| Issue |
E3S Web Conf.
Volume 460, 2023
International Scientific Conference on Biotechnology and Food Technology (BFT-2023)
|
|
|---|---|---|
| Article Number | 09001 | |
| Number of page(s) | 7 | |
| Section | Agroecology and Sustainable Food Systems | |
| DOI | https://doi.org/10.1051/e3sconf/202346009001 | |
| Published online | 11 December 2023 | |
Neural networks as a promising direction in the agricultural industry
1 Don State Technical University, Gagarin Sq., 1, 344010 Rostov-on-Don, Russia
2 Agricultural Research Centre “Donskoy”, Lenin Str., 14, Zernograd, 347740 Rostov Region, Russia
* Corresponding author: rudoy.d@gs.donstu.ru
The agro-industrial complex (AIC) is considered one of the most conservative in terms of innovation. The global agro-industrial complex is developing today according to the concept of electronic agriculture, one of the basic elements of which is information and communication technologies. Big data, robotics, M2M systems, the Internet of Things, artificial intelligence, blockchain and cloud computing are effectively used by the world's leading agricultural producers. Russia is following the same path: large and medium-sized agricultural producers are trying and implementing various modern technologies, and advanced developers are offering new solutions to the market. Among the many different types of neural network schemes, the so-called recurrent neural networks occupy a special place. Their architecture allows efficient processing of data representing a directed sequence: for example, text, speech and time series. The purpose of this study is to identify growth points and problem areas in the field of robotics and informatization of the agro-industrial complex based on a comparison of the experience of various organizations and different countries in the use of artificial intelligence technologies in agriculture.
© The Authors, published by EDP Sciences, 2023
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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