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Intelligent Technologies and Systems

Course developers

Course abstract

The aim of the course is to develop students’ ability to analyse, design, and apply intelligent technologies and systems in agricultural engineering, using methods such as artificial intelligence, data processing, automation, and others. The course covers solutions in artificial intelligence, data analysis, sensor networks, robotics, digital twins, and automation, which are applied in agricultural machinery, applied energy, machine design and production process optimization. The course provides students with knowledge and practical skills in the development, integration, and application of intelligent systems in the context of smart agriculture. Special emphasis is placed on data-driven decision-making, energy efficiency, predictive maintenance, and cyber-physical system security, as well as the ability to analyse and design intelligent systems using sensors, IoT, and data models, and to promote understanding of security and sustainability in the use of intelligent technologies.

Learning outcomes and their assessment

Knowledge. Understands the concepts, classification, and applications of intelligent technologies and systems (ITS). Has a clear understanding of the theoretical foundations of ITS and their significance in agricultural engineering. Is familiar with the basic principles of artificial intelligence, machine learning, and automation. Understands the role of sensor systems, data collection, digital twins, and robotics in smart agriculture. Knowledge is assessed through practical and independent assignments.
Skills. Able to analyse ITS examples, use AI tools for data interpretation, and create simple models and visualizations. Can analyse and process different types of data using modern analytical and AI methods. Capable of designing and evaluating the performance of intelligent systems using sensor networks, machine learning, or expert systems. Able to perform experimental data analysis, interpret results, and assess technical solutions. Can present an intelligent system concept based on technical and economic justification. Skills are assessed through practical and independent assignments.

Competences. Able to integrate ITS into engineering solutions, critically evaluate their suitability, and collaborate on data management and AI tasks. Can independently assess and apply intelligent technologies to specific agricultural engineering problems. Capable of integrating various technological solutions (AI, sensors, automation, energy) into a unified system. Able to justify chosen solutions with arguments and analyse their efficiency and security. Competences are assessed through independent assignments.

Course Content(Calendar)

1. Introduction to ITS. Basic concepts – intelligent technologies, intelligent systems, artificial intelligence, machine learning, etc. Regulatory documents for AI use at LBTU – 1 h
2. Best practices in file management. Data literacy for beginners. Practical guidelines for implementing AI in institutional work and services. Examples of IT, IS, and AI applications in Latvian public administration – 2 h
3. AI tools and their applications. Classification of AI tools. Useful AI tools for students in studies, research, thesis preparation, data processing, etc. – 5 h
4. Analysis of advanced technology development over the past decade – 1 h
5. Examples of ITS in automotive, agricultural machinery, energy, machine design and manufacturing, animal monitoring, food supply chains, etc., within LBTU fields – 2 h
6. The importance of data and digital twins. Sensor networks, data collection, IoT, digital twin concept. Simple IoT simulation – 1 h
7. Basics of machine learning. Supervised and unsupervised learning, regression, classification. Data analysis with Python/Excel. Neural networks and deep learning. CNN, RNN, and their applications in technical systems. Model training – 1 h
8. Expert and decision support systems. Rule-based systems, fuzzy logic, decision trees. Creating a simple fuzzy system – 1 h
9. Robotics and autonomous systems. Agricultural robots, autonomous tractors, drones. Simulator or video analysis: autonomous machinery in action – 1 h
10. Image processing and environmental assessment. Computer vision, image classification, object recognition. ITS modelling – 2 h
11. Intelligent energy systems. Energy efficiency, forecasting, optimization with AI. Energy consumption analysis and prediction – 1 h
12. Cyber-physical systems and security. Integrated systems, data security, standards. Cyberattack scenarios in machinery – 1 h
13. Economic analysis of ITS and impact on production efficiency. ITS safety and reliability. ITS and climate change adaptation strategies. ITS data management and data ethics – 1 h

14. Presentation and defence of students’ independent work, summary – 2 h

Requirements for awarding credit points

The course concludes with a pass/fail assessment. Students receive a pass if all practical and independent assignments have been completed and successfully defended.

Description of the organization and tasks of students’ independent work

At least six independent assignments (one for every two course content topics), for example: an in-depth analysis of one example of IT, IS, or AI application in Latvian public administration; an individual task on using AI tools in the study process, research, or data processing; an individual task analysing the development of advanced technologies over the past decade; and others.

Criteria for Evaluating Learning Outcomes

All coursework during the semester (practical assignments and individual independent tasks) is graded on a 10-point scale. Each assignment must be completed with at least the minimum passing grade.

