Asian Giant Hornet Control based on Image Processing and Biological Dispersal
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The Asian giant hornet (AGH) appeared in Washington State appears to have a potential danger of bioinvasion. Washington State has collected public photos and videos of detected insects for verification and further investigation. In this paper, we analyze AGH using data analysis,statistics, discrete mathematics, and deep learning techniques to process the data to controlAGH spreading.First, we visualize the geographical distribution of insects in Washington State. Then we investigate insect populations to varying months of the year and different days of a month.Third, we employ wavelet analysis to examine the periodic spread of AGH. Fourth, we apply ordinary differential equations to examine AGH numbers at the different natural growthrate and reaction speed and output the potential propagation coefficient. Next, we leverage cellular automaton combined with the potential propagation coefficient to simulate the geographical spread under changing potential propagation. To update the model, we use delayed differential equations to simulate human intervention. We use the time difference between detection time and submission time to determine the unit of time to delay time. After that, we construct a lightweight CNN called SqueezeNet and assess its classification performance. We then relate several non-reference image quality metrics, including NIQE, image gradient, entropy, contrast, and TOPSIS to judge the cause of misclassification. Furthermore, we build a Random Forest classifier to identify positive and negative samples based on image qualities only. We also display the feature importance and conduct an error analysis. Besides, we present sensitivity analysis to verify the robustness of our models. Finally, we show the strengths and weaknesses of our model and derives the conclusions.
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