IoT Product Engineering and Edge AI for AgriFood Tech: Low-Cost NIR Spectrometer for Biomarker Measurement
Introduction: The Challenge of Portable and Affordable Biomarker Screening
High-end spectrometers are expensive and confined to laboratories. Farmers and small labs required affordable, portable NIR solutions to measure biomarkers like creatinine in egg matrixes reliably for quality control and research. This required clever IoT product engineering to deliver a sensitive, low-cost smart infrastructure solution.
Solution Overview: Edge AI Embedded Systems for Chemometric Screening
EurthTech designed a bench and field-capable NIR spectrometer solution using a compact diode-array sensor (e.g., C12880MA-class). The system uses an integrating sphere interface for diffuse reflectance, where an AI-powered embedded system performs pre-processing and chemometric modeling locally. This Edge AI embedded system enables rapid screening of out-of-spec samples, speeding up decision loops on farms.
Technical Implementation: Embedded Systems Development and AI Engineering
The embedded systems development focused on a robust calibration strategy to overcome low-SNR limitations. This included stabilized LED illumination, temperature control, and applying Partial Least Squares Regression (PLSR) models with cross-validation. Pre-processing included Savitzky-Golay smoothing and MSC. We used instrument-specific transfer functions derived from reference panels, enabling the low-cost unit to approximate lab-grade screening, which is a key demonstration of our practical AI engineering solutions.
Results & Impact: AI Engineering and Operational ROI
In an initial dataset (n~400 eggs), PLSR models produced R^2~0.72 and RMSEP within acceptable screening thresholds. The device was reliable for screening (classifying out-of-spec samples with >85% sensitivity). This screening device reduced sample turnaround times from days to minutes and allowed early detection of feeding anomalies, demonstrating a high-ROI solution for agrifood quality control.
Scalability & GIS Context: We provided EMS-ready enclosures, simple UIs, and detailed calibration protocols for field use. The ability to monitor quality and detect anomalies across a geographically distributed farm network is a form of Predictive maintenance AI IoT applied to livestock. This technology forms a crucial quality layer within the broader Digital twin smart city or supply chain, integrating seamlessly with geospatial engineering services for farm management.