Food Processing: Techniques and Technology Food Processing: Techniques and Technology Техника и технология пищевых производств 2074-9414 2313-1748 65745 10.21603/2074-9414-2023-2-2440 ОРИГИНАЛЬНАЯ СТАТЬЯ ORIGINAL ARTICLE ОРИГИНАЛЬНАЯ СТАТЬЯ Neural Network and Home Hydroponics Применение нейронной сети для управления системой домашней гидропоники https://orcid.org/0000-0003-3035-0354 Бородулин Дмитрий Михайлович Borodulin Dmitry M. https://orcid.org/0000-0003-4512-1933 Шафрай Антон Валерьевич Shafrai Anton V. shafraia@mail.ru https://orcid.org/0000-0002-6408-9839 Максименко Александр Александрович Maximenko Alexander A. sasha-maksimienko@mail.ru Кемеровский государственный университет Кемерово Россия Kemerovo State University Kemerovo Russian Federation Кемеровский государственный университет Кемерово Россия Kemerovo State University Kemerovo Russian Federation Кемеровский государственный университет Кемерово Россия Kemerovo State University Kemerovo Russian Federation 23 06 2023 23 06 2023 53 2 384 395 13 12 2022 07 02 2023 https://fptt.ru/en/issues/21711/21760/

Метод беспочвенного культивирования растений, называемый гидропоникой, является перспективным способом обеспечения населения продуктами растительного происхождения. Гидропонное производство характеризуется эффективным управлением водными ресурсами, сокращением вегетационного периода роста растений, низким уровнем их заболеваемости и поражения насекомыми, а также круглогодичным циклом выращивания. Однако существуют сложности в конструировании, эксплуатации и обслуживании гидропонных установок. В сельском хозяйстве все чаще применяются технологии на базе нейронных сетей, способные управлять технологическими процессами. Цель работы заключалась в применении нейронной сети для повышения эффективности выращивания растений в системе домашней гидропоники. В исследовании использовалась установка гидропоники питательного слоя, в которую высаживались растения вида Lactuca sativa в количестве 10 штук. С помощью датчиков собиралась информация о температуре и влажности воздуха, освещенности растений и температуре поверхности листа. Обработка данных, обучение нейронной сети и программирование микроконтроллера проводились с помощью языка программирования Python 3, используя фрейморки PyTorch и MicroPython соответственно. Наиболее эффективной архитектурой нейронной сети для решения поставленной задачи управления оказался четырехслойный персептрон. Данный тип архитектуры широко используется в качестве механизма управления. В экспериментах с меньшим количеством слоев нейронная сеть показала высокий уровень ошибки, который составил более 5 %. При увеличении слоев выше четырех ошибка обученной нейронной сети осталась на уровне четырехслойной и составила 0,2 %. Дальнейшие практические испытания обученной нейронной сети показали повышение энергоэффективности на 32,3 % по сравнению с классическим алгоритмом управления при близких значениях транспирации растений. Данная технология может быть применена для интеграции в энергосберегающие жилые помещения и системы умного дома с целью повышения самообеспечения населения продуктами растениеводства.

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.

 Гидропоника технологии растениеводства современное растениеводство автоматизация процесса Hydroponics plant growing technologies modern plant growing process automation Исследование было проведено с использованием оборудования Центра коллективного пользования научным оборудованием Кемеровского государственного университета (КемГУ) в рамках соглашения № 075-15- 2021-694 от 05.08.2021, заключенного между Министерством науки и высшего образования Российской Федерации (Минобрнауки России) и КемГУ (уникальный идентификатор контракта RF----2296.61321X0 032). The research was conducted on the premises of the Research Equipment Sharing Center, Kemerovo State University (KemSU) , based on agreement No. 075-15-2021-694, August 5, 2021, between the Ministry of Science and Higher Education of the Russian Federation (Minobrnauki) and KemSU (contract identifier: RF----2296.61321X0032).

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