Neural network models in reducing the risks of irrigated agriculture

Abstract

The article presents the results of studies on the construction of neural network models that will help reduce risks and increase the efficiency of irrigated agriculture. The projected increase in food production on irrigated land is subject to significant risks of climatic and infrastructural nature. Irrigated agriculture, in the global understanding of interrelationships, is a complex dynamic system with nonlinear dependencies. Therefore, traditional approaches based only on physical modeling of environmental and technical processes and relationships often complicate the search for effective solutions. Technological advances that stimulate unprecedented data growth, the rapid extraction of meaningful information from the modern data flow can increase the efficiency of decisions and minimize risks. An approach based on the implementation of neural network models for predicting agro-climatic parameters and intelligent control of irrigation equipment using neurocontrollers is proposed. The models are implemented in the Matlab. The use of these models can significantly reduce risks and increase the efficiency of irrigated agriculture.