Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping

编号 040027402

推送时间 20210118

研究领域 森林培育 

年份 2021 

类型 期刊 

语种 英语

标题 Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping

来源期刊 Plant Molecular Biology

期 第274期

发表时间 20200820

关键词 Ca2+/cation antiporters superfamily (CaCAs);  Phylogenetic analyses;  Selective expansion;  Expressiondivergence;  Ion-response pattern;  Functional divergence; 

摘要 Plant stress phenotyping is essential to select stress-resistant varieties and develop better stress-management strategies. Standardization of visual assessments and deployment of imaging techniques have improved the accuracy and reliability of stress assessment in comparison with unaided visual measurement. The growing capabilities of machine learning (ML) methods in conjunction with image-based phenotyping can extract new insights from curated, annotated, and high-dimensional datasets across varied crops and stresses. We propose an overarching strategy for utilizing ML techniques that methodically enables the application of plant stress phenotyping at multiple scales across different types of stresses, program goals, and environments.

服务人员 孙小满

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