Compulsory reading

1. Gunchenko Y. (2021). Intellectual systems and information technologies: Monograph. Vienna; Premier Publishing s.r.o.; 184 p. DOI: 10.29013/gunchenkoy.isait.2021.184
2. Maslej N., Fattorini L., Perrault R., Gil Y., Parli V., Kariuki N., Capstick E., Reuel A., Brynjolfsson E., Etchemendy J., Ligett K., Lyons T., Manyika J., Carlos Niebles J., Shoham Y., Wald R., Hamrah A., Santarlasci L., Betts Lotufo J., … Oak S. (2025). Artificial Intelligence Index Report 2025. 457 p.
3. Moskvins G. (2008). Intelektuālās sistēmas un tehnoloģijas: Mācību līdzeklis. Jelgava: LLU. 136 lpp.

4. Russel S., Norvig P. (2022). Artificial Intelligence: A Modern Approach (4th ed.). Pearson Education Limited; 1136 p.

Further reading

1. Moskvins G. (2008). Automatizācija: Mācību līdzeklis. Jelgava: LLU. 120 lpp.
2. Araújo S. O., Peres R. S., Ramalho J. C., Lidon F., Barata J. (2023). Machine Learning applications in agriculture: Current trends, challenges, and future perspectives. Agronomy, 13. DOI: 10.3390/agronomy13122976
3. Banh L., Strobel G. (2023). Generative artificial intelligence. Electronic Markets, 33. DOI: 10.1007/s12525-023-00680-1
4. Rajak P., Ganguly A., Adhikary S., Bhattacharya S. (2023). Internet of Things and smart sensors in agriculture: Scopes and challenges. Journal of Agriculture and Food Research, 14, 100776. DOI: 10.1016/j.jafr.2023.100776
5. Xu Y., Liu X., Cao X., Huang C., Liu E., Qian S., Liu X., Wu Y., Dong F., Qiu C. W., Qiu J., Hua K., Su W., Wu J., Xu H., Han Y., Fu C., Yin Z., Liu M., … Zhang J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2. DOI: 10.1016/j.xinn.2021.100179

6. Aijaz N., Lan H., Raza T., Yaqub M., Iqbal R., Pathan M. S. (2025). Artificial intelligence in agriculture: Advancing crop productivity and sustainability. Journal of Agriculture and Food Research, 20, 101762. DOI: 10.1016/j.jafr.2025.101762

Periodicals and other sources

1. Gebresenbet G., Bosona T., Patterson D., Persson H., Fischer B., Mandaluniz N., Chirici G., Zacepins A., Komasilovs V., Pitulac T., Nasirahmadi A. (2023). A concept for application of integrated digital technologies to enhance future smart agricultural systems. Smart Agricultural Technology, 5, 100255. DOI: 10.1016/j.atech.2023.100255
2. Ghazal S., Munir A., Qureshi W. S. (2024). Computer vision in smart agriculture and precision farming: Techniques and applications. Artificial Intelligence in Agriculture, 13, 64–83. DOI: 10.1016/j.aiia.2024.06.004
3. Javaid M., Haleem A., Khan I. H., Suman R. (2023). Understanding the potential applications of Artificial Intelligence in agriculture sector. Advanced Agrochem, 2, 15–30. DOI: 10.1016/j.aac.2022.10.001
4. Jiang Y., Li X., Luo H., Yin S., Kaynak O. (2022). Quo vadis artificial intelligence? Discover Artificial Intelligence, 2. DOI: 10.1007/s44163-022-00022-8
5. Kiran, Saikanth D. R. K., Saikia A. R., Chintey R., Talukdar N., Bahadur R., Vasuki A. (2023). Smart agriculture: Technologies, practices, and future directions. International Journal of Environment and Climate Change, 13 (12), 689–695. DOI: 10.9734/ijecc/2023/v13i123730
6. Masasi J., Ng’ombe J. N., Masasi B. (2024). Artificial Intelligence in agriculture: Current trends and innovations. Big Data in Agriculture, 6 (2), 96–99. DOI: 10.26480/bda.02.2024.96.99
7. Sadhu P. K., Yanambaka V. P., Abdelgawad A. (2022). Internet of Things: Security and solutions survey. Sensors, 22, 7433. DOI: 10.3390/s22197433
8. Sharma A., Georgi M., Tregubenko M., Tselykh A., Tselykh A. (2022). Enabling smart agriculture by implementing artificial intelligence and embedded sensing. Computers and Industrial Engineering, 165, 107936. DOI: 10.1016/j.cie.2022.107936
9. Sheikh H., Prins C., Schrijvers E. (2023). Mission AI: The new system technology. Springer; 421 p. DOI: 10.1007/978-3-031-21448-6

10. Waqas M., Naseem A., Humphries U. W., Hlaing P. T., Dechpichai P., Wangwongchai A. (2025). Applications of machine learning and deep learning in agriculture: A comprehensive review. In: Green Technologies and Sustainability (Vol. 3, p. 100199). KeAi Communications Co. DOI: 10.1016/j.grets.2025.100199

Notes

A mandatory course in the IITF Master’s study program in Agricultural Engineering.