AI in Plastic Lifecycle Analysis: Detection, Tracking, and Mitigation

From Detection to Prevention: Harnessing AI to Track, Predict, and Combat Plastic Pollution.

Mode: Virtual / Online
Type: Mentor Based
Level: Moderate
Duration: 3 Days (60-90 minutes each day)
Starts: 27 April 2026
Time: 4:30 PM IST

About This Course

Plastic pollution is a global environmental crisis, with millions of tons of plastic waste affecting marine ecosystems, wildlife, and human health. This 3-day hands-on workshop combines computer vision, geospatial AI, and machine learning to equip participants with advanced techniques for analyzing plastic pollution sources, tracking its movement, and forecasting future trends.

Through practical exercises, participants will use satellite imagery, UAV data, and spectroscopy to identify plastic hotspots, model microplastic transport pathways, and develop predictive models to forecast pollution levels. They will also explore how AI can optimize waste management and inform policy decisions in the context of a circular economy.

Aim

To provide participants with the skills and tools to use AI and machine learning for detecting plastic pollution sources, modeling its movement, and predicting future trends to support mitigation strategies and policy-making.

Workshop Objectives

By the end of this workshop, participants will be able to:

Use computer vision and geospatial AI to detect and map plastic pollution hotspots from satellite and UAV imagery.
Implement machine learning models for classifying microplastics based on spectral data (e.g., Raman spectroscopy).
Model plastic transport in ecosystems, incorporating environmental variables like wind, tides, and river currents.
Develop temporal forecasting models to predict seasonal pollution trends using AI-based time-series analysis.
Understand the role of AI in the circular economy and apply predictive analytics to waste management and recycling optimization.

Workshop Structure

Day 1 AI for Plastic Pollution Analytics

Introduction to Plastic Pollutants, Master the use of Computer Vision and Geospatial AI to identify plastic accumulation hotspots from the sky to the shore
Remote Sensing (Satellites & Drones): AI models, Computer Vision (Shipborne Cameras): AI systems
Satellite Intelligence: Leveraging Sentinel-2 and UAV imagery for automated plastic debris mapping
Neural Architectures: Implementing YOLO and CNN frameworks for real-time object detection in marine environments
The MDPI Advantage: Synthesizing open-access research data to pinpoint urban vs. industrial leakage points
Micro plastic Analysis: 3D AI-powered systems (e.g., MP3D),
Hands-on Lab: “The Polymer Classifier” – Build a Python-based classifier in Google Colab to identify microplastic types (PE, PET, PP) using Raman Spectroscopy datasets

Day 2 Dynamics of Micro plastics & Transport Pathways

Move from observation to action by forecasting future pollution trends and optimizing mitigation strategies
Temporal Forecasting: Using Recurrent Neural Networks (RNNs) and LSTMs to predict seasonal pollution surges
Transport Modeling: Utilizing AI-driven Lagrangian particle tracking to simulate plastic flow in riverine system
Microplastic Fingerprinting: Using Random Forests and Support Vector Machines (SVM) to classify polymers via spectroscopy data
Environmental Correlation: Feature engineering techniques to link wind, tide, and weather patterns to plastic drift
Hands-on Lab: “The Drift Navigator” – Develop a Random Forest regression model in Google Colab to correlate environmental variables (currents, wind, topography) with plastic drift trajectories

Day 3 Predictive Analytics & Circular Economy Strategy

Pathway Prediction & Modelling: Employing deep learning, reinforcement learning,
Microplastic Analytics: Developing tools that analyze oceanic factor
Industrial Sorting & Recycling, Data-Driven Environmental Solutions, Geospatial Analysis
Digital Twins: AI’s role in creating virtual models of waste-to-energy pathways and sorting facility optimization
Policy & ESG Impact: Generating predictive risk maps to guide government intervention and corporate sustainability goals
Hands-on Lab: “The Trend Forecaster” – Develop an LSTM time-series model in Google Colab to predict plastic accumulation rates based on historical environmental variables

Who Should Enrol?

Environmental scientists, policy makers, and sustainability experts.
Researchers and data scientists in the fields of marine science, pollution, and waste management.
PhD scholars, postdocs, and industry professionals with an interest in AI applications for environmental sustainability.
Basic familiarity with Python and machine learning is recommended, but not required.

Important Dates

Registration Ends

April 27, 2026
IST 3:30 PM

Workshop Dates

April 27, 2026 – April 29, 2026
IST 4:30 PM

Workshop Outcomes

By the end of the workshop, participants will have:

Learned how to use AI and computer vision for detecting plastic pollution from satellite and UAV imagery.
Built and deployed machine learning models for classifying microplastics using spectral data.
Developed transport models to simulate plastic flow and drift in ecosystems.
Created temporal forecasting models to predict future plastic pollution trends.
Applied AI for optimizing circular economy strategies in waste management and plastic recycling.

Meet Your Mentor(s)

Umapriya

Student

₹2499 | $75

Ph.D. Scholar / Researcher

₹3499 | $85

Academician / Faculty

₹4499 | $95

Industry Professional

₹6499 | $115

What You’ll Gain

Live & recorded sessions
e-Certificate upon completion
Post-workshop query support
Hands-on learning experience

Need Help?

We’re here for you!

(+91) 120-4781-217

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