- Foundations of digitization and digital transformation
- Applications for digital farming methods, basic and technical processes (e.g. process engineering in crop production, soil cultivation, sowing, fertilization, crop protection and harvesting).
- Overview of systems and areas of digital farming: e.g. sensors and actuators, farm management information systems, digitized agricultural technology (tractors and mounted implements) up to systems with autonomous functions, tracking systems, decision support systems, satellite systems, geographic information systems
- Systems and data in precision farming (systems and data used in the field in the crop year: tillage, seeding, ...).
- Systems and data in Precision Livestock Farming
- Cross-cutting and further in-depth topics: Role of preceding and following sectors, economic factors, documentation and reporting in agriculture (acreage registers, fleet management systems), cycle of nutrients and sustainability.
Foundations of Digital Farming (M, 4.0 LP)
|Module Number||Module Name||CP (Effort)|
|INF-37-51-M-5||Foundations of Digital Farming||4.0 CP (120 h)|
|CP, Effort||4.0 CP = 120 h|
|Position of the semester||1 Sem. in SuSe|
|Level|| Master (Entry Level)|
|Area of study||[INF-SE] Software-Engineering|
|Reference course of study||[INF-82.79-SG] B.Sc. Computer Science|
|Type/SWS||Course Number||Title||Choice in |
|SL||SL is |
required for exa.
Foundations of Digital Farming
|P||42 h||78 h||
Examination achievement PL1
- Form of examination: written or oral examination
- Examination Frequency: each semester
- Examination number: 63751 ("Foundations of Digital Farming")
Evaluation of grades
The grade of the module examination is also the module grade.
Competencies / intended learning achievements
The students understand the importance of digital farming for agriculture in particular and for a sustainable society in general and the role of computer science in digital farming in particular. They will be able to analyse the effects of targeted farming with digital tools in relation to conventional methods and to reflect on the economic and societal impacts.
Upon successful completion of the module, students will be able to,
- analyse important target variables of digital farming (e.g. plant yield, biodiversity),
- describe specific application scenarios of IT solutions in digital farming,
- analyse IT systems in complex digital ecosystems,
- describe the potentials and challenges, such as lack of interoperability of systems and lack of end-use acceptance, associated with digital farming,
- describe suitable IT systems for generating data in digital farming and analyse their use.
Will be announced during the lecture.
Requirements for attendance of the module (informal)None
- Notice: Some Courses have informal requirements for attendance: