Artificial Intelligence for Plant Genomics and Crop Improvement
dc.Affiliation | October University for modern sciences and Arts (MSA) | |
dc.contributor.author | Hatem, Yasmine | |
dc.contributor.author | Hammad, Gehan | |
dc.contributor.author | Safwat, Gehan | |
dc.date.accessioned | 2022-04-28T07:51:20Z | |
dc.date.available | 2022-04-28T07:51:20Z | |
dc.date.issued | 2022-04 | |
dc.description.abstract | Currently, food scarcity has become a serious problem that the entire planet is facing, as populations are increasing promptly and it is expected to reach 9 billion by 2050, leading to dramatic issues associated with supply as well as accessibility. There are various technologies that are being revolutionized in order to elevate the production of agriculture and food to fit the need and overcome challenges such as climate, water scarcity, diseases and pests. The understanding of plant genomics could lead to the discovery, cloning, and sequencing of genes responsible for tolerance to unfavorable environmental influences, and for the past few decades numerous techniques have surfaced for crop improvement including tissue culture transformation and mutagenesis. More Recently, artificial intelligence and machine learning are being integrated as an anticipating multidisciplinary approach for enhancing and improving the food and agriculture sector and this field is evolving exponentially. This review investigates the correlation of agriculture and food production with artificial intelligence as a promising approach for the determination of the plant genomics in order to improve and enhance issues of food security. | en_US |
dc.identifier.doi | https://doi.org/10.21608/EJBO.2022.83200.1731 | |
dc.identifier.other | https://doi.org/10.21608/EJBO.2022.83200.1731 | |
dc.identifier.uri | https://bit.ly/3ki4FwP | |
dc.language.iso | en_US | en_US |
dc.publisher | Academy of Scientific Rescarch and Technology National Information and Documentation Centre (NIDOC) | en_US |
dc.relation.ispartofseries | Egyptian Journal of Botany;Online ISSN: 2357 - 0350 Print ISSN : 0375- 9237 | |
dc.subject | Agriculture | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Crop improvement | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Plant genomics | en_US |
dc.title | Artificial Intelligence for Plant Genomics and Crop Improvement | en_US |
dc.type | Article | en_US |
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