ArticleSensors (Basel, Switzerland)2019
A Deep Learning-Based Automatic Mosquito Sensing and Control System for Urban Mosquito Habitats.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- How technologies are driving digital innovation in mosquito surveillance systems: a global scoping review over the past decade.BMJ public health · 2026Article
- Identifying disease vector images in the Americas in the age of artificial intelligence.Revista da Sociedade Brasileira de Medicina Tropical · 2025Review
- Automatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system.Scientific reports · 2023Article
- An IoT-based smart mosquito trap system embedded with real-time mosquito image processing by neural networks for mosquito surveillance.Frontiers in bioengineering and biotechnology · 2023Article
- AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot.Sensors (Basel, Switzerland) · 2022Article
- Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks.Scientific reports · 2021Article
- Deep learning approaches for challenging species and gender identification of mosquito vectors.Scientific reports · 2021Article
- Deep learning identification for citizen science surveillance of tiger mosquitoes.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Mosquito control is important as mosquitoes are extremely harmful pests that spread various infectious diseases. In this research, we present the preliminary results of an automated system that detects the presence of mosquitoes via image processing using multiple deep learning networks. The Fully Convolutional Network (FCN) and neural network-based regression demonstrated an accuracy of 84%. Meanwhile, the single image classifier demonstrated an accuracy of only 52%. The overall processing time also decreased from 4.64 to 2.47 s compared to the conventional classifying network. After detection, a larvicide made from toxic protein crystals of the
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.