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Sensor Data in Animal Sciences

ADP31306

Sensor data is increasingly used by commercial livestock farmers, feeding companies, breeding companies, and the scientific community. In this course, sensors in animal production systems will be discussed, including their use in science and for commercial purposes, their constraints, and future potential. The course consists of lectures, ICT practicals, and a case study in groups. Lectures will be used to teach about sensors and basic methods for looking at and working with data. Practicals will give the students hands-on experience with the data generated by sensors, using methods such as visualizing, cleaning, filtering, aggregating, processing, combining and analysing data. Emphasis will be put on accelerometer data, feeding station data, and tracking data. Last, a case study in small groups will enable students to apply what they have learned in the lectures and ICT practicals and go through the full process of data handling, processing, interpretation and reporting. In this project, the link with scientific and/or commercial relevance in animal science will be made by the students. This course prepares students in animal science for sensor data analysis and interpretation in their future career.

Leerresultaten

After successful completion of this course students are expected to be able to:

  • discuss main sensors used in animal production systems, and their possibilities and constraints;
  • explain how and what kind of data is generated by feeding stations, location sensors (cameras) and accelerometers in livestock systems;
  • apply techniques to visualize and analyze basic features of sensor data;
  • engineer new features and create new hypotheses based on sensor data;
  • interpret processed results in the context of the livestock system;
  • report on data processing, analysis and results.

Onderwijsmethode en toetsing

  • group presentation (20%);
  • written group report (40%);
  • individual exam (40%).

Veronderstelde voorkennis

Assumed Knowledge:
This course assumes basic working knowledge on mathematics and statistics, and familiarity with computer programming.

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