Course: Modern trends in agricultural technology

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Course title Modern trends in agricultural technology
Course code KZT/MTZT
Organizational form of instruction no contact
Level of course Doctoral
Year of study not specified
Semester Winter and summer
Number of ECTS credits 0
Language of instruction Czech
Status of course Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Bartoš Petr, doc. RNDr. Ph.D.
Course content
Current trends in research and development in the field of agricultural technology. Use of GPS, systems for precision agriculture, modern trends in machine design.

Learning activities and teaching methods
Work with text (with textbook, with book), Individual preparation for exam, Work with multi-media resources (texts, internet, IT technologies), Individual tutoring
  • Class attendance - 50 hours per semester
  • Semestral paper - 100 hours per semester
  • Preparation for exam - 100 hours per semester
Learning outcomes
The aim is to study current trends in research and development in the field of agricultural technology.
Gaining knowledge in the field of current trends in research and development in the field of agricultural technology in the use of GPS, systems for precision agriculture, modern trends in machine design.
Prerequisites
Completion of a master's or engineering degree

Assessment methods and criteria
Oral examination, Written examination, Seminar work

Working with text Individual study for the exam Working with multimedia resources Individual consultations with the teacher Elaboration of a seminar work on a given topic Presentation of seminar work
Recommended literature
  • Duckett, T., et al. Agricultural Robotics: The Future of Robotic Agriculture. UK-RAS, 2018.
  • Izmailov, A. Yu. Intelligent Technologies and Robotic Means in Agricultural Production. Herald of the Russian Academy of Sciences, 2019, 89(2):209-210.. Russian Academy of Sciences, 2019.
  • Jani, K., et al. Machine learning in films: an approach towards automation in film censoring J. of Data, Inf. and Manag. 2019. 2019.
  • Liu, N. et al. Exploiting Convolutional Neural Networks With Deeply Local Description for Remote Sensing Image Classification?, IEEE Access. IEEE, (2018): 6, pp. 11215? 11228. doi:. 2018.
  • Ma, L., Xie, W. and Huang, H. Convolutional neural network based obstacle detection for unmanned surface vehicle, Mathematical Biosciences and Engineering, (2019): 17(1), pp. 845?861. doi: .. 2019.
  • Panpatte, D. G. Artificial Intelligence in Agriculture: An Emerging Era of Research, 2018. Anand Agricultural University, 2018.
  • Souček, J., Machálek, A. Roboty v zemědělství, 2018,. Mechanizace zemědělství, 2018.
  • Sun, S. et al. Image processing algorithms for infield single cotton boll counting and yield prediction, Computers and Electronics in Agriculture, (2019): 166, p.. Elsevier, 2019.
  • Tekin, A. B. and Fornale, M. The development of a low cost UAV-based image acquisition system and the procedure for capturing data in precision agriculture, Turkish Journal of Agriculture and Forestry, (2019): 43(3), pp. 288?298. 2019.
  • Ullah et al. A survey on precision agriculture: technologies and challenges The 3rd International Conference on Next Generation Computing (2017), pp. 1-3.. 2017.
  • Wang, A., Zhang, W. and Wei, X. A review on weed detection using ground-based machine vision and image processing techniques, Computers and Electronics in Agriculture. (2019): 158, pp. 226-240. Elsevier, 2019.
  • Wei et al.. Monitoring leaf nitrogen accumulation in wheat with hyper-spectral remote sensing Acta Ecol. Sin., 28 (1) (2008), pp. 23-32.. 2008.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Agriculture and Technology Study plan (Version): Agricultural Ecology (1) Category: Agriculture and forestry - Recommended year of study:-, Recommended semester: -
Faculty: Faculty of Agriculture and Technology Study plan (Version): Agricultural Ecology (1) Category: Agriculture and forestry - Recommended year of study:-, Recommended semester: -
Faculty: Faculty of Agriculture and Technology Study plan (Version): General plant production (1) Category: Agriculture and forestry - Recommended year of study:-, Recommended semester: -