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Honey Adulteration Detection through Hyperspectral Imaging and Machine Learning

Using Machine Learning technology on spectral features extracted through hyperspectral imaging to de

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Supervised by: Dr. Maryam Mahsal Khan • Posted on: 23 May, 2024

Objectives

Main objective of the project are: (1) To automate the process of honey identification. (2) To automate the process of detecting adulteration in honey. (3) Integration of multiple ML models onto one front-end web interface.

Socio-Economic Benefit

The purity and authenticity of honey are paramount for ensuring consumer trust and maintaining the integrity of the honey industry. SDG3: GoodHealth and WellBeing

Methodologies

Through the use of python libraries such as sklearn for machine learning model, and a Flask API, we were able to form a fully automated and integrated system that is both a honey type and quality detector.

Outcome

A fully automated and integrated honey identification and adulteration system that can predict quality of honey and its associated honey type in real-time through the use of hyperspectral features.

Project Team Members

Registration# Name
CU-1176-2020 Hazrat Usman
CU-1782-2020 Anna Amjad

PROJECT GALLERY

Honey Adulteration Detection through Hyperspectral Imaging and Machine Learning

23 May, 2024 08:07 pm

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Honey Adulteration Detection through Hyperspectral Imaging and Machine Learning

23 May, 2024 08:07 pm

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Honey Adulteration Detection through Hyperspectral Imaging and Machine Learning

23 May, 2024 08:07 pm

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