Behavior Prediction of Vespa mandarinia based on Convolutional Neural Networks

Shihao Wu

doi:10.54097/fx3btx22

Authors

Shihao Wu

DOI:

https://doi.org/10.54097/fx3btx22

Keywords:

Component, Convolutional Neural Networks, Poisson Distribution, Vespa Mandarinia

Abstract

Vespa mandarinia poses a significant threat to honey bees and beekeepers. This paper aims to accurately predict the presence and behavior of Vespa mandarinia to enable effective control strategies. We develop an "Asian hornet queen prediction model" to forecast the dispersal path and number of queens, identifying their concentration in Bellingham, Washington, and Bellingham, Canada. Using a convolutional neural network, we achieve 96.13% accuracy in identifying Vespa mandarinia images. Analyzing confidence levels of unprocessed and unverified labels reveals a significant number of high-confidence samples. Incorporating human control factors into the model, we find 60% human intervention to be most effective in reducing the number of Asian hornet queens. Ultimately, our research highlights the potential for anthropological or ecological measures to eliminate Vespa mandarinia populations, aiding in resource allocation for proactive management.

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References

M. J. Fisher and A. P. Marshall, “Understanding descriptive statistics,” Australian Critical Care, vol. 22, no. 2, pp. 93–97, May 2009, doi: 10.1016/j.aucc.2008.11.003.

X. Chu, I. F. Ilyas, S. Krishnan, and J. Wang, “Data Cleaning: Overview and Emerging Challenges,” in Proceedings of the 2016 International Conference on Management of Data, San Francisco California USA: ACM, Jun. 2016, pp. 2201–2206. doi: 10.1145/2882903.2912574.

H. S. Bakouch, M. Kachour, and S. Nadarajah, “An extended Poisson distribution,” Communications in Statistics - Theory and Methods, vol. 45, no. 22, pp. 6746–6764, Nov. 2016, doi: 10.1080/03610926.2014.967587.

D. Johnston, Random Number Generators—Principles and Practices: A Guide for Engineers and Programmers. De Gruyter, 2018. doi: 10.1515/9781501506062.

L. Xu, J. He, and Y. Hu, “Early diabetes risk prediction based on deep learning methods,” in 2021 4th international conference on pattern recognition and artificial intelligence (PRAI), 2021, pp. 282–286.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278–2324, Nov. 1998, doi: 10.1109/ 5. 726791.

M. Lin and B. Zhao, “The study of molecular ratio trend prediction of electrolytic tank based on deep learning,” in 2022 IEEE 5th advanced information management, communicates, electronic and automation control conference (IMCEC), 2022, pp. 1457–1460.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3431–3440.

S. Zhang, H. Tong, J. Xu, and R. Maciejewski, “Graph convolutional networks: a comprehensive review,” Computational Social Networks, vol. 6, no. 1, pp. 1–23, 2019, doi: 10.1186/s40649-019-0069-y.

I. Bello, B. Zoph, V. Vasudevan, and Q. V. Le, “Neural optimizer search with reinforcement learning,” in International Conference on Machine Learning, PMLR, 2017, pp. 459–468.