Please use this identifier to cite or link to this item:
https://cris.library.msu.ac.zw//handle/11408/6401
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | David Mhlanga | en_US |
dc.contributor.author | Farai Mlambo | en_US |
dc.contributor.author | Mufaro Dzingirai | en_US |
dc.contributor.editor | David Mhlanga | en_US |
dc.contributor.editor | Mufaro Dzingirai | en_US |
dc.date.accessioned | 2024-12-11T12:09:46Z | - |
dc.date.available | 2024-12-11T12:09:46Z | - |
dc.date.issued | 2024-08-31 | - |
dc.identifier.uri | https://cris.library.msu.ac.zw//handle/11408/6401 | - |
dc.description.abstract | The global population is expected to exceed nine billion by 2050, necessitating at least a 70% increase in agricultural output to meet the demands of this growth. It is anticipated that only about 10% of this increase will come from newly developed areas, with the majority—90%—arising from enhanced productivity in existing agricultural practices. Addressing this challenge requires leveraging the latest advancements in technology to maximize agricultural efficiency. This research focuses on assessing the role of artificial intelligence (AI) and machine learning (ML) in enhancing food security and resilience, especially in developing economies. The study explains how AI and ML contribute to food security and resilience, offering practical recommendations for emerging markets to harness these technologies. The chapter concludes with an exploration of the impact of various AI and ML-driven technologies on food security. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Palgrave Macmillan, Cham | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Enhanced Agricultural Practices | en_US |
dc.subject | Food Security | en_US |
dc.title | Harnessing Artificial Intelligence and Machine Learning for Enhanced Agricultural Practices: A Pathway to Strengthen Food Security and Resilience | en_US |
dc.type | book part | en_US |
dc.relation.publication | Fostering Long-Term Sustainable Development in Africa: Overcoming Poverty, Inequality, and Unemployment | en_US |
dc.identifier.doi | https://doi.org/10.1007/978-3-031-61321-0_20 | - |
dc.contributor.affiliation | College of Business and Economics, The University of Johannesburg, Johannesburg, South Africa | en_US |
dc.contributor.affiliation | University of the Witwatersrand, Johannesburg, South Africa | en_US |
dc.contributor.affiliation | Department of Business Management, Faculty of Commerce, Midlands State University, Gweru, Zimbabwe | en_US |
dc.contributor.editoraffiliation | College of Business and Economics, University of Johannesburg, Auckland Park, South Africa | en_US |
dc.contributor.editoraffiliation | Department of Business Management, Midlands State University, Gweru, Zimbabwe | en_US |
dc.relation.isbn | 978-3-031-61321-0 | en_US |
dc.description.startpage | 465 | en_US |
dc.description.endpage | 483 | en_US |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.fulltext | With Fulltext | - |
item.openairetype | book part | - |
item.openairecristype | http://purl.org/coar/resource_type/c_3248 | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | Book Chapters |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Harnessing Artificial Intelligence and Machine Learning for Enhanced Agricultural Practices.pdf | Abstract | 57.82 kB | Adobe PDF | View/Open |
Page view(s)
22
checked on Jan 31, 2025
Download(s)
2
checked on Jan 31, 2025
Google ScholarTM
Check
Altmetric
Items in MSUIR are protected by copyright, with all rights reserved, unless otherwise indicated.