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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 | |
| 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/299363 |
| 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 | . High-throughput chlorophyll fluorescence image-based phenotyping for water deficit stress tolerance in wheat[J],2024. |
| APA | (2024).High-throughput chlorophyll fluorescence image-based phenotyping for water deficit stress tolerance in wheat.PLANT PHYSIOLOGY REPORTS. |
| MLA | "High-throughput chlorophyll fluorescence image-based phenotyping for water deficit stress tolerance in wheat".PLANT PHYSIOLOGY REPORTS (2024). |
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