USING ARTIFICIAL INTELLIGENCE TO EVALUATE THE RISKS ASSOCIATED WITH YIELD SHORTAGE

Avtorlar

  • Muhamediyeva D.T.

    Tashkent institute of irrigation and agricultural mechanization engineers - National Research University

  • Raupova M.

    Chirchik State Pedagogical University image/svg+xml

Gilt sózler: digital economy, agriculture, ambiguous data, robotics, satellites, remote sensing devices, remote sensors, soil condition monitoring

Annotaciya

The article highlights the emergence of the digital economy and related technologies, which provide many perspectives for working with uncertain data in agriculture. Mainly, the possibilities of using advanced technologies that provide were considered a large amount of information in agricultural conditions to the target agricultural workers.

Paydalanılǵan ádebiyatlar

1. Nabokov V.I., Nekrasov K.V., Zueva O.N., Donskova L.A., Agrarian Bulletin of the Urals. J. Ye 12. 2016.

2. Nabokov V.I., Skvortsov Ye.A., Nekrasov K.V. Bulletin of VIESH. J. Ye 4. 2018.

3. Forsyth D.A., Pons J. Computer vision. Modern approach. (YeDP Williams, 2004).

4. Fedorenko V.F., Chernoivanov V.I., Goltyapin V. World trends in the intellectualization of agriculture: a scientific analytical review (YeDPR os informagrotech, 2018).

5. Amatya S., Karkee M., Gongal A., Zhang Q., M.D.Whiting, Biosystems Engineering. J. Ye 146.2015.

6. Dutta R., Smith D., Rawnsley R., Bishop-Hurle, Computers and Electronics in Agriculture. J. Ye 111.2015.

7. Luger D.F. Artificial Intelligence. Strategies and methods for solving complex problems. (YeDP Williams, 2005).

8. Siegel Ye. Calculate the future. Who clicks, buys, lies or dies. (YeDP Alpina Publisher, 2018).

9. Denning P.J., Lewis T.G. Yexponential Laws of Computing Growth. Communications of the ACM. J. Ye 60. 2017.

10. Sotvoldiev D., Muhamedieva D.T., Juraev Z., Journal of Physics: Conference Series. 2020.

11. Muhamedieva D.T., Sayfiev J. Journal of Physics: Conference Series. 2019.