REDUCING THE IMPACT ON HARVESTING THROUGH ARTIFICIAL INTELLIGENCE TECHNOLOGIES

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Published: Oct 25, 2022
Keywords:
model, modeling, agro-industry, optimization, technology.

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Davron Shamsievich Ziyadullaev
Candidate of Technical Sciences, associate professor National Research University "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers"
Daniil Vyacheslavovich Shunkevich
Candidate of Technical Sciences, Associate Professor, Head of the Department of ICT, Belarusian State University of Informatics and Radioelectronics
Yusuf Yakhshiboevich Muhammadiev
2nd year student National Research University "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers"
Laziz Azimzhonovich Orakov
2nd year student National Research University "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers"

Abstract

The article discusses how artificial intelligence technology can reduce the risk involved in harvesting crops. According to experts, most of them, especially in all private property, market, manufacturing, family, education, public and legal institutions, will undergo radical changes.

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How to Cite
Davron Shamsievich Ziyadullaev, Daniil Vyacheslavovich Shunkevich, Yusuf Yakhshiboevich Muhammadiev, & Laziz Azimzhonovich Orakov. (2022). REDUCING THE IMPACT ON HARVESTING THROUGH ARTIFICIAL INTELLIGENCE TECHNOLOGIES. Galaxy International Interdisciplinary Research Journal, 10(10), 560–566. Retrieved from https://giirj.com/index.php/giirj/article/view/2760
Issue
Vol. 10 No. 10 (2022): GIIRJ
Section
Articles
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

Набоков В.И., Некрасов К.В., Зуева О.Н., Донскова Л.А. Отраслевые особенности как фактор формирования и развития логистических систем в АПК // Аграрный вестник Урала.2016. № 12 (154). С. 102–104.

Набоков В.И., Скворцов Е.А., Некрасов К.В. Внедрение робототехники в организациях сельского хозяйства // Вестник ВИЭСХ. 2018. № 4 (33). С. 126–131.

Федоренко В.Ф., Черноиванов В.И., Гольтяпин В.Я., Федоренко И.В. Мировые тенденции интеллектуализации сельского хозяйства: научный аналитический обзор. М.:ФГБНУ «Росинформагротех», 2018. 232 с.

Amatya S., Karkee M., Gongal A., Zhang Q., Whiting M. D. Detection of cherry tree branches with full foliage in planar architecture for automated sweet-cherry harvesting // Biosystems Engineering. 2015. No. 146. Pp. 3–15

Dutta R., Smith D., Rawnsley R., Bishop-Hurle, G., Hills J., Timms G., Henry D. Dynamic cattle behavioural classification using supervised ensemble classifiers // Computers and Electronics in Agriculture. 2015. No. 111. Pp. 18–28.

Люгер Д.Ф. Искусственный интеллект. Стратегии и методы решения сложных проблем. М.: Вильямс, 2005. 864 с.

Сигель Э. Просчитать будущее. Кто кликнет, купит, соврет или умрет. М.: Альпина Паблишер, 2018. 374 с.

Denning P.J., Lewis T.G. Exponential Laws of Computing Growth. Communications of the ACM, 2017, vol. 60, no. 1, pp. 54–65.

Muhamediyeva D.T. Fuzzy cultural algorithm for solving optimization problems // Journal of Physics: Conference Series (2020).

Ziyadullayev D.Sh., Mukhamedieva D.T., Ziyodullaeva G.E., Ibadullaeva Z.J. 2018 Develop the student model. Journal of Advanced Research in Dynamical and Control Systems – JARDCS Vol. 10(14) http://www.jardcs.org/backissues/archives-special.php?year=2018&issue=14 11. Мухамедиева Д.Т., Н.Х. Норалиев, Зиядуллаев Д.Ш. Баҳолаш тизимларида бошқариш жараёнини ифодаловчи моделлар қуриш ва алгоритмлар яратиш. Ўзбекистон Аграр фани Хабарномаси - 4 (78) 2019. http://www.agriculture.uz/publications Б171-176.