适应气候变化的作物:整合人工智能,多态学和先进的表型化来应对全球农业和社会挑战
Doni Thingujam1, Sandeep Gouli2, Sachin Promodh Cooray3
1Department of Biological Sciences, Clemson University, 132 Long Hall, Clemson, SC 29634, USA.
Plants (Basel, Switzerland)
|September 13, 2025
概括
发展适应气候变化的作物对于全球粮食安全至关重要. 多种经济学,先进的现象学和人工智能加速识别和培育能够承受环境压力的作物.
科学领域:
- 农业科学 农业科学
- 遗传学 是一个遗传学.
- 环境科学 环境科学
背景情况:
- 气候变化加剧了农业压力 (干旱,高温),威胁到粮食安全.
- 开发适应气候变化的作物对于可持续和高效的农业至关重要.
- 目前的农业实践面临极端天气事件带来的重大挑战.
研究的目的:
- 审查多种经济学,先进的现象学和人工智能的作用,以开发适应气候的作物.
- 突出这些技术的整合,以提高农业的可持续性.
- 讨论这些进步的更广泛的社会,经济和教育影响.
主要方法:
- 基因组学,转录组学,蛋白质组学,代谢组学和表观组学 (多组学) 用于识别应激弹性途径.
- 先进的现象学利用无人机和高光谱成像来进行高通量特征监测.
- 人工智能 (AI) 和机器学习 (ML) 用于分析大规模的数据和表型数据.
主要成果:
- 多omics方法识别了植物承受压力的基因机制.
- 先进的表型化加速了对所需作物特征的监测和选择.
- 人工智能/ML工具增强了对压力耐受性的预测,并优化了育种策略.
- 植物微生物组为改善作物弹性和土壤健康提供了一个互补的策略.
结论:
- 整合人工智能,多种经济学和先进的表型是开发适应气候变化的作物的关键.
- 这些创新有望提高全球粮食安全,农民生计和经济稳定.
- 面对气候变化,发展气候适应性农业对于可持续的未来至关重要.
关键词:
无生物压力是无生物压力.气候变化 气候变化 气候变化在表观基因组学上,表观基因组学.基因组学就是基因组学.机器学习是机器学习.代谢生物组的代谢生物组微生物组是一个微生物组.蛋白质组学 蛋白质组学更多相关视频
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