气候变化领域集成服务门户(CCPortal): High-throughput chlorophyll fluorescence image-based phenotyping for water deficit stress tolerance in wheat

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DOI	10.1007/s40502-024-00783-7
	High-throughput chlorophyll fluorescence image-based phenotyping for water deficit stress tolerance in wheat
	Arya, Sunny; Sahoo, Rabi N.; Sehgal, V. K.; Bandyopadhyay, Kalikinkar; Rejith, R. G.; Chinnusamy, Viswanathan; Kumar, Sudhir; Kumar, Sanjeev; Manjaiah, K. M.
发表日期	2024
ISSN	2662-253X
EISSN	2662-2548
英文摘要	As global populations increase and economies expand, the demand for freshwater is surging, exacerbated by the effects of climate change and shifting lifestyles. It is resulting in widespread water stress and straining food production systems, a challenge anticipated to intensify in the coming decades. One potential solution to mitigate the impact of water scarcity, particularly in water-deficient regions, is the cultivation of water deficit stress-tolerant crop varieties. This study explores the simultaneous assessment of photosynthetic machinery and plant growth responses using chlorophyll fluorescence (ChlF) image based high-throughput phenotyping (HTP) for water deficit stress tolerance on 184 RILs in a controlled environment phenotyping facility. Under stress, recombinant inbred lines (RILs) displayed a diminished variable to maximum fluorescence ratio (Fv/Fm) compared to the control. However, stress-tolerant lines maintained higher Fv/Fm ratio and projected Fv/Fm area, mitigating water stress-induced yield losses. Machine learning using K-Nearest Neighbor, Support Vector Classifier and Random Forest, classified wheat RILs intro stress tolerance classes using sensor derived parameters with high accuracy of 0.56, 0.58 and 0.60 respectively. This study demonstrates the full potential of ChlF image-based phenotyping for enhanced throughput, identifying stress tolerance RILs as well as sensor derived traits, and novel indices. It emphasizes the importance of utilizing innovative data analytics techniques like PCA, clustering and machine learning to alleviate the data analysis bottleneck of HTP, for accelerating the pace of crop improvement for stress tolerance and sustainable food production.
英文关键词	High-throughput phenotyping; Chlorophyll fluorescence; Machine learning; Water deficit stress; Clustering; Classification
语种	英语
WOS研究方向	Plant Sciences
WOS类目	Plant Sciences
WOS记录号	WOS:001209541600001
来源期刊	PLANT PHYSIOLOGY REPORTS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/299364
作者单位	Indian Council of Agricultural Research (ICAR); ICAR - Indian Agricultural Research Institute; Indian Council of Agricultural Research (ICAR); ICAR - Indian Agricultural Research Institute; Indian Council of Agricultural Research (ICAR); ICAR - Indian Agricultural Statistics Research Institute; Indian Council of Agricultural Research (ICAR); ICAR - Indian Agricultural Research Institute; Indian Council of Agricultural Research (ICAR); ICAR - Indian Agricultural Research Institute
推荐引用方式 GB/T 7714	Arya, Sunny,Sahoo, Rabi N.,Sehgal, V. K.,et al. High-throughput chlorophyll fluorescence image-based phenotyping for water deficit stress tolerance in wheat[J],2024.
APA	Arya, Sunny.,Sahoo, Rabi N..,Sehgal, V. K..,Bandyopadhyay, Kalikinkar.,Rejith, R. G..,...&Manjaiah, K. M..(2024).High-throughput chlorophyll fluorescence image-based phenotyping for water deficit stress tolerance in wheat.PLANT PHYSIOLOGY REPORTS.
MLA	Arya, Sunny,et al."High-throughput chlorophyll fluorescence image-based phenotyping for water deficit stress tolerance in wheat".PLANT PHYSIOLOGY REPORTS (2024).