The surveillance of mosquito populations is paramount in the fight against mosquito-borne diseases that affect millions of people every year. Evaluating the efficiency of mitigation methods requires extensive and long-term surveys which can be costly and time consuming. The recent development of optical sensors give access to alternative methods for entomological monitoring but require efficient classification algorithms to be successful. In this contribution, supervised machine learning algorithms such as Linear Discriminant Analysis, Decision Trees, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes are compared for the identification of mosquitoes through optical signals. Based on predictor variables derived from the wing beat frequency and optical cross section of mosquitoes, these algorithms were trained to perform different classification tasks: the identification of species, sex and/or gravidity of mosquitoes present in New Jersey, USA. Results shows that the most polyvalent machine learning algorithm for mosquito identification is Support Vector Machine that performs on average over all tasks between 0.65 and 7.3% better than other algorithms. Moreover, Support Vector Machine is the algorithm that best performed for the most complex tasks, more than 2% above the second best, it is therefore the most suited for real-world study where several species of mosquito can be expected at a single location. A close second is Linear Discriminant Analysis that is only outperformed by Support Vector Machine by 0.65% over all tasks and is the most performant when studying mosquito gravidity. Finally, Decision Trees algorithm has reached almost perfection in identifying the sex of a single mosquito species with 99.9% accuracy which is 1.3% more than the second best performing algorithm on this task. These results demonstrate that optical sensors, coupled with machine learning, can be a viable alternative or complementary methodology for the monitoring of mosquito populations. Furthermore, this methodology can perform non-intrusive, automatic, time-resolved measurements of insect population dynamics over extended periods of time without the need for laboratory analysis of captured specimens as in most traditional survey methods.

在防治蚊媒疾病方面，对蚊虫种群的监视至关重要，这种疾病每年影响数百万人。评估缓解方法的效率需要进行广泛而长期的调查，这可能既昂贵又耗时。光学传感器的最新发展为昆虫学监测提供了替代方法，但需要有效的分类算法才能成功。在此贡献中，比较了监督的机器学习算法，例如线性判别分析，决策树，支持向量机，K最近邻和朴素贝叶斯，以通过光信号识别蚊子。根据从机翼拍频和蚊子的光学横截面得出的预测变量，这些算法经过训练可以执行不同的分类任务：识别美国新泽西州存在的蚊子的种类，性别和/或引力。结果表明，用于识别蚊子的最广泛的机器学习算法是支持向量机，它在所有任务上的平均执行效率比其他算法高0.65％至7.3％。此外，支持向量机是最能完成最复杂任务的算法，比次优任务高出2％以上，因此，它最适合在现实世界中研究，在一个地方可能会遇到几种蚊子。紧随其后的是线性判别分析，在所有任务中，线性判别分析的性能仅比支持向量机高出0.65％，并且在研究蚊子的柔软度时性能最高。最后，决策树算法在识别单个蚊子的性别方面已达到近乎完美的状态，准确度为99.9％，比在该任务上表现第二好的算法高1.3％。这些结果表明，将光学传感器与机器学习相结合，可以成为监测蚊子种群的可行替代方法或补充方法。此外，该方法可以像大多数传统调查方法一样，在较长的时间内执行非侵入式，自动的，时间分辨的昆虫种群动态测量，而无需对捕获的标本进行实验室分析。结合机器学习，可以成为监测蚊子种群的可行替代方法或补充方法。此外，该方法可以像大多数传统调查方法一样，在较长的时间内执行非侵入式，自动的，时间分辨的昆虫种群动态测量，而无需对捕获的标本进行实验室分析。结合机器学习，可以成为监测蚊子种群的可行替代方法或补充方法。此外，该方法可以像大多数传统调查方法一样，在较长的时间内执行非侵入式，自动的，时间分辨的昆虫种群动态测量，而无需对捕获的标本进行实验室分析。