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Wearable Edge AI Applications for Ecological Environments.

Mateus C Silva1, Jonathan C F da Silva1, Saul Delabrida1

  • 1Computer Science Department, Federal University of Ouro Preto, Ouro Preto 35400-000, Brazil.

Sensors (Basel, Switzerland)
|August 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces wearable edge artificial intelligence (AI) for forest management, achieving 90% accuracy in detecting diseased leaves in the field. The proposed hardware/software co-design enhances ecological monitoring capabilities.

Keywords:
(multipurpose) wearable edge AIcomputer visionedge computingembedded systemswearable computing

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Area of Science:

  • Ecological informatics
  • Computer science
  • Forestry science

Background:

  • Forest management requires advanced tools for impact assessment and monitoring.
  • Limited connectivity in forests necessitates edge-computing solutions for data processing.
  • Wearable technology offers potential for in-situ data collection and analysis.

Purpose of the Study:

  • To evaluate the feasibility of wearable edge artificial intelligence (AI) in forest environments.
  • To propose a novel hardware/software co-design process for wearable edge AI applications.
  • To assess the performance of wearable edge AI for ecological tasks, such as disease detection.

Main Methods:

  • Developed a hardware/software co-design approach for wearable edge AI.
  • Utilized wireless personal and body area networks for edge AI application deployment.
  • Conducted a case study involving wearable devices for forest disease detection and epicenter identification.

Main Results:

  • Achieved approximately 90% accuracy in classifying diseased leaves in the field using the proposed wearable edge AI technique.
  • The system demonstrated a quality factor of 0.95 across three devices for performing edge AI tasks.
  • Successfully detected a forest disease epicenter with a spatial offset of approximately 0.5 meters.

Conclusions:

  • The proposed wearable edge AI concept and co-design pattern are effective for forest environment applications.
  • Wearable edge AI systems can achieve high accuracy in ecological monitoring tasks like disease detection.
  • The study validates the practical application of wearable edge AI in addressing forest management challenges.