一个集成的多态和人工智能框架,用于园艺中的植物先进表型化
Danuta Cembrowska-Lech1,2, Adrianna Krzemińska2,3, Tymoteusz Miller2,4
1Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland.
Biology
|October 27, 2023
概括
整合多omics数据与人工智能 (AI) 彻底改变了园艺中的植物表型. 这种方法增强了精准园艺,预测育种和可持续的作物管理,以实现未来的农业进步.
科学领域:
- 园艺科学 园艺科学
- 生物信息学是一种生物信息学.
- 植物生物学 植物生物学
背景情况:
- 传统的植物表型化方法与生物复杂性作斗争.
- 多omics数据 (基因组学,转录组学,蛋白质组学,代谢组学) 提供了全面的生物学见解.
- 人工智能 (AI) 和机器学习 (ML) 对于分析大型多领域数据集至关重要.
研究的目的:
- 审查多学科数据和人工智能用于园艺植物表型的整合.
- 展示最先进的解决方案,并应对这个跨学科领域的挑战.
- 探索这种一体化的未来前景和变革潜力.
主要方法:
- 审查关于多学科数据集成和人工智能在植物表型化中的应用的当前文献.
- 讨论多omics和人工智能驱动的表型设计的实验设计考虑.
- 识别技术和非技术方面的挑战,并提出解决方案.
主要成果:
- 人工智能和机器学习有效地解释了复杂的多组数据,用于植物表型.
- 整合使园艺研究的先进解决方案成为可能.
- 确定了实际实施的挑战和潜在解决方案.
结论:
- 多种经济学和人工智能的协同作用有望彻底改变园艺研究和应用.
- 这种整合为精准园艺,预测育种和增强作物管理铺平了道路.
- 植物表型的新时代正在出现,由数据驱动的洞察力驱动.
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