Deep learning, irrigation enhancement, and agricultural economics for ensuring food security in emerging economies
This paper delves into the critical issues of individual health, environmental health, and public health, which are all interconnected in the complex web of food security in emerging countries. Leveraging data from the top 10 countries with the lowest climate index values according to the Numbeo ran...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | Global Transitions |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589791824000094 |
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| author | Aktam U. Burkhanov Elena G. Popkova Diana R. Galoyan Tatul M. Mkrtchyan Bruno S. Sergi |
| author_facet | Aktam U. Burkhanov Elena G. Popkova Diana R. Galoyan Tatul M. Mkrtchyan Bruno S. Sergi |
| author_sort | Aktam U. Burkhanov |
| collection | DOAJ |
| description | This paper delves into the critical issues of individual health, environmental health, and public health, which are all interconnected in the complex web of food security in emerging countries. Leveraging data from the top 10 countries with the lowest climate index values according to the Numbeo ranking, this article introduces a groundbreaking deep learning algorithm. This algorithm has the potential to revolutionize agricultural productivity and food security in the face of climate change, filling the gap in research on deep learning in agriculture. By enabling intelligent management, this algorithm could boost yields in agriculture, rendering it less dependent on climatic factors and ensuring the effectiveness of digital modernization. Furthermore, we explore the promising benefits of restoring ancient irrigation systems to elevate productivity levels. Our study provides definitive insights into deep learning techniques for yield prediction and productivity enhancement, offering a beacon of hope for the future of food security in emerging economies. |
| format | Article |
| id | doaj-art-0eb11d118f904f50abfaa4ae2eb4dd26 |
| institution | Kabale University |
| issn | 2589-7918 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Global Transitions |
| spelling | doaj-art-0eb11d118f904f50abfaa4ae2eb4dd262024-12-06T05:14:12ZengKeAi Communications Co., Ltd.Global Transitions2589-79182024-01-016164172Deep learning, irrigation enhancement, and agricultural economics for ensuring food security in emerging economiesAktam U. Burkhanov0Elena G. Popkova1Diana R. Galoyan2Tatul M. Mkrtchyan3Bruno S. Sergi4International School of Finance and Technology, Tashkent, UzbekistanRUDN University, Moscow, Russia; Corresponding author.Armenian State University of Economics, Yerevan, ArmeniaArmenian State University of Economics, Yerevan, ArmeniaUniversity of Messina, ItalyThis paper delves into the critical issues of individual health, environmental health, and public health, which are all interconnected in the complex web of food security in emerging countries. Leveraging data from the top 10 countries with the lowest climate index values according to the Numbeo ranking, this article introduces a groundbreaking deep learning algorithm. This algorithm has the potential to revolutionize agricultural productivity and food security in the face of climate change, filling the gap in research on deep learning in agriculture. By enabling intelligent management, this algorithm could boost yields in agriculture, rendering it less dependent on climatic factors and ensuring the effectiveness of digital modernization. Furthermore, we explore the promising benefits of restoring ancient irrigation systems to elevate productivity levels. Our study provides definitive insights into deep learning techniques for yield prediction and productivity enhancement, offering a beacon of hope for the future of food security in emerging economies.http://www.sciencedirect.com/science/article/pii/S2589791824000094L95Q15Q51Q54Q55Q56 |
| spellingShingle | Aktam U. Burkhanov Elena G. Popkova Diana R. Galoyan Tatul M. Mkrtchyan Bruno S. Sergi Deep learning, irrigation enhancement, and agricultural economics for ensuring food security in emerging economies Global Transitions L95 Q15 Q51 Q54 Q55 Q56 |
| title | Deep learning, irrigation enhancement, and agricultural economics for ensuring food security in emerging economies |
| title_full | Deep learning, irrigation enhancement, and agricultural economics for ensuring food security in emerging economies |
| title_fullStr | Deep learning, irrigation enhancement, and agricultural economics for ensuring food security in emerging economies |
| title_full_unstemmed | Deep learning, irrigation enhancement, and agricultural economics for ensuring food security in emerging economies |
| title_short | Deep learning, irrigation enhancement, and agricultural economics for ensuring food security in emerging economies |
| title_sort | deep learning irrigation enhancement and agricultural economics for ensuring food security in emerging economies |
| topic | L95 Q15 Q51 Q54 Q55 Q56 |
| url | http://www.sciencedirect.com/science/article/pii/S2589791824000094 |
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