Thesis subject

Identify Behaviour Patterns in Chicken to Detect Feed Contamination

Note: This is a cooperative project between INF group and WFSR group of Wageningen University & Research

Internet of Things (IoT) is growing exponentially and can become an enormous source of information. However, little attention has been paid to its potential use in IoT in the food safety domain. Specifically, if the feed is contaminated with toxic elements, such as mycotoxins and pesticides, these compounds end up in muscle meat or eggs that are later used for human consumption.

In this project, we aim to explore possibilities to determine the contaminating condition of feed consumed by farm animals, such as chicken. To achieve this aim, this project will use devices, such as infrared cameras among others, to measure the environment conditions from a poultry farm.

The aim of this project is to explore various variables that can be measured in a poultry farm, to identify the behavior patterns of chickens when their feed is contaminated. The project will include the following tasks:

  • Identify variables that are related to contaminated feed in a farm environment.
  • Collect and fuse these sensing data together.
  • Build Machine Learning (ML) solutions to identify the internal patterns of the sensing data.
  • Use the ML model to test if we can identify contaminated feed when there is an abnormality in the pattern of sensing data.

The content above is an overall description of the project. The detailed research work of the project could be based on further discussion between supervisors and students.


    Requirements (optional)

    • Pyhon programming
    • Machine learning

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