E3S Web Conf.
Volume 217, 2020International Scientific and Practical Conference “Environmental Risks and Safety in Mechanical Engineering” (ERSME-2020)
|Number of page(s)||9|
|Section||Natural Resource and Soil Management|
|Published online||14 December 2020|
Neural network models in reducing the risks of irrigated agriculture
Saratov State Agrarian University, Theatralnaia pl., 1, Saratov, 410012, Russia
* Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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