摘要
干旱胁迫是限制作物生长的重要因素之一。在全球气候变暖的大环境下,干旱造成大量作物产量损失,使得世界范围内粮食安全受到威胁。为应对这一问题,必须实现生产过程中作物水分含量的实时监测,以指导合理灌溉。同时有必要加快选育耐旱、抗旱的作物品种,以保证干旱胁迫下农业的可持续稳定发展。近年来,借助遥感技术、光谱技术、机器学习技术的集成能力,基于高通量表型技术的作物生长监测研究取得极大进展,为干旱胁迫下,大规模作物水分含量快速、无损、精确研究提供可能。高通量表型技术在作物干旱胁迫中的应用有助于指导精准灌溉,提高作物水分利用效率,并帮助育种学家快速筛选耐旱作物品种,辅助优质作物改良。系统阐述了高通量表型技术的原理以及其在作物干旱胁迫中的应用进展,以期为相关研究提供参考。
Drought stress is a significant factors limiting crop growth.Drought has resulted in significant crop yield loss in the context of global warming,which has threatened global food security.To address this issue,real-time monitoring of crop water content in the production process is required to guide rational irrigation.Simultaneously,it is critical to accelerate the development of drought-tolerant and droughtresistant crop types in order to assure the sustainable and stable development of agriculture under drought stress.Crop growth monitoring based on high-throughput phenotypic technology has advanced significantly in recent years with the integration of remote sensing,spectral,and machine learning technologies.This has made it possible to quickly,accurately,and non-destructively study crop water content on a large scale under drought stress.The use of high-throughput phenotypic technology in crop drought stress is beneficial for guiding precision irrigation,improving crop water use efficiency,and assisting breeders in speedily screening drought-tolerant crop varieties and improving high-quality crops.The idea of high-throughput phenotypic technology and its application advancement in crop drought stress were thoroughly evaluated in this work to provide a reference for future research.
作者
王薇
付虹雨
龚喜红
卢建宁
杨瑞芳
崔国贤
佘玮
WANG Wei;FU Hong-yu;GONG Xi-hong;LU Jian-ning;YANG Rui-fang;CUI Guo-xian;SHE Wei(Ramie Research Institute of Hunan Agricultural University,Changsha 410128,China)
出处
《节水灌溉》
北大核心
2023年第7期110-116,124,共8页
Water Saving Irrigation
基金
财政部和农业农村部国家现代农业产业技术体系(CARS-16-E11)
国家重点研发计划课题(2018YFD0201106)
国家自然科学基金项目(31471543)。
关键词
高通量表型技术
遥感技术
干旱胁迫
含水量
光谱技术
机器学习
high-throughput phenotypic technology
remote sensing technology
drought stress
water content
spectral technology
machine learning