Behavior Prediction of Vespa mandarinia based on Convolutional Neural Networks


  • Shihao Wu



Component, Convolutional Neural Networks, Poisson Distribution, Vespa Mandarinia


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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How to Cite

Wu, S. (2024). Behavior Prediction of Vespa mandarinia based on Convolutional Neural Networks. Frontiers in Computing and Intelligent Systems, 7(1), 14-17.