English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

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.

Neural network models in reducing the risks of irrigated agriculture