Neural network for automatic farm control

Prediction of metrological, botanical characteristics is extremely important for different directions in agriculture. The availability of these data allows us to adjust the process of growing crops, which has a huge impact on yield, speed of ripening and the presence of vitamins in the grown culture. Increasing yields due to changes in culture growing conditions without the use of gene mutations and herbicides are the most popular destination in the agriculture field. In this manuscript, a realisation of the neural network for the construct of an efficient autonomous farm was represented. The developed by farm creates the optimal conditions for growing a crop by controlling the following indicators: Illumination, PH of the ground, air temperature, the temperature of the ground, air humidity, CO2 concentration and humidity of the ground. Theoretical research and experimental research on the use of a neural network to predict vegetable growth were represented. The presented model can also be considered as a prototype device for testing various cultivated vegetables to identify the optimal characteristics for them growing.