About this course

Smart farming is highly interdisciplinary and contains areas within among other things, applied robotics, sensors, machine and tool development, collection of vital data and AI, image analysis, GIS mapping, and not least knowledge in plant, soil and fertilizer science. Students from several topic areas are invited to participate and are mixed in interdisciplinary groups, where the goal is to work actively together on concrete cases that have been announced and prepared in advance. The students go deeply into their definite area and finally present the work to the other students and the examiner. The students get a close look at advanced measuring equipment and possibilities for the digitization of Norwegian agriculture, for example by the use of drones and various cameras for mapping the existing and varying conditions in the field and how this can be optimized through site-specific dosing using VRA technology ( volume rate application). Students with different backgrounds and subject areas make the groups strong and complementary. Student innovation and active learning are emphasized. This can also provide a basis for further interdisciplinary Bachelor's or Master's theses work where different technologies can be used in different study programs & purposes of use.

Learning outcome

The student will become familiar with the most used technologies within the digitization of agriculture through hands-on teaching where the measurement methods are applied to relevant cases, preferably linked to existing projects within the area. The students will learn to work in groups with interdisciplinary students where the goal is through critical thinking and collaboration to further develop the solutions and assess what such equipment can mean for Norwegian agriculture both in the short and long term. The equipment and technology can also be used in other related areas such as forestry, greenhouses and green environments, animal husbandry in addition to conventional agriculture.

  • Learning activities

    Work in groups with interdisciplinary subjects and knowledge levels.

    Learn to collaborate, be creative and think of practical solutions for a given case.

    Become familiar with advanced and new technological targeting methods and learn their strengths and weaknesses.

    Present results from work to the examiner.

    Listen to the other groups and learn from each other.

    Finalize a full report of their work within two months after the bootcamp week.

  • Teaching support

    Available research technical infrastructure at NMBU within the areas.

    Textbooks & litterature online or by the NMBU library and other resources.

    Access to results and status in ongoing relevant projects.

    Technical support to ensure the equipment works

    Mentors who assist the group

    Each day starts with short academic presentations from professionals at NMBU in the area for all groups.

    Each group then works on the selected case.

  • Syllabus

    Textbooks and online material and material from literature searches at the NMBU library.

    Access to results and status in ongoing projects.

    Information from relevant machine companies of the equipment used.

    Necessary software and training in the computer program used.

  • Recommended prerequisites

    No prior knowledge is required, but agronomic understanding or participation in relevant subjects is an advantage,

    especially TMPA210 Precision Agriculture and Sensor Technology

    possibly TMPA220 Landscape Engineering, Plant Establishment and Management

    and JORD231-1 25V Fertilization planning and precision agriculture or similar courses.

    But students from robotics, sensors, geomatics, image processing, computer science, mechanical engineering and so fort will also fit very well into the activities. together with other interdisciplinary students with an agronomic background.

  • Assessment method

    Examination takes place by the students presenting their group work together with the other student groups and an external examiner.

    Any changes are made and a complete written report is submitted within two months of the end of the bootcamp week and evaluated by an internal examiner.



    Assignment Grading: Passed / Not Passed

  • About use of AI

    K2 - Specified use of AI.

    AI is used more and more in the data analysis of large collected amounts of data in agriculture (talking about petabytes = 1,000,000 GB). But it will not be used in the preparation and the writing of the report.

    The use of AI must comply with the guidelines for the use of artificial intelligence (AI) at NMBU.

    Descriptions of AI-category codes.

  • Examiner scheme

    External examiner of final result at the end of the bootcamp week.

    Internal examiner of final report submitted no later than two months after the end of the bootcamp week including the changes that emerge during the first external evaluation.

  • Mandatory activity

    Active participation all days during the bootcamp period.

    Solve cases in smart farming together with fellow students.

    Follow the scheduled compulsory lectures (one at the start of each day)

    Present the final work (could be in the form of a detailed powerpoint presentation) for an external examineer.

    Review the work in a written presentation and/or show and demonstrate the final end result in another way in front of the external examiner.

  • Notes

    The first BootCamp was organized in 2024 with very successful results. It will also be run in 2025 as a special curriculum and then from 2026 as a separate subject code. Smaller adaptations will occur after the experiences that are continuously made, but such student-active learning seems to have a very good effect.

  • Teaching hours

    30 hours of lectures.

    90-100 hours of independent work and student innovation in groups with mentor support when needed

  • Reduction of credits

    No

  • Admission requirements

    Minimum requirements for entrance to higher education in Norway (generell studiekompetanse)