Characterization of leaf surface phenotypes based on light interaction

• Published in: Plant methods 2023-03, Vol.19 (1), p.26-26, Article 26
• Main Authors: Peters, Reisha D, Noble, Scott D
• Format: Article
• Language: English
• Subjects: Adaptation; Analysis; Aspect ratio; Cell size; Classification; Decision making; Discriminant analysis; Environmental stress; Foliar diagnosis; Image classification; Leaves; Light; Mathematical models; Methodology; Methods; Modelling; Parameter estimation; Parameter identification; Phenotype; Phenotypes; Phenotyping; Plant breeding; Plants; Plastics; Polarization; Polarized light; Pubescence; Reflectance; Spectral classification; Visual observation; Wax; Waxes
• ISSN: 1746-4811; 1746-4811
• DOI: 10.1186/s13007-023-01004-2

Leaf surface phenotypes can indicate plant health and relate to a plant's adaptations to environmental stresses. Identifying these phenotypes using non-invasive techniques can assist in high-throughput phenotyping and can improve decision making in plant breeding. Identification of these surface phenotypes can also assist in stress identification. Incorporating surface phenotypes into leaf optical modelling can lead to improved biochemical parameter retrieval and species identification. In this paper, leaf surface phenotypes are characterized for 349 leaf samples based on polarized light reflectance measured at Brewster's Angle, and microscopic observation. Four main leaf surface phenotypes (glossy wax, glaucous wax, high trichome density, and glabrous) were identified for the leaf samples. The microscopic and visual observations of the phenotypes were used as ground truth for comparison with the spectral classification. In addition to surface classification, the microscope images were used to assess cell size, shape, and cell cap aspect ratios; these surface attributes were not found to correlate significantly with spectral measurements obtained in this study. Using a quadratic discriminant analysis function, a series of 10,000 classifications were run with the data randomly split between training and testing datasets, with 150 and 199 samples, respectively. The average correct classification rate was 72.9% with a worst-case classification of 60.3%. Leaf surface phenotypes were successfully correlated with spectral measurements that can be obtained remotely. Remote identification of these surface phenotypes will improve leaf optical modelling and biochemical parameter estimations. Phenotyping of leaf surfaces can inform plant breeding decisions and assist with plant health monitoring.