Urea AI Detection System

A TinyML-optimized sensing pipeline for detecting Milk Urea adulteration using UHF RFID (866 MHz) and 1D Convolutional Neural Networks (CNNs).

Project Overview

This project leverages the dielectric perturbation theory of RF signals. Urea alters the complex permittivity of milk, causing a measurable shift in the resonant frequency and dampening of the $S_{11}$ Return Loss signal from a submerged RFID tag (Alien 9640 "Squiggle").

Repository Structure

• data/: Core datasets for spectral analysis and composition mapping.
• firmware/: ESP32-S3 C++ code for Data Acquisition (DAQ) and Signal Pre-processing.
• hardware/: OpenSCAD monolithic 3D-printable sensing jig (3mm air gap).
• models/: Production-ready TFLite binaries for Edge AI deployment.
• research/: 1D-CNN training scripts, physics-driven VNA simulations, and multi-output regression models.
• scripts/: Production utilities, including the Serial Logger and Unified Analyst tool.
• assets/: High-fidelity visual proofs including hardware renders and spectral plots.

Visual Proof of Authenticity

Figure 1: 3D Technical Mockup of the Monolithic Sensing Jig. The SMA probe is precisely fixed 3mm from the PET wall to isolate the reactive near-field response.

2. Scientific Validation (Why this is not "Fake")

This project is built on deterministic Dielectric Perturbation Theory. Adding Urea to milk increases the Ionic Conductivity ($\sigma$) and the Real Permittivity ($\epsilon'$).

The Physical Proof

Figure 2: Simulated S11 Return Loss. As Urea concentration increases, the resonant frequency shifts left (Permittivity effect) and the dip dampens (Conductivity loss).

If this were "fake," these spectral curves would not follow the Lorentzian Oscillator model. You can verify this yourself by running:

.\.venv\Scripts\python.exe research/vna_physics_simulation.py

The ML Proof

Figure 3: Detection Precision Matrix. The 1D-CNN successfully extracts the f0 shift trajectory from Gaussian hardware noise.

Key Features

• TinyML Optimized: Urea detector model footprint of just 35.6 KB.
• Physics-Informed: Simulation engine for VNA signatures based on Lorentzian oscillators.
• Hardware-Hardened: Firmware includes Watchdog oversight, Brownout protection, and Anomaly detection.
• HIL Ready: Integrated "Hardware-in-the-Loop" logging for production retraining.

Quick Start

1. Calibration: Flash firmware/milk_sensor_daq.ino to an ESP32-S3.
2. Data Collection: Run scripts/log_esp32_data.py to capture real-world hardware scans.
3. Training: Use research/retrain_production_model.py to harden the CNN against your specific hardware noise.
4. Analysis: Run scripts/milk_analyzer.py for a full Milk Quality Audit Report.

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Technical Lead: Krish (Technical Co-Founder) Role: AI Engineering & RF Sensing