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机器和深度学习:人工智能在植物生物和非生物压力管理中的应用

Caiming Gou¹, Sara Zafar², Zuhair Hasnain³

¹School of Agriculture, Forestry and Food Engineering, Yibin University, 644000 Yibin, Sichuan, China.

Frontiers in bioscience (Landmark edition)

|January 30, 2024

概括

机器学习 (ML) 和深度学习 (DL) 等人工智能 (AI) 工具有助于管理工厂的压力. 这些技术分析高通量数据,以早期检测作物压力并提高弹性.

科学领域:

植物科学和农业学
计算生物学计算生物学
人工智能在农业中的应用

背景情况:

生物和非生物压力严重影响作物健康和全球粮食生产.
植物压力会影响至关重要的生物过程,包括代谢物生物合成,基因表达和基因组变异.
低水平的压力暴露可以诱导有益的特征,如耐压力,一种被称为hormesis的现象.

研究的目的:

审查机器学习 (ML) 和深度学习 (DL) 在管理植物生物和非生物压力的先进应用.
突出高通量技术在现代植物压力研究中的作用.
总结一下人工智能如何帮助早期检测作物中的压力和特征识别.

主要方法:

利用像卫星和无人机 (UAV) 这样的高通量 (HTP) 设备来获取数据.
利用了重要的数据集,例如用于农业表型的TERRA-REF平台.
应用深度学习 (DL) 和机器学习 (ML) 算法用于数据分析和压力预测.

主要成果:

人工智能驱动的HTP工具产生准确,大规模的数据,对于压力管理至关重要.
ML和DL模型允许在大型植物种群中早期检测理想的特征和压力指标.
这些技术有助于预测非生物压力和早期识别生物压力.

结论:

先进的ML和DL应用对于有效的工厂压力管理至关重要.
与人工智能相结合的HTP传感提供了强大的解决方案,以减轻由于环境压力的作物损失.
人工智能在植物科学中的整合加快了育种计划,提高了作物弹性.

关键词:

人工智能的人工智能是人工智能.生物和非生物的压力.深度学习是一种深度学习.机器学习是机器学习.植物表型化植物表型化卫星卫星卫星卫星卫星卫星卫星卫星卫星.这些都是智能手机.无人驾驶飞行器是一种无人驾驶飞行器.

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