Current active course description (last updated 2025/26)
Precision Livestock Farming
HUS5002
Current active course description (last updated 2025/26)
Precision Livestock Farming
HUS5002
This course on Precision Livestock Farming (PLF) covers the principles, sensor technologies, data analytics, automation, and ethical considerations involved in PLF, with a focus on improving animal health, welfare and productivity.
This course provides an in-depth exploration of the technologies used in Precision Livestock Farming (PLF). Students will learn about the latest advancements in sensor technology, data analytics, and automation, and how these technologies are applied to improve animal health, welfare, and productivity. The course will include lectures, hands-on labs, and self-studies to provide a comprehensive understanding of PLF technologies.
Students must meet current admission requirements for the associated study programme.
Upon successful completion of this course, it is expected that the student has reached the following learning objectives:
Knowledge:
- Understand the fundamental principles and concepts of Precision Livestock Farming (PLF).
- Identify and describe various sensor technologies used in PLF, including their applications and integration.
- Explain the role of data analytics and big data in livestock farming.
- Understand the impact of environmental conditions on livestock and the technologies used for environmental monitoring.
- Comprehend the ethical and regulatory considerations in the implementation of PLF technologies.
Skills:
- Analyze and interpret data collected from PLF technologies.
- Integrate multiple PLF technologies into a cohesive and functional system.
- Conduct case studies and practical applications of PLF technologies in real-world scenarios.
- Evaluate and address ethical and regulatory challenges in PLF.
General competence:
- Demonstrate critical thinking and problem-solving skills in the context of PLF.
- Communicate effectively about PLF technologies and their applications to diverse audiences.
- Collaborate with peers and professionals to develop and implement PLF solutions.
In addition to the semester fee and curriculum literature, it is assumed that the student has a laptop computer at his disposal.
Elective for master in biosciences
Lectures, exercises, group and individual work and student presentations.
Evaluated annually by students through course surveys. These evaluations are included in the university’s quality assurance system.
Compound assessment
- Mandatory participation in at least 75% of specified activities. Counts 0/100 of the grade. Grading rule: Pass/Fail. Prerequisite for receiving a final grade in the course, counts 0/100 of the grade.
- Submission of up to 3 reports, grading rule: Pass/Fail. Prerequisite for receiving a final grade in the course.
- Assignment, weights 40/100 of the grade. Grading rule: A-E, best A, fail F.
- Oral exam, weights 60/100 of the grade. Grading rule: A-E, best A, fail F.
Pen, paper and up to 2 bilingual dictionaries.
Generating an answer using ChatGPT or similar artificial intelligence and submitting it wholly or partially as one's own answer is considered cheating.
It is recommended that students have basic knowledge in the subject areas of livestock production and ethology.
Overlap refers to a similarity between courses with the same content. Therefore, you will receive the following reduction in credits if you have taken the courses listed below:
HUS5000 - Precision Livestock Farming - 7.5 credits