A Deep Learning-Based Automatic Mosquito Sensing and Control System for Urban Mosquito Habitats

Sensors (Basel). 2019 Jun 21;19(12):2785. doi: 10.3390/s19122785.

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 Bacillus thuringiensis serotype israelensis bacteria was injected into static water to stop the proliferation of mosquitoes. This system demonstrates a higher efficiency than hunting adult mosquitos while avoiding damage to other insects.

Keywords: deep learning; drug spray; mosquito; urban habitat; vector control.

MeSH terms

  • Animals
  • Bacillus thuringiensis / chemistry
  • Bacterial Proteins / chemistry*
  • Biosensing Techniques
  • Deep Learning*
  • Ecosystem*
  • Image Processing, Computer-Assisted / methods
  • Mosquito Control / methods*
  • Neural Networks, Computer

Substances

  • Bacterial Proteins