ABSTRACT 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.

中文翻译：

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用于自动农场控制的神经网络

摘要 计量学、植物学特征的预测对于农业的不同方向极为重要。这些数据的可用性使我们能够调整作物的种植过程，这对产量、成熟速度和生长培养物中维生素的存在具有巨大影响。在不使用基因突变和除草剂的情况下，由于培养物生长条件的变化而增加产量是农业领域最受欢迎的目的地。在这份手稿中，展示了用于构建高效自治农场的神经网络的实现。农场开发通过控制以下指标为作物生长创造最佳条件：光照、地面 PH、气温、地面温度、空气湿度、CO2 浓度和地面湿度。介绍了使用神经网络预测蔬菜生长的理论研究和实验研究。所提出的模型也可以被视为一种原型设备，用于测试各种栽培蔬菜，以确定它们生长的最佳特性。