Classification of high-throughput phenotyping data for differentiation among nutrient deficiency in common bean.

ABSTRACT: The development of automated, image-based, high-throughput plant phenotyping enabled the simultaneous measurement of many plant traits. Big and complex phenotypic datasets require advanced statistical methods which enable the extraction of the most valuable traits when combined with other measurements, interpretation, and understanding of their (eco)physiological background. Nutrient deficiency in plants causes specific symptoms that can be easily detected by multispectral imaging, 3D scanning, and chlorophyll fluorescence measurements. Screening of numerous image-based phenotypic traits of common bean plants grown in nutrient-deficient solutions was conducted to optimize phenotyping and select the most valuable phenotypic traits related to the specific nutrient deficit. Discriminant analy

SUBMITTER: Lazarevic B 

PROVIDER: S-EPMC9353735 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature