• Imants Zarembo Institute of Engineering, Rezekne Academy of Technologies (LV)
  • Sergejs Kodors Institute of Engineering, Rezekne Academy of Technologies (LV)
  • Ilmārs Apeināns Institute of Engineering, Rezekne Academy of Technologies (LV)
  • Gunārs Lācis Institute of Horticulture (LatHort) (LV)
  • Daina Feldmane Institute of Horticulture (LatHort) (LV)
  • Edgars Rubauskis Institute of Horticulture (LatHort) (LV)



cyber-physical system, digital twin, data-based decision-making, smart horticulture


Orchard management can benefit greatly from the use of modern technology to reach higher yields, decrease costs and achieve more sustainable farming. Implementation or such a smart farming approach into orchard management can be realised via application of unmanned aerial vehicles (UAV) for data collection and artificial intelligence (AI) for yield estimation and forecasting. On top of that, a digital twin of the orchard can be implemented to represent the physical system of the orchard in the digital format allowing implement modern data-driven decision-making based on fruit-growing automation.

The aim of this study is to present a digital twin based on application of UAV and AI for orchard management that is being developed as part of a research project lzp-2021/1-0134. At this moment, we are developing a user-centred design which is oriented to satisfy horticulture specialists’ needs for an autonomous monitoring system and to help them in decision-making. Within the framework of this study an enterprise model of orchard management is designed, which supports the digital twin concept and provides autonomous orchard monitoring. The study is scoped with subjects: apples, pears and cherries, and yield management based on orchard monitoring using UAV.


Supporting Agencies
This research is funded by the Latvian Council of Science, project “Development of autonomous unmanned aerial vehicles based decision-making system for smart fruit growing”, project No. lzp-2021/1-0134.


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D. M. Botín-Sanabria, A.-S. Mihaita, R. E. Peimbert-García, M. A. Ramírez-Moreno, R. A. Ramírez-Mendoza, and J. de J. Lozoya-Santos, “Digital Twin Technology Challenges and Applications: A Comprehensive Review,” Remote Sensing, vol. 14, no. 6, p. 1335, Mar. 2022,

Ch. Pylianidis, S. Osinga, I. N. Athanasiadis, “Introducing digital twins to agriculture”, Computers and Electronics in Agriculture, Volume 184, 2021.

P. Moghadam, T. Lowe, E. J. Edwards, “Digital Twin for the Future of Orchard Production Systems”. Proceedings 2019, 36, 92.

C. N. Verdouw, J. W. Kruize, “Digital twins in farm management: illustrations from the FIWARE accelerators SmartAgriFood and Fractals.” 7th Asian-Australasian Conference on Precision Agriculture, 2017,

J. Grabis, J. Zdravkovic, J. Stirna, “Overview of Capability-Driven Development Methodology”. Capability Management in Digital Enterprises. Springer, Cham, 2018,

J. Stirna, J. Grabis, I. Stefanisina, E. Roponenta, V. Minkevica, “ARTSS: Advanced Resilience Technologies for Secure Service” [Online], Oct 31 2020. Available:

S. Kodors, G. Lacis, O. Sokolova, T. Bartulsons, and I. Apeinans, “Fruit-Grower Digital Tool for Apple Scab Detection.”, International Symposium of Digital Horticulture, Bulduri, Latvia, 2021.

C. Zhang, J. Valente, L. Kooistra et al., “Orchard management with small unmanned aerial vehicles: a survey of sensing and analysis approaches.” Precision Agric 22, 2021, pp. 2007–2052.

S. Bērziša, G. Bravos, T. C. Gonzalez et al., “Capability Driven Development: An Approach to Designing Digital Enterprises.”, Bus Inf Syst Eng 57, 2015, pp. 15–25.

U. Meier, “Growth stages of mono- and dicotyledonous plants: BBCH Monograph.” Open Agrar Repositorium, Quedlinburg, 2018.

U. Meier, “BBCH – Monographie.”, Entwicklungsstadien von Pflanzen. Berlin und Wien, Blackwell, 1997. (in German)




How to Cite

I. Zarembo, S. Kodors, I. Apeināns, G. Lācis, D. Feldmane, and E. Rubauskis, “DIGITAL TWIN: ORCHARD MANAGEMENT USING UAV”, ETR, vol. 1, pp. 247–251, Jun. 2023, doi: 10.17770/etr2023vol1.7290.