Thesis subject

Food security prediction using Artificial Intelligence: A systems approach (MSc)

Food security is a critical issue affecting millions of people worldwide. In recent years, there has been growing interest in using open access data and artificial intelligence (AI) to predict food security and identify strategies to address it. This MSc project aims to use a systems approach to develop a predictive model that can forecast food security outcomes based on various socio-economic and environmental factors.

Short description

The project will involve collecting and analyzing data from various sources, including publicly available databases, remote sensing data, and surveys. The student will use AI techniques such as machine learning algorithms to identify patterns and correlations between the data and food security outcomes. They will also develop a systems model that can integrate the various factors influencing food security, such as climate change, land use, crop yields, and socio-economic factors.


Objectives

  1. Conduct a comprehensive literature review on AI techniques, food security, and prediction models.
  2. Develop a predictive model that can forecast food security outcomes based on various socio-economic and environmental factors.
  3. Identify the most significant factors influencing food security and their relative importance.
  4. Develop a systems model that can integrate the various factors influencing food security, such as climate change, land use, crop yields, and socio-economic factors.
  5. Provide insights and recommendations on how to improve food security outcomes and reduce hunger.

    Tasks

    The work in this master thesis entails:

    • Literature review: Conduct a review of existing research studies, to identify relevant studies on food security 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 security outcomes. Perform feature selection and sensitivity analysis to identify the most significant factors influencing food security outcomes.
    • Results reporting and documentation: Prepare a comprehensive report summarizing the research methodology, results, and conclusions.


    Literature

    • Liu, Ningjing ; Bouzembrak, Yamine ; Bulk, Leonieke M. van den; Gavai, Anand ; Heuvel, Lukas J. van den; Marvin, Hans J.P. Automated food safety early warning system in the dairy supply chain using machine learning (2022) Food Control 136.
    • HJP Marvin, Y Bouzembrak (2020). A system approach towards prediction of food safety hazards: Impact of climate and agrichemical use on the occurrence of food safety hazards, Agricultural Systems 178, 102760. https://doi.org/10.1016/j.agsy.2019.102760
    • Bouzembrak, Yamine ,Marvin, Hans J.P. (2019). Impact of drivers of change, including climatic factors, on the occurrence of chemical food safety hazards in fruits and vegetables : A Bayesian Network approach. Food Control 97, p. 67 - 76. https://doi.org/10.1016/j.foodcont.2018.10.021


    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 security, early warning systems, systems approach

      Contact person(s)

      Dr. Yamine Bouzembrak (yamine.bouzembrak@wur.nl)
      Prof. Bedir Tekinerdogan (bedir.tekinerdogan@wur.nl)