Thesis subject

Artificial Intelligence and citizen science to combat food fraud (MSc)

Food fraud is a serious problem that may compromise the safety of the food products being sold on the market. Previous studies have shown that food fraud is associated with a large variety of food products and the fraud type may vary from deliberate changing of the food product to the manipulation of documents. It is therefore important that all actors within the food supply chain have methodologies and tools available to detect fraudulent products at an early stage so that preventative measures can be taken. This MSc project aims to use AI to develop a predictive model that can forecast food fraud based on various factors.

Short description

The project will involve collecting and analyzing data provided by citizens from various countries.  The student will use computer vision algorithms to identify the origin country of the food. The main focus of this study will be the bananas chain.


Objectives

  1. Conduct a literature review on AI techniques, food fraud, and citizen science.
  2. Identify, collect and preprocess the images of the different species of Banan, including literature and news articles and health-related websites.
  3. Explore different AI algorithms, including machine learning models for predicting the authenticity of the food.

Tasks

The work in this master thesis entails:

  • Literature review: Conduct a review of existing research studies, to identify relevant studies on food fraud prediction using open access data and AI techniques. This will provide a foundation of knowledge and identify research gaps.
  • Data collection and preparation: Identify relevant open access data sources and collect and preprocess the data.
  • AI models development: Use machine learning algorithms to develop a predictive model that can forecast food fraud.
  • Results reporting and documentation: Prepare a comprehensive report summarizing the research methodology, results, and conclusions.

Literature

  • Marvin, Hans J.P. ; Hoenderdaal, Wouter ; Gavai, Anand K. ; Mu, Wenjuan ; Bulk, Leonieke M. van den; Liu, Ningjing ; Frasso, Gianluca ; Ozen, Neris ; Elliott, Chris ; Manning, Louise ; Bouzembrak, Yamine (2022). Global media as an early warning tool for food fraud; an assessment of MedISys-FF. Food Control 137 .
  • Bouzembrak, Y. ; Steen, B. ; Neslo, R. ; Linge, J. ; Mojtahed, V. ; Marvin, H.J.P. (2018). Development of food fraud media monitoring system based on text mining, Food Control 93 . - p. 283 - 296.

Requirements

  • Courses: Programming in Python (INF-22306), Data Science Concepts (INF-34306) or Machine Learning (FTE-35306)
  • Required skills/knowledge: Food and health, Machine Learning

    Key words: Artificial Intelligence, food quality, food safety and health.

    Contact person(s)

    Yamine Bouzembrak (yamine.bouzembrak@wur.nl)

    Ayalew Kassahun (ayalew.kassahun@wur.nl)