Project Overview #
| Attribute | Details |
|---|---|
| Domain | Machine Learning & Applied Optical Instrumentation |
| Tech Stack | Python (Scikit-Learn, Pandas, NumPy), ESP32, Multispectral Array |
| Core Goal | Automated Real-Time Honey Authenticity & Botanical Origin Classification |
System Architecture & Machine Learning Pipeline #
This engineering initiative solves critical quality verification bottlenecks in the food industry by coupling low-cost optical sensing hardware with robust Python machine learning pipelines.
Technical Workflow: #
- Optical Data Acquisition: Multispectral sensors capture detailed spectral reflectance responses across UV, visible, and near-infrared (NIR) wavelengths.
- Data Processing Pipeline: Automated feature extraction, spectral baseline normalization, and noise filtering handled via Pandas and NumPy.
- Supervised Classification Models: Comparative evaluation of Support Vector Machine (SVM), Random Forest, and Artificial Neural Network (ANN) classifiers, surpassing 95% classification accuracy on test samples.