Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY.
Department of Environmental and Earth Sciences, Columbia University, New York, NY.
J Med Entomol. 2020 Sep 7;57(5):1588-1595. doi: 10.1093/jme/tjaa078.
Aedes albopictus (Skuse), an invasive disease vector, poses a nuisance and public health threat to communities in the Northeastern United States. Climate change and ongoing adaptation are leading to range expansion of this mosquito into upstate New York and other northeastern states. Organized mosquito control can suppress populations, but it is time consuming, costly, and difficult as Ae. albopictus oviposits in small, artificial, water-holding containers. Unmanned aerial vehicles (UAVs), with centimeter-resolution imaging capabilities, can aid surveillance efforts. In this work, we show that a convolutional neural network trained on images from a UAV is able to detect Ae. albopictus habitat in suburban communities, and the number of containers successfully imaged by UAV predicted the number of containers positive for mosquito larvae per home. The neural network was able to identify some, but not all, potential habitat, with up to 67% precision and 40% recall, and can classify whole properties as positive or negative for larvae 80% of the time. This combined approach of UAV imaging and neutral network analysis has the potential to dramatically increase capacity for surveillance, increasing the reach and reducing the time necessary for conventional on-the-ground surveillance methods.
白纹伊蚊（Skuse）是一种入侵性疾病媒介，对美国东北部的社区造成滋扰和公共卫生威胁。气候变化和不断的适应导致这种蚊子向纽约州北部和其他东北部州扩展。有组织的蚊子控制可以抑制其种群数量，但由于白纹伊蚊在小而人工的蓄水容器中产卵，因此这种控制方式耗时、昂贵且困难。具有厘米级分辨率成像能力的无人机可以辅助监测工作。在这项工作中，我们表明，经过无人机图像训练的卷积神经网络能够检测到郊区社区的白纹伊蚊栖息地，并且无人机成功拍摄的容器数量可以预测每个家庭中带有蚊子幼虫的容器数量。该神经网络能够识别出一些但不是全部的潜在栖息地，其准确率高达 67%，召回率为 40%，并且能够在 80%的时间内将整个物业分类为幼虫阳性或阴性。这种结合无人机成像和神经网络分析的方法具有显著提高监测能力的潜力，可以扩大监测范围并减少传统地面监测方法所需的时间。
