TMPA210 Precision Farming and Sensor Technology

Credits (ECTS):5

Course responsible:Ibrahim Abdelfattah Abdelhameed

Campus / Online:Taught campus Ås

Teaching language:Norsk

Limits of class size:

Normally, a minimum number of 10 students are required in order to follow the described schedule.

A number of 5-10 students may be run by an adapted project & lecture solution.

Course frequency:Anually

Nominal workload:

The course has 5 ECTS which requires approximately 125 hours of work.

In addition, we recommend that students take the TMPA230 Smart Farming BootCamp which takes place in June immediately after completing TMPA210. This also provides 5 ECTS, This is also open for other students at NMBU.

Teaching and exam period:This course starts in Spring parallel. This course has teaching/evaluation in Spring parallel.

About this course

Technology and solutions for more sustainable agriculture. Participation in the Smart Farming and Green Innovation NMBU Sustainable plattform (2021-24) and Green Innovation Student LAB.. Precision farming. GNSS / GIS/ IoT. Agriculture 4.0. Digitalization in agriculture. Automation. Use of sensors, drones and field robotics in Agricultural engineering. Soil cultivation, tillage and soil preservation. How to reduce soil compaction. Machinery for seeding and planting of crops. Distribution of fertilisers. Cultivation and handling of potatoes. Power and base machinery for agriculture. Chemical and alternative pest control. Inspection and certification of sprayers.Students may follow autorization course for proper handling av pesticide (license to use of pesticides).

*)Green Innovation Student LAB is a makerspace promoting studentactivated learning and innovation mainly in the field through different projects by using sensors, robots an drones among others togehter with researchers and other stakeholders.

Learning outcome

The students should gain knowledge about agrotechnical solutions and methods to ensure product quality and reduce environmental impact in agriculture, as well as an overview over important measures and their consequences. The student shall obtain a fully understanding of precision farming and how this better can be implemented in Norwegian agriculture.The students should also be able to understand how agricultural machinery functions and interacts in a broader agronomic perspective. This means not just mechanical behaviour, but to a large degree how different types of machinery, tuning and utilisation must be adapted to farming structure, soil conditions, biological and climatic conditions etc.

Learning activities
The course focuses on the most important topics due to environmental sustainability and food quality. The student may choose relevant topics to present for the other students during the lectures in order to initiate more active teaching and stimulate to common discussions. Additionally, there will be arranged technical exercises and also a visit to a farm dealing with precision farming.
Teaching support
The course responsible is available for support and advice in working hours and by e-mail.
Syllabus
A wide range of national and international digital and written literature within the topics will be available for the students.
Prerequisites
No prior knowledge is required, but it is an advantage for the student to have practical experience from Norwegian agriculture. This can be compensated by reviewing basic literature on their own.
Potential supporting courses offered at NMBU:
JORD101 - Soil Science. PJH102 - Introduction to Horticulture and Crop Science,
JORD231 Fertilizer planning and precision farming
BIN302 Phenotyping in precision farming.
or equivalent are recommended.
Recommended prerequisites
Knowledge and experience from practical agriculture production will be an advantage.
Assessment method
Evaluation based on project work and presentation of a open approved topic within precision farming and sensor technology. External evaluation (passed/ not passed).

Project work Karakterregel: 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.
Descriptions of AI-category codes.
Examiner scheme
External sensor evaluates final project work and presentation (passed / not passed).
Mandatory activity
Write a final project paper that is presented to an external examiner.
Exercises and a one-day trip.
Notes
The course is offered to all students at the university with an adequate basic knowledge and experience.

Minimum10 students. If this number is not reached, the course may be arranged to accomodate more independent study.
Teaching hours
Preparation of a project assignment and presentation on a topic of your choice, approx. 40 hours.
Lectures including presentations from the students, approx. 60 hours.
Demonstrations and exercises with homework, approx. 30 hours.
Field trip, approx. 10 hours.
In the event of a low number of students, the proportion of project work is increased in relation to the number of lectures. This may differ and be adjusted somewhat according to the number of students.
Preferential right
Students taking Horticulture and Crop Science, Economics, Animal Sciences, Technology. Students also from other directions may participate. Precision agriculture is a multi displinary topic itself.
Students with a background in applied robotics, product development, sensors, geomatics and image recognition are also relevant for developing the interdisciplinary area within smart farming.
Reduction of credits
No
Admission requirements
Minimum requirements for entrance to higher education in Norway (generell studiekompetanse)