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Development of a Portable Low-Cost Multispectral Sensor Integrated with IoT and Machine Learning for Classifying Honey Types

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Publications AS7265X Sensor Honey Classification Internet of Things Machine Learning Spectroscopy JEMIT
Table of Contents

Publication Details
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Attribute Details
Authors Muhammad, R., Isroni, Wisesa, T. P., & Syahputra, T. S.
Journal / Publisher Journal of Energy, Material, and Instrumentation Technology (JEMIT)
Volume & Issue Vol. 6, No. 3, pp. 143–153 (2025)
DOI https://doi.org/10.23960/jemit.343
Official Article Link Read Paper at JEMIT
Google Scholar Scholar Citation Page

Official Abstract
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“Accurate honey type authentication is a significant challenge for small-scale producers, as conventional methods are often costly and impractical. This study aims to design and develop a low-cost honey classification prototype by integrating the AS7265X multispectral sensor with Internet of Things (IoT) technology and machine learning. Spectral data from 18 channels of various Indonesian honey types were acquired using the AS7265X sensor and analyzed exploratively using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The data were then normalized and used to train Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) classification models. An ESP32-based IoT system was developed for real-time monitoring and cloud data storage. The results demonstrate that AS7265X spectral data effectively differentiate honey types, with the ANN model achieving 94.05% accuracy, supported by a responsive IoT system (1–2 seconds) for monitoring and centralized storage. This prototype shows potential as a practical, rapid, accurate, and efficient honey authentication solution for various stakeholders.”

Keywords: AS7265X Sensor, Honey classification, Internet of Things, Machine Learning, Spectroscopy.

Technical Performance & Key Metrics
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  • Sensing Hardware: 18-Channel AS7265X Multispectral Optical Sensor covering UV-Visible-Near Infrared (UV-Vis-NIR) bands.
  • Embedded Controller & IoT: ESP32 microcontroller with real-time wireless telemetry (1–2 seconds response latency) and centralized cloud storage.
  • Exploratory Data Analysis: Dimensionality reduction and clustering via Principal Component Analysis (PCA) & Linear Discriminant Analysis (LDA).
  • Classification Algorithms: Evaluated Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM).
  • Top Accuracy: The Artificial Neural Network (ANN) model achieved the highest classification accuracy at 94.05%.

Recommended Citation # 

@article{Muhammad2025Honey,
  author  = {Muhammad, Riki and Isroni and Wisesa, T. P. and Syahputra, T. S.},
  title   = {Development of a Portable Low-Cost Multispectral Sensor Integrated with IoT and Machine Learning for Classifying Honey Types},
  journal = {Journal of Energy, Material, and Instrumentation Technology},
  volume  = {6},
  number  = {3},
  pages   = {143--153},
  year    = {2025},
  doi     = {10.23960/jemit.343},
  url     = {https://doi.org/10.23960/jemit.343}
}