ОСНОВЫ ИДЕНТИФИКАЦИИ СИСТЕМЫ ПРИ УПРАВЛЕНИИ ТЕМПЕРАТУРОЙ И ОТНОСИТЕЛЬНОЙ ВЛАЖНОСТЬЮ В СОВРЕМЕННЫХ ТЕПЛИЦАХ

Adeyemi, O., Grove, I., Peets, S., & Domun, T. (2018). Dynamic neural network modelling of soil moisture content for predictive irrigation scheduling. Sensors, 18(3408). https://doi.org/10.3390/ s18103408

Altes-Buch, Q., Quoilin, S., & Lemort, V. (2019, March). Greenhouses: A Modelica library for the simulation of greenhouse climate and energy systems. In Proceedings of the 13th International Modelica Conference (pp. 54). Regensburg, Germany. https://doi.org/10.3384/ecp19157533

Arnaud, S. E., Calisti, M., & Polydoros, A. (2025). Data-driven greenhouse climate regulation in lettuce cultivation using BiLSTM and GRU predictive control. Computers and Electronics in Agriculture, 215, 108417. https://doi.org/10.1016/j.compag.2025.108417

Chen, S., Liu, A., Tang, F., Hou, P., Lu, Y., & Yuan, P. (2025). A Review of Environmental Control Strategies and Models for Modern Agricultural Greenhouses. Sensors, 25(5), 1388. https://doi. org/10.3390/s25051388

Chimankare, R. V. (2023). A review study on the design and control of optimised greenhouse environments. Renewable Agriculture Reviews, 18(4), 221–235.

Despommier, D. D. (2011). The vertical farm: Feeding the world in the 21st century. First Picador ed., St. Martin’s Press.

Diaz, G. (2023). Design and evaluation of a greenhouse interface for user-friendly climate monitoring and control. International Journal of Smart Agriculture, 9(3), 102–115.

Kalantari, F., Tahir, O. M., Joni, R. A., & Fatemi, E. (2017). Vertical farming: Creating an accessible and sustainable future. A review. Journal of Landscape Ecology, 10(1), 35–60.

Kittas, C., Katsoulas, N., Bartzanas, T., & Bakker, S. (2013). Greenhouse climate control and energy use. Food and Agriculture Organization of the United Nations.

Labidi, A., Chouchaine, A., & Marni, A. (2021). Intelligent climate control system inside a greenhouse. International Journal of Advanced Computer Science and Applications, 12(2). https://doi. org/10.14569/IJACSA.2021.0120229

Linker, R., Kacira, M., & Arbel, A. (2011). Robust climate control of a greenhouse equipped with variable-pressure fogging system and variable-speed extracting fans. Control Engineering Practice, 19(6), 636–648. https://doi.org/10.1016/j.conengprac.2011.02.003

López-Cruz, I.L., Ramírez-Arias, A., Rojano-Aguilar, A. and Ruiz-García, A. (2008). Modeling of greenhouse climate using evolutionary algorithms. Acta Hortic. 801, 401-408. https://doi. org/10.17660/ActaHortic.2008.801.42

Mahmood, F., Govindan, R., Bermak, A., Yang, D., & Al-Ansari, T. (2023). Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment. Applied Energy, 343, Article 121190. https://doi.org/10.1016/j.apenergy.2023.121190

Mallick, S., Airaldi, F., Dabiri, A., Sun, C., & De Schutter, B. (2024). Reinforcement Learning- based Model Predictive Control for Greenhouse Climate Control. arXiv preprint. arXiv:2409.12789 arxiv.org

Matyakubova, P. M., Ismatullayev, P. R., & Sharipov, Sh. M. (2023). O‘lchash usullari va vositalari (fizik-kimyoviy o‘lchashlar bir qismi) [Methods and tools of measurement (part of physico-chemical measurements)]. Tаshkent.

Morales, M. (2018). Deep reinforcement learning. Manning Publ.

Morcego, B., López, G., & Colomer, M. (2023). Reinforcement learning versus model predictive control on greenhouse climate control. Control Engineering Practice, 138, 105536. https://doi. org/10.1016/j.conengprac.2023.105536

Naagarajan, R. A., Sathyanarayanan, K. K., Bauer, N., & Streif, S. (2025). Automated analysis and textual summarization of time-varying references in advanced greenhouse climate control. Frontiers in Agronomy, 7. https://doi.org/10.3389/fagro.2025.1536998

Platero-Horcajadas, M., Pardo-Pina, S., Cámara-Zapata, J.-M., Brenes-Carranza, J.-A., & Ferrández-Pastor, F.-J. (2024). Enhancing greenhouse ef�iciency: Integrating IoT and reinforcement learning for optimized climate control. Sensors, 24(24), 8109. https://doi.org/10.3390/s24248109 MDPI

Puglisi, G., Vox, G., Schettini, E., Morosinotto, G., & Campiotti, C. (2017). Climate control inside a greenhouse by means of a solar cooling system. In International Symposium on New Technologies for Environment Control, Energy-Saving and Crop Production in Greenhouse and Plant 1227. Beijing, China.

Sen, N. (2018). Automatic Climate Control of a Greenhouse: a Review. ADBU Journal of Electrical and Electronics Engineering (AJEEE).

Singh, N. (2024). IoT-based greenhouse technologies for enhanced crop monitoring and climate control. Journal of Agricultural IoT Studies, 5(2), 45–56.

Tuttelberg, K., Kilter, J., & Uhlen, K. (2017). Comparison of system identification methods applied to analysis of inter-area modes. In Proceedings of the International Conference on Power Systems Transients (IPST2017). Seoul, South Korea.

Urakov, E. E., Musayeva, Z. D., & Rashidov, T. E. (2023). The importance of metrology and standardization in greenhouse climate control. Science and Innovation, Series D, 2(1). https:// scientists.uz/view?id=3604

Van Mourik, S., van ’t Ooster, B., & Vellekoop, M. (2023). Plant performance in precision horticulture: Optimal climate control under stochastic uncertainty. Biosystems Engineering, 232, 34– 48. https://doi.org/10.1016/j.biosystemseng.2023.05.010

Yassin, I. M., Taib, M. N., & Adnan, R. (2013). Recent Advancements & Methodologies in System Identification: A Review. Scientific Research Journal (SCIRJ), 1(1), 14-33. https://www.scirj.org/ papers-0813/scirj-august-2013-edition-03.pdf

Yusuf, A. G. (2025). Optimizing greenhouse microclimate for plant pathology. Plant Pathology and Environment, 12(1), 77–89.