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  1. Home
  2. Integrating Artificial Intelligence In Environmental Monitoring: A Paradigm Shift In Data-driven Sustainability.
  1. Home
  2. Integrating Artificial Intelligence In Environmental Monitoring: A Paradigm Shift In Data-driven Sustainability.

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Integrating Artificial Intelligence in Environmental Monitoring: A Paradigm Shift in Data-Driven Sustainability.

Ufondu Maryann Afoma1, Shilpy Singh1, Abhishek Kumar Mishra1

  • 1Department of Biotechnology & Microbiology, School of Sciences, Noida International University, Greater Noida, Gautam Budh Nagar, Uttar Pradesh, India.

Ecohealth
|August 28, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence (AI) revolutionizes environmental monitoring by automating data collection and analysis for better ecosystem understanding. AI tools enhance biodiversity efforts and sustainable management, overcoming traditional method limitations.

Keywords:
artificial intelligenceconservation and biodiversity analysisdisaster forecastingenvironmental monitoringpollution detectionpredictive modelingsustainability

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Area of Science:

  • Environmental Science
  • Ecology
  • Computer Science

Background:

  • Traditional environmental monitoring methods are often resource-intensive and difficult to scale.
  • Growing human impact necessitates advanced, efficient monitoring solutions.
  • Artificial intelligence (AI) offers automated data collection, real-time analysis, and predictive capabilities.

Purpose of the Study:

  • To review advancements in AI for environmental monitoring.
  • To highlight AI applications in air/water quality, climate, and biodiversity.
  • To discuss challenges and strategies for AI implementation in ecological stewardship.

Main Methods:

  • Review of current AI techniques, including machine learning (ML) and deep learning (DL).
  • Exploration of AI applications using drone imagery, camera traps, and GPS data.
  • Emphasis on emerging technologies like explainable AI (XAI), edge computing, and the Internet of Things (IoT).
  • Main Results:

    • AI enables automated data collection, real-time analysis, and predictive modeling for environmental monitoring.
    • AI applications span species identification, habitat assessment, wildlife tracking, and anti-poaching efforts.
    • Emerging technologies like XAI, edge computing, and IoT enhance AI model transparency and reduce costs.

    Conclusions:

    • AI holds transformative potential for sustainable environmental management and ecological stewardship.
    • Addressing challenges like data quality, computational demands, and model interpretability is crucial.
    • Interdisciplinary strategies, ethical frameworks, and policy alignment are key for successful AI integration.