The article provides information on concept of creating a model of commercial activities network management for agricultural organizations. In the modern information world, important factors in the development of commercial activities of organizations of the agro-industrial complex are the construction of productive long-term relationships with contractors and the place of the organization in the network structure. This becomes possible only under the condition of successful implementation and development of network relations, implementation of "smart agriculture" technologies, adequacy of the network's potential and information resources of the organization, which ultimately stimulates organizations to create high-quality network management systems and network transactions in the agro-industrial complex.
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.
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