Dengue, Chikungunya and Zika viruses cause dangerous infections in tropical and subtropical regions throughout the world. The World Health Organization estimates that one out of every three persons in the entire human population is in danger of contracting one of these diseases from a single mosquito bite. Currently, these viral infections are not preventable by vaccines and there is not a direct treatment that can effectively diminish the viral infection, which causes a wide range of pathologies, including severe joint pain, internal blood loss, permanent neurological damage in unborn children and even death. Due to this grim scenario, the best and maybe the only line of defense against these diseases is the effective surveillance, control and suppression of the mosquitoes that transmit these viruses: Aedes aegypti and Aedes albopictus. In this paper, we present a complete solution that is capable of identifying the Aedes aegypti and Aedes albopictus mosquito in the larval stage, which is easily disposable, restricted to water bodies, and incapable of transmitting diseases according to the Centers for Disease Control and Prevention (CDC). Our proposal is based on deep neural networks (DNN) that effectively recognize larval samples with an accuracy of 94.19%, which is better than other state-of-the-art automatic methods. Additionally, the capabilities of our proposed DNN allow us to automatically crop the region of interest (ROI) with an accuracy of 92.85% and then automatically classify the region as Aedes positive or Aedes negative, without further human intervention and in less than a second, accelerating the response time for biological control from days to seconds. Our proposal includes hardware designs that allow inexpensive implementation, making it suitable for isolated communities, underdeveloped countries, and rural or urban areas.

登革热，基孔肯雅热和寨卡病毒在全世界的热带和亚热带地区引起危险的感染。世界卫生组织估计，在整个人口中，每三人中就有一个人有被蚊子叮咬感染其中一种疾病的危险。目前，这些病毒感染无法用疫苗预防，没有直接的治疗方法可以有效地减少病毒感染，从而导致多种病理，包括严重的关节痛，内部失血，未出生的孩子甚至是永久性神经系统损害。死亡。由于这种严峻的形势，对这些疾病的最佳，也许也是唯一的防御措施是对传播这些病毒的蚊子进行有效的监视，控制和抑制：埃及伊蚊和白纹伊蚊。在本文中，我们提供了一个完整的解决方案，该解决方案能够根据疾病控制与预防中心的识别幼虫阶段的埃及伊蚊和白纹伊蚊，这些易处置，易受水体限制并且不能传播疾病。 （CDC）。我们的建议基于深度神经网络（DNN），该神经网络能够以94.19％的准确度有效识别幼虫样本，这比其他最新的自动方法要好。此外，我们提议的DNN的功能使我们能够以92.85％的精度自动裁剪感兴趣的区域（ROI），然后自动将该区域分类为伊蚊或伊蚊。阴性反应，无需进一步的人工干预，并且不到一秒钟，可将生物学控制的响应时间从几天缩短到几秒钟。我们的建议包括允许廉价实施的硬件设计，使其适合于偏远社区，欠发达国家以及农村或城市地区。