Abstract and keywords
Abstract (English):
Hydroponics is a method of soilless cultivation of plants. It shortens the vegetation period, reduces the risk of disease and insect infestation, and provides a year-round growing cycle. Hydroponics depends on efficient water management. It is associated with a complex design, operation, and maintenance. Neural networks can control complex technological processes in agriculture. The research objective was to use a neural network to increase the efficiency of a home hydroponics system. The study involved a nutrient bed hydroponics setup with ten Lactuca sativa plants. Sensors collected information about the temperature and humidity of air, illumination, and the temperature of the leaf surface. Data processing, neural network training, and microcontroller programming relied on Python 3, PyTorch, and MicroPython. The four-layer perceptron, which is a popular control mechanism, turned out to be the most effective neural network architecture. Fewer layers resulted in a high error rate (≥ 5%). When the number of layers was > 4, the error level remained at that of the four-layer experiment (0.2%). Further practical tests showed an increase in energy efficiency by 32.3%, compared to the classical control algorithm at close values of plant transpiration. Neural net technology could be integrated into energy-saving residential premises and smart home systems in order to increase the self-sufficiency of hydroponics installations.

Keywords:
Hydroponics, plant growing technologies, modern plant growing, process automation
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References

1. World population prospects: Population division department of economic and social affairs. United Nation; 2019. 46 p.

2. The state of food security and nutrition in the world. Building climate resilience for food security and nutrition. Rome: Food and Agriculture Organization; 2018. 202 p.

3. Alexandratos N, Bruinsma J. World agriculture towards 2030/2050: the 2012 revision. Rome: Food and Agriculture Organization; 2012. 155 p. https://doi.org/10.22004/ag.econ.288998

4. Bren d’Amour C, Reitsma F, Baiocchi G, Barthel S, Güneralp B, Erb K-H, et al. Future urban land expansion and implications for global croplands. Proceedings of the National Academy of Sciences. 2016;114(34):8939-8944. https://doi.org/10.1073/pnas.1606036114

5. Kaledin AP, Stepanova MV. Bioaccumulation of trace elements in vegetables grown in various anthropogenic conditions. Foods and Raw Materials. 2023;11(1):10-16. https://doi.org/10.21603/2308-4057-2023-1-551

6. Bychkova SM, Zhidkova EA, Andreeva OO. Innovative controlling technologies. Food Processing: Techniques and Technology. 2019;49(3):479-486. (In Russ.). https://doi.org/10.21603/2074-9414-2019-3-479-486

7. Rutkin NM, Lagutkina LYu, Lagutkin OYu. Urban agrotechnologies (city-farming) as a perspective branch of development of world agribusiness and the way to improve the cities food security. Vestnik of Astrakhan State Technical University. Series: Fishing Industry. 2017;(4):95-108. (In Russ.). https://doi.org/10.24143/2073-5529-2017-4-95-108

8. We are what we eat: Healthy eating trends around the world. Nielsen; 2015. 27 p.

9. Gerasimenko NF, Poznyakovskiy VM, Chelnokova NG. Healthy eating and its role in ensuring the quality of life. Technologies of the Food and Processing Industry of the Agro-Industrial Complex-Healthy Food Products. 2016;12(4):52-57. (In Russ.). https://elibrary.ru/VIPFHU

10. From agriculture to AgTech. An industry transformed beyond molecules and chemicals. Monitor Delloite; 2016. 24 p.

11. Sedych TV, Pogrebnyak SV. Growth and productivity of cucumbers in winter greenhouses in autumn-winter crop rotation on hydroponics succinct in LLC “Sibagroholding” in a suburb of the city of Omsk. Vestnik of Omsk SAU. 2016;23(3):53-58. (In Russ.). https://elibrary.ru/WLSMER

12. Dmitriev VM, Gandzha TV, Kurin'ka VS. Structural-functional scheme of a computer model of the smart hydroponic greenhouses. Informatika i Sistemy Upravleniya. 2018;55(1):51-63. (In Russ.). https://doi.org/10.22250/isu.2018.55.51-63

13. Saaid MF, Sanuddin A, Ali M, Yassin MSAIM. Automated pH controller system for hydroponic cultivation. IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE); 2015; Langkawi. Langkawi: IEEE; 2015. p. 186-190. https://doi.org/10.1109/ISCAIE.2015.7298353

14. William T. Hydroponics for everybody: All about home horticulture. Mama Publishing; 2015. 288 p.

15. Morimoto T, Hashimoto Y. Optimal control of plant growth in hydroponics using neural networks and genetic algorithms. Acta Horticulturae. 1996;406:433-440. https://doi.org/10.17660/ActaHortic.1996.406.43

16. Hashimoto Y. Computer integrated plant growth factory for agriculture and horticulture. IFAC Proceedings Volumes. 1991;24(11):105-110. https://doi.org/10.1016/B978-0-08-041273-3.50023-9

17. Hatou K, Nonami H, Itoh M, Tanaka I, Hashimoto Y. Computer integrated plant growth factory for agriculture and horticulture. IFAC Proceedings Volumes. 1991;24(11):301-306. https://doi.org/10.1016/B978-0-08-041273-3.50058-6

18. Yumeina D, Aji GK, Morimoto T. Dynamic optimization of water temperature for maximizing leaf water content of tomato in hydroponics using an intelligent control technique. Acta Horticulturae. 2017;5:55-64. https://doi.org/10.17660/ActaHortic.2017.1154.8

19. Son JE, Kim H, Ahn TI. Hydroponic systems. In: Kozai T, Niu G, Takagaki M, editors. Plant factory. An indoor vertical farming system for efficient quality food production. Academic Press; 2020. pp. 273-283. https://doi.org/10.1016/B978-0-12-816691-8.00020-0

20. Aji GK, Hatou K, Morimoto T, Modeling the dynamic response of plant growth to root zone temperature in hydroponic chili pepper plant using neural networks. Agriculture. 2020;10(6). https://doi.org/10.3390/agriculture10060234

21. Ferentinos KP, Albright LD. Fault detection and diagnosis in deep-trough hydroponics using intelligent computational tools. Biosystems Engineering. 2003;84(1):13-30. https://doi.org/10.1016/S1537-5110(02)00232-5

22. Saraswathy VR, Nithiesh C, Palani Kumaravel S, Ruphasri S. Integrating intelligence in hydroponic farms. International Journal of Electrical Engineering and Technology. 2020;11(4):150-158. https://doi.org/10.34218/IJEET.11.4.2020.017

23. Jung D-H, Kim H, Jhin C, Kim H-J, Park S. Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse. Computers and Electronics in Agriculture. 2020;173. https://doi.org/10.1016/j.compag.2020.105402

24. Saraswathi D, Manibharathy P, Gokulnath R, Sureshkumar E, Karthikeyan K. Automation of hydroponics green house farming using IoT. 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCA); 2018; Pondicherry. Pondicherry: IEEE; 2018. p. 1-4. https://doi.org/10.1109/ICSCAN.2018.8541251

25. Mehra M, Saxena S, Sankaranarayanan S, Tom RJ, Veeramanikandan M. IoT based hydroponics system using Deep Neural Networks. Computers and Electronics in Agriculture. 2018;155:473-486. https://doi.org/10.1016/j.compag.2018.10.015

26. Kularbphettong K, Ampant U, Kongrodj N. An automated hydroponics system based on mobile application. International Journal of Information and Education Technology. 2019;9(8):548-552. https://doi.org/10.18178/ijiet.2019.9.8.1264


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