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Summary

This study introduces a multi-sensor Internet of Things (IoT) system for smart agriculture, utilizing AI-powered sensors and data analytics to optimize crop yields and farm efficiency while minimizing environmental impact.

Keywords:
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Area of Science:

  • Agricultural Science
  • Environmental Science
  • Computer Science

Background:

  • Growing global population necessitates increased food production and agricultural efficiency.
  • Environmental concerns require sustainable farming practices to limit pollution.
  • Smart agriculture technologies offer solutions for optimizing resource management.

Purpose of the Study:

  • To describe a multi-sensor Internet of Things (IoT) system for smart agriculture.
  • To detail the integration of an AI-based gas identification sensor.
  • To present an analytics and decision support system with farmer feedback and a social trust index.

Main Methods:

  • Development of a multi-sensor IoT system including soil and air probes.
  • Integration of an innovative Artificial Intelligence (AI) based gas identification sensor.
  • Implementation of an analytics and decision support system with a farmer feedback loop and social trust index.

Main Results:

  • The system enables data-driven farming recommendations.
  • Enhanced sensor integration and AI capabilities improve agricultural monitoring.
  • The decision support system, augmented by farmer feedback, increases reliability.

Conclusions:

  • The developed IoT system is a key enabler for smart agriculture.
  • AI-powered sensors and data analytics optimize yields and efficiency.
  • The system contributes to sustainable food production with reduced environmental impact.