The relevance of the study is due to the need to improve productivity enhancement technologies within the framework of programmed harvesting in precision farming using specialized hardware and software systems based on artificial intelligence algorithms that allow for a stable increase in yield, taking into account strict compliance with environmental standards and restrictions, preservation of agricultural landscapes and soil fertility. In the course of the work, the authors proposed an algorithm for managing the production processes of phytoagrocenosis cultivation, adapted to the conditions of precision agriculture in the Volgograd region, taking into account the characteristics of the agricultural landscape, the composition of the soil cover of the site, climatic factors, etc., which is necessary for an accurate and adequate description of models of agricultural objects in the development of hybrid automated systems within the framework of programmed cultivation of agricultural crops. A control model of the pilot site in closed agroecosystems has been implemented, including three levels: lower (sensors, executive bodies), middle (PLC/MC), upper (human-machine interface). The communication between the middle and upper levels is represented by the Modbus RTU protocol implemented via Bluetooth radio communication. A hybrid automated control system (GASU) for a heterogeneous agricultural object is proposed, where the control of some parameters (temperature, humidity, irrigation, etc.) can be implemented by non-adaptive and adaptive methods, and the condition of plants, prevention and determination of diseases – using artificial intelligence and machine learning algorithms, including neural networks. The results obtained in the course of the study can be used to improve domestic technologies to ensure the sustainability of agricultural production and reduce the level of import dependence in the framework of a comprehensive solution to the problems of socio-economic development of regions.