Abstract

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

Contribution

D.M. Borodulin supervised the research and wrote the manuscript. A.A. Maximenko reviewed scientific publications, conducted the experiment, processed the results, and wrote the manuscript. A.V. Shafrai provided consultation.

CONFLICTS OF INTEREST

The authors declare that there is no conflict of interest regarding the publication of this article.

FUNDING

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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