Irrigation management information system model with integrated elements of artificial intelligence

Abstract

The research is devoted to the development of new, self-adapting irrigation management systems allowing the control decisions reliability increasing based on the consideration of the terrain natural features in the service area. A model of irrigation management with integrated elements of artificial intelligence is proposed, which can significantly increase the forecast accuracy for the total water consumption of agricultural crops and the expected dates of irrigation. A distinctive feature of the proposed model is the use of an artificial intelligence segment to adapt the parameters of the total water bioclimatic model consumption to the conditions of a real area. The model assumes that adaptation occurs automatically through self-learning of a neural network integrated into the system. To train a neural network it is proposed to use hybrid information systems based on the combined use of sensor and computational methods of irrigation control. Such “double” systems can be implemented on test fields and adapted to the area decisions can already be extended to neighboring or similar areas.