., Yosinski, J., & Clune, J. (2015). Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 427–436).
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., & Berg, A. C. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211–252.
Sauermann, H., & Franzoni, C. (2015). Crowd science user contribution patterns and their implications. Proceedings of the National Academy of Sciences, 112(3), 679–684.
Schaller, R. R. (1997). Moore’s law: Past, present and future. IEEE Spectrum, 34(6), 52–59.
Sein, M., Henfridsson, O., Purao, S., Rossi, M., & Lindgren, R. (2011). Action design research. https://aisel.aisnet.org/misq/vol35/iss1/5/
Shirk, J. L., Ballard, H. L., Wilderman, C. C., Phillips, T., Wiggins, A., Jordan, R., McCallie, E., Minarchek, M., Lewenstein, B. V., Krasny, M. E., & Bonney, R. (2012). Public participation in scientific research: A framework for deliberate design. Ecology and Society, 17(2), 29.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929–1958.
Von Alan, R. H., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.
Wiggins, A., & Crowston, K. (2011, January). From conservation to crowdsourcing: A typology of citizen science. In 2011 44th Hawaii international conference on system sciences (pp. 1–10). IEEE.
Willi, M., Pitman, R. T., Cardoso, A. W., Locke, C., Swanson, A., Boyer, A., Veldthuis, M., & Fortson, L. (2018). Identifying animal species in camera trap images using deep learning and citizen science. Methods in Ecology and Evolution. https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13099
World Health Organization. (2014). A global brief on vector-borne diseases (No. WHO/DCO/WHD/2014.1). World Health Organization.
Zitnick, C. L., & Dollár, P.. (2014, September). Edge boxes: Locating object proposals from edges. In European conference on computer vision (pp. 391–405). Cham: Springer.
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Muñoz, J.P., Boger, R., Dexter, S., Low, R. (2020). Mosquitoes and Public Health: Improving Data Validation of Citizen Science Contributions Using Computer Vision. In: Wickramasinghe, N., Bodendorf, F. (eds) Delivering Superior Health and Wellness Management with IoT and Analytics. Healthcare Delivery in the Information Age. Springer, Cham. https://doi.org/10.1007/978-3-030-17347-0_23
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