Edge AI & Graphene Biosensors for Soil Microbiome Analytics
Smart Sensors at the Edge: Real-Time Soil Intelligence Powered by Graphene & TinyML.
About This Course
This 3-day interdisciplinary workshop integrates nanotechnology, edge AI, and sustainability analytics to build next-generation soil intelligence systems. Participants will explore how graphene Field-Effect Transistor (gFET) biosensors detect soil nitrates and microbial activity, transform raw electrical signals into meaningful biological insights, and deploy lightweight AI models directly onto farm-level microcontrollers.
The program concludes with a sustainability lens—quantifying whether smart farming technologies genuinely reduce environmental impact using comparative Life Cycle Assessment (LCA).
Designed for researchers, agritech innovators, and sustainability professionals, this workshop bridges nanomaterials → AI → environmental validation in a single applied workflow.
Aim
To equip participants with practical skills to analyze graphene biosensor data, deploy TinyML models on edge devices for soil health classification, and evaluate environmental sustainability through Life Cycle Assessment (LCA).
Workshop Objectives
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Understand the working mechanism of graphene FET biosensors for soil monitoring.
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Preprocess and denoise nano-sensor electrical data using digital signal processing techniques.
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Train lightweight AI models for soil health classification.
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Quantize and deploy models using TinyML frameworks (TensorFlow Lite for Microcontrollers).
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Evaluate the environmental trade-offs of smart farming technologies using LCA modeling.
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Calculate sustainability break-even points for graphene sensor deployment.
Workshop Structure
📅 Day 1 — Graphene Sensor Data & Pre-processing (Led by Mr. Indraneel)
- Focus: Understanding the raw data generated by nanotechnology
- Mechanism of Graphene Field-Effect Transistors (gFETs): how functionalized graphene binds to soil nitrates or microbial enzymes, altering electrical resistance
- Hands-On:
- Importing an open-source dataset of raw gFET sensor readings (current-voltage sweep data over time) into Python
- Identifying electrical “signatures” of different soil pH levels and nitrogen concentrations
- Applying digital signal processing (e.g., moving average filters) to clean inherent electronic noise from nano-sensor data
📅 Day 2 — TinyML & Edge AI for Soil Health (Led by Mrs. Gurpreet Kaur)
- Focus: Building AI small enough to live on a farm sensor
- Philosophy of Edge Computing and introduction to TinyML; model quantization (32-bit → 8-bit models for microcontrollers)
- Hands-On:
- Training a lightweight Random Forest and a quantized Neural Network using cleaned sensor data to classify soil health status
- Using TensorFlow Lite for Microcontrollers (tflite) to compress trained AI models
- Evaluating accuracy trade-offs versus improvements in processing speed and energy efficiency
📅 Day 3 — LCA & Sustainable Deployment (Led jointly by Mr. Indraneel & Mrs. Gurpreet)
- Focus: Proving the environmental viability of your technology
- The paradox of “Smart Farming”: evaluating whether sensor manufacturing impacts outweigh agricultural waste reduction benefits
- Hands-On:
- Building a comparative Life Cycle Assessment (LCA) model in Python
- Simulating carbon footprint of graphene sensor manufacturing versus carbon savings from AI-optimized fertilizer reduction
- Calculating the environmental “break-even” point for sustainable deployment
Who Should Enrol?
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Researchers in nanotechnology, biosensors, agriculture, AI, or sustainability.
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Agritech developers and IoT engineers.
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Basic Python familiarity recommended; no prior TinyML or LCA experience required.
Important Dates
Registration Ends
02/24/2026
IST 4 : 30 PM
Workshop Dates
02/24/2026 – 02/26/2026
IST 5 : 30 PM
Workshop Outcomes
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Interpret graphene biosensor electrical signals for soil microbiome indicators.
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Deploy TinyML models for real-time soil health classification.
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Quantify trade-offs between model performance and hardware efficiency.
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Build a Python-based LCA model to evaluate smart farming sustainability.
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Design a research-ready prototype pipeline: Sensor → Edge AI → Sustainability Validation.
Fee Structure
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
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